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2
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| Author | SHA1 | Date | |
|---|---|---|---|
 harshgupta-mem0
|
884e056853 | ||
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harshgupta-mem0
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fbf6b8c0db |
@@ -12,7 +12,7 @@
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"name": "mem0",
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"source": "./integrations/mem0-plugin",
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"description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
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"version": "0.2.10"
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"version": "0.2.9"
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}
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]
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}
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@@ -12,7 +12,7 @@
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"name": "mem0",
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"source": "./integrations/mem0-plugin",
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"description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search.",
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"version": "0.2.10"
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"version": "0.2.9"
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}
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]
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}
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@@ -1,4 +0,0 @@
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[submodule "evaluation"]
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path = evaluation
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url = https://github.com/mem0ai/memory-benchmarks
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branch = main
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@@ -12,7 +12,7 @@ This file provides context for AI coding assistants (Claude Code, Cursor, GitHub
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## Repository Structure
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This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs, servers, plugins, and documentation.
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This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs, servers, plugins, documentation, and evaluation tooling.
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### Key Directories
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@@ -29,11 +29,12 @@ This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs,
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| `integrations/vercel-ai-sdk/` | `@mem0/vercel-ai-provider` — Vercel AI SDK memory provider |
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| `server/` | FastAPI REST server for self-hosted Mem0 (Docker: FastAPI + PostgreSQL/pgvector + Neo4j) |
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| `openmemory/` | Self-hosted memory platform — `api/` (FastAPI + Alembic + MCP server) and `ui/` (Next.js 15 + React 19) |
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| `skills/` | Claude Code skill definitions. Reference skills (SDK knowledge, always-on): `mem0/`, `mem0-cli/`, `mem0-vercel-ai-sdk/`. Pipeline skills (run on demand): `mem0-integrate/`, `mem0-test-integration/`, `mem0-oss-to-platform/` |
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| `skills/` | Claude Code skill definitions. Reference skills (SDK knowledge, always-on): `mem0/`, `mem0-cli/`, `mem0-vercel-ai-sdk/`. Pipeline skills (run on demand): `mem0-integrate/`, `mem0-test-integration/` |
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| `docs/` | Documentation site (Mintlify) |
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| `tests/` | Python SDK tests (pytest) |
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| `evaluation/` | Submodule → [`mem0ai/memory-benchmarks`](https://github.com/mem0ai/memory-benchmarks) — benchmarking (LOCOMO, LongMemEval, BEAM) lives in that repo |
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| `examples/` | Sample projects & runnable demos — apps, Chrome extension, multi-agent patterns, and Jupyter notebooks (`notebooks/`) |
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| `evaluation/` | Benchmarking framework — LOCOMO evals, experiment runner, score generation |
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| `examples/` | Sample projects — demo apps, Chrome extension, multi-agent patterns |
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| `cookbooks/` | Jupyter notebooks — customer support chatbot, AutoGen integration |
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| `pr-reviews/` | Pull request review materials |
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| `scripts/` | Repo-wide utility scripts (e.g., `check-llms-txt-coverage.py` for docs/llms.txt sync) |
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@@ -246,19 +247,18 @@ make docs # or: cd docs && mintlify dev
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- **API spec:** `docs/openapi.json`
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- **Structure:** `api-reference/`, `open-source/`, `platform/`, `integrations/`, `cookbooks/`, `core-concepts/`
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### Evaluation / Benchmarking
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Benchmarking lives in the external [`mem0ai/memory-benchmarks`](https://github.com/mem0ai/memory-benchmarks) repo (LOCOMO + LongMemEval + BEAM). The in-repo `evaluation/` path is a **git submodule** pinned to that repo's `main` — populate it with `git submodule update --init evaluation` (or clone mem0 with `--recurse-submodules`), or clone the benchmarks repo standalone:
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### Evaluation (`evaluation/`)
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```bash
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git clone https://github.com/mem0ai/memory-benchmarks.git
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cd memory-benchmarks
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pip install -r requirements.txt
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# Run a benchmark (Mem0 Cloud; use docker compose for OSS)
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python -m benchmarks.locomo.run --project-name my-test --backend cloud --mem0-api-key $MEM0_API_KEY
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python -m benchmarks.longmemeval.run --project-name my-test --backend cloud --mem0-api-key $MEM0_API_KEY --all-questions
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python -m benchmarks.beam.run --project-name my-test --backend cloud --mem0-api-key $MEM0_API_KEY --chat-sizes 100K --conversations 0-9
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cd evaluation
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make run-mem0-add # Run mem0 add experiments
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make run-mem0-search # Run mem0 search experiments
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make run-mem0-plus-add # With graph memory
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make run-mem0-plus-search # With graph memory
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make run-rag # RAG baseline
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make run-full-context # Full context baseline
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make run-langmem # LangMem comparison
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make run-openai # OpenAI comparison
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```
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## Core APIs
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@@ -390,7 +390,7 @@ Model Context Protocol support in multiple places:
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- `integrations/mem0-plugin/` provides integrations for Claude Code, Cursor, and Codex via MCP server connections and lifecycle hooks for automatic memory capture.
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- `skills/` contains structured skill definitions for AI agents, split into two categories:
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- **Reference skills** (always-on SDK knowledge): `mem0` (Python + TS SDKs, framework integrations), `mem0-cli` (terminal workflows), `mem0-vercel-ai-sdk` (Vercel AI provider).
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- **Pipeline skills** (run on demand): `mem0-integrate` wires Mem0 into an existing repo via a TDD pipeline; `mem0-test-integration` verifies what the integrator produced on the same branch (the two are loosely coupled via `.mem0-integration/` artifacts); `mem0-oss-to-platform` migrates an existing project from Mem0 OSS to the hosted Platform SDK (plan, then execute on approval).
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- **Pipeline skills** (run on demand): `mem0-integrate` wires Mem0 into an existing repo via a TDD pipeline; `mem0-test-integration` verifies what the integrator produced on the same branch. The two are loosely coupled via `.mem0-integration/` artifacts.
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### Adding a New Provider
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@@ -1026,8 +1026,7 @@ def get_user_preferences(user_id: str):
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### AutoGen Integration
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```python
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# Mem0Teachability lives in examples/notebooks/helper/ — see examples/notebooks/mem0-autogen.ipynb
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from helper.mem0_teachability import Mem0Teachability
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from cookbooks.helper.mem0_teachability import Mem0Teachability
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from mem0 import Memory
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# Add memory capability to AutoGen agents
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@@ -186,10 +186,9 @@ npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk
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```bash
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npx skills add https://github.com/mem0ai/mem0 --skill mem0-integrate
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npx skills add https://github.com/mem0ai/mem0 --skill mem0-test-integration
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npx skills add https://github.com/mem0ai/mem0 --skill mem0-oss-to-platform
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```
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Use `/mem0-integrate` to wire Mem0 into an existing repo via a test-first pipeline, then `/mem0-test-integration` to verify. Use `/mem0-oss-to-platform` to migrate an existing project from Mem0 OSS to the hosted Platform SDK. See the [skills catalog](./skills/) or [Vibecoding with Mem0](https://docs.mem0.ai/vibecoding) for the full picture.
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Use `/mem0-integrate` to wire Mem0 into an existing repo via a test-first pipeline, then `/mem0-test-integration` to verify. See the [skills catalog](./skills/) or [Vibecoding with Mem0](https://docs.mem0.ai/vibecoding) for the full picture.
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### Basic Usage
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@@ -555,7 +555,7 @@
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"# - Enables creation of AI agents with long-term memory and learning abilities.\n",
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"# - Improves consistency and reduces repetition in user-agent interactions.\n",
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"\n",
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"from helper.mem0_teachability import Mem0Teachability\n",
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"from cookbooks.helper.mem0_teachability import Mem0Teachability\n",
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"\n",
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"teachability = Mem0Teachability(\n",
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" verbosity=2, # for visibility of what's happening\n",
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@@ -4,39 +4,6 @@ description: "Release notes for the OpenClaw plugin and agent harness."
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mode: "wide"
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---
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||||
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<Update label="2026-06-12" description="v1.0.13">
|
||||
|
||||
**Fixes:**
|
||||
- **Custom categories payload:** `customCategories` (a `Record<string, string>` map) is now converted via the new `customCategoryMapToList()` helper into the `Array<Record<string, string>>` shape the Mem0 SDK expects on `add` calls — previously the raw object was passed as `custom_categories` and silently ignored ([#5345](https://github.com/mem0ai/mem0/pull/5345))
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- **Skip runtime setup during metadata registration:** `register()` now detects `registrationMode === "cli-metadata"`, registers only the CLI commands, and returns early — avoiding backend initialization, service/tool registration, and hook installation during OpenClaw's metadata-only registration pass ([#5383](https://github.com/mem0ai/mem0/pull/5383))
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|
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**Security:**
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- Bumped `mem0ai` from `3.0.3` to `3.0.7` (latest Node SDK) — includes the transitive axios CVE remediation shipped in `3.0.6` ([#5460](https://github.com/mem0ai/mem0/pull/5460))
|
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- Added pnpm override `uuid@<11.1.1` → `>=11.1.1` to resolve an open MEDIUM Dependabot alert ([#5489](https://github.com/mem0ai/mem0/pull/5489))
|
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|
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**Improvements:**
|
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- **Repo consolidation:** Plugin moved from repo-root `openclaw/` to `integrations/openclaw/`; `package.json` `repository.directory` updated to match so npm provenance links to the correct subdirectory ([#5491](https://github.com/mem0ai/mem0/pull/5491))
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**Tests:**
|
||||
- Added `customCategoryMapToList` unit tests and a `PlatformProvider` test asserting `custom_categories` is passed to the Mem0 SDK as a list ([#5345](https://github.com/mem0ai/mem0/pull/5345))
|
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- Added a regression test asserting `cli-metadata` registration registers only CLI commands and triggers no runtime side effects ([#5383](https://github.com/mem0ai/mem0/pull/5383))
|
||||
|
||||
</Update>
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||||
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<Update label="2026-06-02" description="v1.0.12">
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||||
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**Docs:**
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||||
- **Agent Mode onboarding:** README now documents an autonomous setup path for AI agents — `mem0 init --agent --json` mints an evaluation Mem0 API key with no email, OTP, or browser and exports it as `MEM0_API_KEY` for `openclaw mem0 init`; a human owner can later run `mem0 init --email <email>` to claim ownership without disrupting the agent ([#5123](https://github.com/mem0ai/mem0/pull/5123))
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**Security:**
|
||||
- Added pnpm overrides to remediate advisories in transitive dependencies: `langsmith@<0.6.0` → `^0.6.0`, `picomatch@<2.3.2` → `^2.3.2`, `vite` → `^8.0.5`, and `@qdrant/js-client-rest` → `^1.18.0` ([#5294](https://github.com/mem0ai/mem0/pull/5294))
|
||||
|
||||
**Dependencies:**
|
||||
- Bumped `mem0ai` from `3.0.2` to `3.0.3` ([#5212](https://github.com/mem0ai/mem0/pull/5212))
|
||||
- Bumped dev dependencies `@vitest/coverage-v8` and `vitest` from `^4.0.18` to `^4.1.7`; added `vite@^8.0.5` and `@qdrant/js-client-rest@^1.18.0` ([#5294](https://github.com/mem0ai/mem0/pull/5294))
|
||||
|
||||
</Update>
|
||||
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||||
<Update label="2026-04-29" description="v1.0.11">
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||||
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||||
**New Features:**
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||||
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@@ -7,22 +7,6 @@ mode: "wide"
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<Tabs>
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<Tab title="Python">
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<Update label="2026-06-13" description="v2.0.6">
|
||||
|
||||
**New Features:**
|
||||
- **Memory:** Add a contextual OSS-to-Platform notices system that surfaces occasional, situation-aware messages (first run, scale/performance thresholds, slow queries, and when temporal/decay features are relevant) pointing to the corresponding Mem0 Platform capabilities; disable via `MEM0_TELEMETRY=false` ([#5494](https://github.com/mem0ai/mem0/pull/5494))
|
||||
|
||||
**Bug Fixes:**
|
||||
- **Memory:** Prevent a crash in `parse_vision_messages` when vision support is disabled ([#5487](https://github.com/mem0ai/mem0/pull/5487))
|
||||
- **Vector Stores:** Expose the `https` option on the Qdrant vector store configuration so TLS endpoints can be targeted explicitly ([#5380](https://github.com/mem0ai/mem0/pull/5380))
|
||||
- **Vector Stores:** Use valid S3 Vectors entity index names, fixing index operations that failed on invalid names ([#5416](https://github.com/mem0ai/mem0/pull/5416))
|
||||
- **Vector Stores:** Fix `search()` crashing with a `TypeError` in the LangChain vector store when a result score is `None` ([#5072](https://github.com/mem0ai/mem0/pull/5072))
|
||||
- **Vector Stores:** Use `is not None` instead of a truthiness check for vector/payload in the PGVector `update()` path, so valid empty/zero values are no longer skipped ([#5488](https://github.com/mem0ai/mem0/pull/5488))
|
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- **Vector Stores:** Index the Valkey `memory` field as `TEXT` rather than `TAG` so full-text search behaves correctly ([#5443](https://github.com/mem0ai/mem0/pull/5443))
|
||||
- **Vector Stores:** Implement `$not` filter support in the ChromaDB vector store ([#5485](https://github.com/mem0ai/mem0/pull/5485))
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026-06-10" description="v2.0.5">
|
||||
|
||||
**New Features:**
|
||||
@@ -976,17 +960,6 @@ See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-
|
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|
||||
<Tab title="TypeScript">
|
||||
|
||||
<Update label="2026-06-13" description="v3.0.8">
|
||||
|
||||
**New Features:**
|
||||
- **Memory:** Add a contextual OSS-to-Platform notices system that surfaces occasional, situation-aware messages (first run, scale/performance thresholds, slow queries, and when temporal/decay features are relevant) pointing to the corresponding Mem0 Platform capabilities; disable via `MEM0_TELEMETRY=false` ([#5494](https://github.com/mem0ai/mem0/pull/5494))
|
||||
|
||||
**Security:**
|
||||
- **Dependencies:** Upgrade `@langchain/community` to `^1.1.18` to remediate CVE-2026-27795 and CVE-2026-26019 ([#5510](https://github.com/mem0ai/mem0/pull/5510))
|
||||
- **Dependencies:** Resolve all open MEDIUM Dependabot alerts via pnpm overrides ([#5489](https://github.com/mem0ai/mem0/pull/5489))
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026-06-10" description="v3.0.7">
|
||||
|
||||
**New Features:**
|
||||
|
||||
@@ -76,7 +76,6 @@ Let's see the available parameters for the `qdrant` config:
|
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| `path` | Path for the qdrant database | `/tmp/qdrant` |
|
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| `url` | Full URL for the qdrant server | `None` |
|
||||
| `api_key` | API key for the qdrant server | `None` |
|
||||
| `https` | Whether to force HTTPS on or off. `None` lets the client decide; set `False` for plain HTTP Qdrant with API key authentication. | `None` |
|
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| `on_disk` | For enabling persistent storage | `False` |
|
||||
</Tab>
|
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<Tab title="TypeScript">
|
||||
@@ -91,4 +90,4 @@ Let's see the available parameters for the `qdrant` config:
|
||||
| `apiKey` | API key for the Qdrant server | `None` |
|
||||
| `onDisk` | For enabling persistent storage | `False` |
|
||||
</Tab>
|
||||
</Tabs>
|
||||
</Tabs>
|
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@@ -6,7 +6,9 @@ versionFrom: "Open Source"
|
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versionTo: "Platform"
|
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---
|
||||
|
||||
## Overview
|
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# Migrate from Open Source to Platform
|
||||
|
||||
Move your Mem0 implementation to managed infrastructure with enterprise features.
|
||||
|
||||
| Scope | Effort | Downtime |
|
||||
| --------------------- | -------------- | ---------------------------- |
|
||||
@@ -28,29 +30,12 @@ versionTo: "Platform"
|
||||
- **Production Grade**: Auto-scaling, high availability, dedicated support
|
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</Info>
|
||||
|
||||
### Plan
|
||||
## Plan
|
||||
|
||||
1. **Sign up**: Create an account on <a href="https://app.mem0.ai?utm_source=oss&utm_medium=migration-oss-to-platform" rel="nofollow">Mem0 Platform</a>.
|
||||
2. **Get API Key**: Navigate to **Settings > API Keys** and generate a new key.
|
||||
3. **Review Usage**: Identify where you instantiate `Memory` and where you call `search` or `get_all`.
|
||||
|
||||
## Migrate with Agent Skill
|
||||
|
||||
Paste this prompt into your coding agent. It uses a migration skill to produce a plan; once you review and approve it, the agent implements the changes.
|
||||
|
||||
```text
|
||||
Migrate my project from Mem0 OSS to the Mem0 Platform SDK using the
|
||||
mem0-oss-to-platform skill in the mem0ai/mem0 repo, at
|
||||
skills/mem0-oss-to-platform/
|
||||
|
||||
Get the skill whichever way is easiest:
|
||||
- install it: npx skills add https://github.com/mem0ai/mem0 --skill mem0-oss-to-platform
|
||||
- if the mem0 repo is cloned locally, read it from skills/mem0-oss-to-platform/
|
||||
- otherwise fetch that folder from github.com/mem0ai/mem0 (SKILL.md + references/)
|
||||
|
||||
Then read SKILL.md and begin the migration.
|
||||
```
|
||||
|
||||
## Migrate
|
||||
|
||||
### 1. Import Memories Into Platform
|
||||
|
||||
@@ -45,12 +45,10 @@ Let your assistant execute an end-to-end workflow in an existing repo. Invoked a
|
||||
```bash
|
||||
npx skills add https://github.com/mem0ai/mem0 --skill mem0-integrate
|
||||
npx skills add https://github.com/mem0ai/mem0 --skill mem0-test-integration
|
||||
npx skills add https://github.com/mem0ai/mem0 --skill mem0-oss-to-platform
|
||||
```
|
||||
|
||||
- `/mem0-integrate` — wire Mem0 into an existing repository using a goal-driven, test-first pipeline. Detects the stack, asks whether to use Platform or OSS, writes failing tests first, and keeps the integration additive and feature-flagged.
|
||||
- `/mem0-test-integration` — verify what `/mem0-integrate` produced. Runs the repo's native test suite and a real end-to-end smoke flow against your API key, then produces a scorecard.
|
||||
- `/mem0-oss-to-platform` — migrate an existing project from Mem0 OSS to the hosted Platform SDK. Audits where Mem0 is used, writes a reviewable migration plan, then executes it on approval.
|
||||
|
||||
See the [skills index](https://github.com/mem0ai/mem0/tree/main/skills) for the full catalog.
|
||||
|
||||
|
||||
-1
Submodule evaluation deleted from 4b61c5d31b
@@ -0,0 +1,31 @@
|
||||
|
||||
# Run the experiments
|
||||
run-mem0-add:
|
||||
python run_experiments.py --technique_type mem0 --method add
|
||||
|
||||
run-mem0-search:
|
||||
python run_experiments.py --technique_type mem0 --method search --output_folder results/ --top_k 30
|
||||
|
||||
run-mem0-plus-add:
|
||||
python run_experiments.py --technique_type mem0 --method add --is_graph
|
||||
|
||||
run-mem0-plus-search:
|
||||
python run_experiments.py --technique_type mem0 --method search --is_graph --output_folder results/ --top_k 30
|
||||
|
||||
run-rag:
|
||||
python run_experiments.py --technique_type rag --chunk_size 500 --num_chunks 1 --output_folder results/
|
||||
|
||||
run-full-context:
|
||||
python run_experiments.py --technique_type rag --chunk_size -1 --num_chunks 1 --output_folder results/
|
||||
|
||||
run-langmem:
|
||||
python run_experiments.py --technique_type langmem --output_folder results/
|
||||
|
||||
run-zep-add:
|
||||
python run_experiments.py --technique_type zep --method add --output_folder results/
|
||||
|
||||
run-zep-search:
|
||||
python run_experiments.py --technique_type zep --method search --output_folder results/
|
||||
|
||||
run-openai:
|
||||
python run_experiments.py --technique_type openai --output_folder results/
|
||||
@@ -0,0 +1,198 @@
|
||||
# Mem0: Building Production‑Ready AI Agents with Scalable Long‑Term Memory
|
||||
|
||||
[](https://arxiv.org/abs/2504.19413)
|
||||
[](https://mem0.ai/research)
|
||||
|
||||
This repository contains the code and dataset for our paper: **Mem0: Building Production‑Ready AI Agents with Scalable Long‑Term Memory**.
|
||||
|
||||
## 📋 Overview
|
||||
|
||||
This project evaluates Mem0 and compares it with different memory and retrieval techniques for AI systems:
|
||||
|
||||
1. **Established LOCOMO Benchmarks**: We evaluate against five established approaches from the literature: LoCoMo, ReadAgent, MemoryBank, MemGPT, and A-Mem.
|
||||
2. **Open-Source Memory Solutions**: We test promising open-source memory architectures including LangMem, which provides flexible memory management capabilities.
|
||||
3. **RAG Systems**: We implement Retrieval-Augmented Generation with various configurations, testing different chunk sizes and retrieval counts to optimize performance.
|
||||
4. **Full-Context Processing**: We examine the effectiveness of passing the entire conversation history within the context window of the LLM as a baseline approach.
|
||||
5. **Proprietary Memory Systems**: We evaluate OpenAI's built-in memory feature available in their ChatGPT interface to compare against commercial solutions.
|
||||
6. **Third-Party Memory Providers**: We incorporate Zep, a specialized memory management platform designed for AI agents, to assess the performance of dedicated memory infrastructure.
|
||||
|
||||
We test these techniques on the LOCOMO dataset, which contains conversational data with various question types to evaluate memory recall and understanding.
|
||||
|
||||
## 🔍 Dataset
|
||||
|
||||
The LOCOMO dataset used in our experiments can be downloaded from our Google Drive repository:
|
||||
|
||||
[Download LOCOMO Dataset](https://drive.google.com/drive/folders/1L-cTjTm0ohMsitsHg4dijSPJtqNflwX-?usp=drive_link)
|
||||
|
||||
The dataset contains conversational data specifically designed to test memory recall and understanding across various question types and complexity levels.
|
||||
|
||||
Place the dataset files in the `dataset/` directory:
|
||||
- `locomo10.json`: Original dataset
|
||||
- `locomo10_rag.json`: Dataset formatted for RAG experiments
|
||||
|
||||
## 📁 Project Structure
|
||||
|
||||
```
|
||||
.
|
||||
├── src/ # Source code for different memory techniques
|
||||
│ ├── mem0/ # Implementation of the Mem0 technique
|
||||
│ ├── openai/ # Implementation of the OpenAI memory
|
||||
│ ├── zep/ # Implementation of the Zep memory
|
||||
│ ├── rag.py # Implementation of the RAG technique
|
||||
│ └── langmem.py # Implementation of the Language-based memory
|
||||
├── metrics/ # Code for evaluation metrics
|
||||
├── results/ # Results of experiments
|
||||
├── dataset/ # Dataset files
|
||||
├── evals.py # Evaluation script
|
||||
├── run_experiments.py # Script to run experiments
|
||||
├── generate_scores.py # Script to generate scores from results
|
||||
└── prompts.py # Prompts used for the models
|
||||
```
|
||||
|
||||
## 🚀 Getting Started
|
||||
|
||||
### Prerequisites
|
||||
|
||||
Create a `.env` file with your API keys and configurations. The following keys are required:
|
||||
|
||||
```
|
||||
# OpenAI API key for GPT models and embeddings
|
||||
OPENAI_API_KEY="your-openai-api-key"
|
||||
|
||||
# Mem0 API keys (for Mem0 and Mem0+ techniques)
|
||||
MEM0_API_KEY="your-mem0-api-key"
|
||||
MEM0_PROJECT_ID="your-mem0-project-id"
|
||||
MEM0_ORGANIZATION_ID="your-mem0-organization-id"
|
||||
|
||||
# Model configuration
|
||||
MODEL="gpt-4o-mini" # or your preferred model
|
||||
EMBEDDING_MODEL="text-embedding-3-small" # or your preferred embedding model
|
||||
ZEP_API_KEY="api-key-from-zep"
|
||||
```
|
||||
|
||||
### Running Experiments
|
||||
|
||||
You can run experiments using the provided Makefile commands:
|
||||
|
||||
#### Memory Techniques
|
||||
|
||||
```bash
|
||||
# Run Mem0 experiments
|
||||
make run-mem0-add # Add memories using Mem0
|
||||
make run-mem0-search # Search memories using Mem0
|
||||
|
||||
# Run Mem0+ experiments (with graph-based search)
|
||||
make run-mem0-plus-add # Add memories using Mem0+
|
||||
make run-mem0-plus-search # Search memories using Mem0+
|
||||
|
||||
# Run RAG experiments
|
||||
make run-rag # Run RAG with chunk size 500
|
||||
make run-full-context # Run RAG with full context
|
||||
|
||||
# Run LangMem experiments
|
||||
make run-langmem # Run LangMem
|
||||
|
||||
# Run Zep experiments
|
||||
make run-zep-add # Add memories using Zep
|
||||
make run-zep-search # Search memories using Zep
|
||||
|
||||
# Run OpenAI experiments
|
||||
make run-openai # Run OpenAI experiments
|
||||
```
|
||||
|
||||
Alternatively, you can run experiments directly with custom parameters:
|
||||
|
||||
```bash
|
||||
python run_experiments.py --technique_type [mem0|rag|langmem] [additional parameters]
|
||||
```
|
||||
|
||||
#### Command-line Parameters:
|
||||
|
||||
| Parameter | Description | Default |
|
||||
|-----------|-------------|---------|
|
||||
| `--technique_type` | Memory technique to use (mem0, rag, langmem) | mem0 |
|
||||
| `--method` | Method to use (add, search) | add |
|
||||
| `--chunk_size` | Chunk size for processing | 1000 |
|
||||
| `--top_k` | Number of top memories to retrieve | 30 |
|
||||
| `--filter_memories` | Whether to filter memories | False |
|
||||
| `--is_graph` | Whether to use graph-based search | False |
|
||||
| `--num_chunks` | Number of chunks to process for RAG | 1 |
|
||||
|
||||
### 📊 Evaluation
|
||||
|
||||
To evaluate results, run:
|
||||
|
||||
```bash
|
||||
python evals.py --input_file [path_to_results] --output_file [output_path]
|
||||
```
|
||||
|
||||
This script:
|
||||
1. Processes each question-answer pair
|
||||
2. Calculates BLEU and F1 scores automatically
|
||||
3. Uses an LLM judge to evaluate answer correctness
|
||||
4. Saves the combined results to the output file
|
||||
|
||||
### 📈 Generating Scores
|
||||
|
||||
Generate final scores with:
|
||||
|
||||
```bash
|
||||
python generate_scores.py
|
||||
```
|
||||
|
||||
This script:
|
||||
1. Loads the evaluation metrics data
|
||||
2. Calculates mean scores for each category (BLEU, F1, LLM)
|
||||
3. Reports the number of questions per category
|
||||
4. Calculates overall mean scores across all categories
|
||||
|
||||
Example output:
|
||||
```
|
||||
Mean Scores Per Category:
|
||||
bleu_score f1_score llm_score count
|
||||
category
|
||||
1 0.xxxx 0.xxxx 0.xxxx xx
|
||||
2 0.xxxx 0.xxxx 0.xxxx xx
|
||||
3 0.xxxx 0.xxxx 0.xxxx xx
|
||||
|
||||
Overall Mean Scores:
|
||||
bleu_score 0.xxxx
|
||||
f1_score 0.xxxx
|
||||
llm_score 0.xxxx
|
||||
```
|
||||
|
||||
## 📏 Evaluation Metrics
|
||||
|
||||
We use several metrics to evaluate the performance of different memory techniques:
|
||||
|
||||
1. **BLEU Score**: Measures the similarity between the model's response and the ground truth
|
||||
2. **F1 Score**: Measures the harmonic mean of precision and recall
|
||||
3. **LLM Score**: A binary score (0 or 1) determined by an LLM judge evaluating the correctness of responses
|
||||
4. **Token Consumption**: Number of tokens required to generate final answer.
|
||||
5. **Latency**: Time required during search and to generate response.
|
||||
|
||||
## 📚 Citation
|
||||
|
||||
If you use this code or dataset in your research, please cite our paper:
|
||||
|
||||
```bibtex
|
||||
@article{mem0,
|
||||
title={Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory},
|
||||
author={Chhikara, Prateek and Khant, Dev and Aryan, Saket and Singh, Taranjeet and Yadav, Deshraj},
|
||||
journal={arXiv preprint arXiv:2504.19413},
|
||||
year={2025}
|
||||
}
|
||||
```
|
||||
|
||||
## 📄 License
|
||||
|
||||
[MIT License](LICENSE)
|
||||
|
||||
## 👥 Contributors
|
||||
|
||||
- [Prateek Chhikara](https://github.com/prateekchhikara)
|
||||
- [Dev Khant](https://github.com/Dev-Khant)
|
||||
- [Saket Aryan](https://github.com/whysosaket)
|
||||
- [Taranjeet Singh](https://github.com/taranjeet)
|
||||
- [Deshraj Yadav](https://github.com/deshraj)
|
||||
|
||||
@@ -0,0 +1,81 @@
|
||||
import argparse
|
||||
import concurrent.futures
|
||||
import json
|
||||
import threading
|
||||
from collections import defaultdict
|
||||
|
||||
from metrics.llm_judge import evaluate_llm_judge
|
||||
from metrics.utils import calculate_bleu_scores, calculate_metrics
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
def process_item(item_data):
|
||||
k, v = item_data
|
||||
local_results = defaultdict(list)
|
||||
|
||||
for item in v:
|
||||
gt_answer = str(item["answer"])
|
||||
pred_answer = str(item["response"])
|
||||
category = str(item["category"])
|
||||
question = str(item["question"])
|
||||
|
||||
# Skip category 5
|
||||
if category == "5":
|
||||
continue
|
||||
|
||||
metrics = calculate_metrics(pred_answer, gt_answer)
|
||||
bleu_scores = calculate_bleu_scores(pred_answer, gt_answer)
|
||||
llm_score = evaluate_llm_judge(question, gt_answer, pred_answer)
|
||||
|
||||
local_results[k].append(
|
||||
{
|
||||
"question": question,
|
||||
"answer": gt_answer,
|
||||
"response": pred_answer,
|
||||
"category": category,
|
||||
"bleu_score": bleu_scores["bleu1"],
|
||||
"f1_score": metrics["f1"],
|
||||
"llm_score": llm_score,
|
||||
}
|
||||
)
|
||||
|
||||
return local_results
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Evaluate RAG results")
|
||||
parser.add_argument(
|
||||
"--input_file", type=str, default="results/rag_results_500_k1.json", help="Path to the input dataset file"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output_file", type=str, default="evaluation_metrics.json", help="Path to save the evaluation results"
|
||||
)
|
||||
parser.add_argument("--max_workers", type=int, default=10, help="Maximum number of worker threads")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
with open(args.input_file, "r") as f:
|
||||
data = json.load(f)
|
||||
|
||||
results = defaultdict(list)
|
||||
results_lock = threading.Lock()
|
||||
|
||||
# Use ThreadPoolExecutor with specified workers
|
||||
with concurrent.futures.ThreadPoolExecutor(max_workers=args.max_workers) as executor:
|
||||
futures = [executor.submit(process_item, item_data) for item_data in data.items()]
|
||||
|
||||
for future in tqdm(concurrent.futures.as_completed(futures), total=len(futures)):
|
||||
local_results = future.result()
|
||||
with results_lock:
|
||||
for k, items in local_results.items():
|
||||
results[k].extend(items)
|
||||
|
||||
# Save results to JSON file
|
||||
with open(args.output_file, "w") as f:
|
||||
json.dump(results, f, indent=4)
|
||||
|
||||
print(f"Results saved to {args.output_file}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,34 @@
|
||||
import json
|
||||
|
||||
import pandas as pd
|
||||
|
||||
# Load the evaluation metrics data
|
||||
with open("evaluation_metrics.json", "r") as f:
|
||||
data = json.load(f)
|
||||
|
||||
# Flatten the data into a list of question items
|
||||
all_items = []
|
||||
for key in data:
|
||||
all_items.extend(data[key])
|
||||
|
||||
# Convert to DataFrame
|
||||
df = pd.DataFrame(all_items)
|
||||
|
||||
# Convert category to numeric type
|
||||
df["category"] = pd.to_numeric(df["category"])
|
||||
|
||||
# Calculate mean scores by category
|
||||
result = df.groupby("category").agg({"bleu_score": "mean", "f1_score": "mean", "llm_score": "mean"}).round(4)
|
||||
|
||||
# Add count of questions per category
|
||||
result["count"] = df.groupby("category").size()
|
||||
|
||||
# Print the results
|
||||
print("Mean Scores Per Category:")
|
||||
print(result)
|
||||
|
||||
# Calculate overall means
|
||||
overall_means = df.agg({"bleu_score": "mean", "f1_score": "mean", "llm_score": "mean"}).round(4)
|
||||
|
||||
print("\nOverall Mean Scores:")
|
||||
print(overall_means)
|
||||
@@ -0,0 +1,130 @@
|
||||
import argparse
|
||||
import json
|
||||
from collections import defaultdict
|
||||
|
||||
import numpy as np
|
||||
from openai import OpenAI
|
||||
|
||||
from mem0.memory.utils import extract_json
|
||||
|
||||
client = OpenAI()
|
||||
|
||||
ACCURACY_PROMPT = """
|
||||
Your task is to label an answer to a question as ’CORRECT’ or ’WRONG’. You will be given the following data:
|
||||
(1) a question (posed by one user to another user),
|
||||
(2) a ’gold’ (ground truth) answer,
|
||||
(3) a generated answer
|
||||
which you will score as CORRECT/WRONG.
|
||||
|
||||
The point of the question is to ask about something one user should know about the other user based on their prior conversations.
|
||||
The gold answer will usually be a concise and short answer that includes the referenced topic, for example:
|
||||
Question: Do you remember what I got the last time I went to Hawaii?
|
||||
Gold answer: A shell necklace
|
||||
The generated answer might be much longer, but you should be generous with your grading - as long as it touches on the same topic as the gold answer, it should be counted as CORRECT.
|
||||
|
||||
For time related questions, the gold answer will be a specific date, month, year, etc. The generated answer might be much longer or use relative time references (like "last Tuesday" or "next month"), but you should be generous with your grading - as long as it refers to the same date or time period as the gold answer, it should be counted as CORRECT. Even if the format differs (e.g., "May 7th" vs "7 May"), consider it CORRECT if it's the same date.
|
||||
|
||||
Now it's time for the real question:
|
||||
Question: {question}
|
||||
Gold answer: {gold_answer}
|
||||
Generated answer: {generated_answer}
|
||||
|
||||
First, provide a short (one sentence) explanation of your reasoning, then finish with CORRECT or WRONG.
|
||||
Do NOT include both CORRECT and WRONG in your response, or it will break the evaluation script.
|
||||
|
||||
Just return the label CORRECT or WRONG in a json format with the key as "label".
|
||||
"""
|
||||
|
||||
|
||||
def evaluate_llm_judge(question, gold_answer, generated_answer):
|
||||
"""Evaluate the generated answer against the gold answer using an LLM judge."""
|
||||
response = client.chat.completions.create(
|
||||
model="gpt-4o-mini",
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": ACCURACY_PROMPT.format(
|
||||
question=question, gold_answer=gold_answer, generated_answer=generated_answer
|
||||
),
|
||||
}
|
||||
],
|
||||
response_format={"type": "json_object"},
|
||||
temperature=0.0,
|
||||
)
|
||||
label = json.loads(extract_json(response.choices[0].message.content))["label"]
|
||||
return 1 if label == "CORRECT" else 0
|
||||
|
||||
|
||||
def main():
|
||||
"""Main function to evaluate RAG results using LLM judge."""
|
||||
parser = argparse.ArgumentParser(description="Evaluate RAG results using LLM judge")
|
||||
parser.add_argument(
|
||||
"--input_file",
|
||||
type=str,
|
||||
default="results/default_run_v4_k30_new_graph.json",
|
||||
help="Path to the input dataset file",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
dataset_path = args.input_file
|
||||
output_path = f"results/llm_judge_{dataset_path.split('/')[-1]}"
|
||||
|
||||
with open(dataset_path, "r") as f:
|
||||
data = json.load(f)
|
||||
|
||||
LLM_JUDGE = defaultdict(list)
|
||||
RESULTS = defaultdict(list)
|
||||
|
||||
index = 0
|
||||
for k, v in data.items():
|
||||
for x in v:
|
||||
question = x["question"]
|
||||
gold_answer = x["answer"]
|
||||
generated_answer = x["response"]
|
||||
category = x["category"]
|
||||
|
||||
# Skip category 5
|
||||
if int(category) == 5:
|
||||
continue
|
||||
|
||||
# Evaluate the answer
|
||||
label = evaluate_llm_judge(question, gold_answer, generated_answer)
|
||||
LLM_JUDGE[category].append(label)
|
||||
|
||||
# Store the results
|
||||
RESULTS[index].append(
|
||||
{
|
||||
"question": question,
|
||||
"gt_answer": gold_answer,
|
||||
"response": generated_answer,
|
||||
"category": category,
|
||||
"llm_label": label,
|
||||
}
|
||||
)
|
||||
|
||||
# Save intermediate results
|
||||
with open(output_path, "w") as f:
|
||||
json.dump(RESULTS, f, indent=4)
|
||||
|
||||
# Print current accuracy for all categories
|
||||
print("All categories accuracy:")
|
||||
for cat, results in LLM_JUDGE.items():
|
||||
if results: # Only print if there are results for this category
|
||||
print(f" Category {cat}: {np.mean(results):.4f} ({sum(results)}/{len(results)})")
|
||||
print("------------------------------------------")
|
||||
index += 1
|
||||
|
||||
# Save final results
|
||||
with open(output_path, "w") as f:
|
||||
json.dump(RESULTS, f, indent=4)
|
||||
|
||||
# Print final summary
|
||||
print("PATH: ", dataset_path)
|
||||
print("------------------------------------------")
|
||||
for k, v in LLM_JUDGE.items():
|
||||
print(k, np.mean(v))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,211 @@
|
||||
"""
|
||||
Borrowed from https://github.com/WujiangXu/AgenticMemory/blob/main/utils.py
|
||||
|
||||
@article{xu2025mem,
|
||||
title={A-mem: Agentic memory for llm agents},
|
||||
author={Xu, Wujiang and Liang, Zujie and Mei, Kai and Gao, Hang and Tan, Juntao
|
||||
and Zhang, Yongfeng},
|
||||
journal={arXiv preprint arXiv:2502.12110},
|
||||
year={2025}
|
||||
}
|
||||
"""
|
||||
|
||||
import statistics
|
||||
from collections import defaultdict
|
||||
from typing import Dict, List, Union
|
||||
|
||||
import nltk
|
||||
from bert_score import score as bert_score
|
||||
from nltk.translate.bleu_score import SmoothingFunction, sentence_bleu
|
||||
from nltk.translate.meteor_score import meteor_score
|
||||
from rouge_score import rouge_scorer
|
||||
from sentence_transformers import SentenceTransformer
|
||||
|
||||
# from load_dataset import load_locomo_dataset, QA, Turn, Session, Conversation
|
||||
from sentence_transformers.util import pytorch_cos_sim
|
||||
|
||||
# Download required NLTK data
|
||||
try:
|
||||
nltk.download("punkt", quiet=True)
|
||||
nltk.download("wordnet", quiet=True)
|
||||
except Exception as e:
|
||||
print(f"Error downloading NLTK data: {e}")
|
||||
|
||||
# Initialize SentenceTransformer model (this will be reused)
|
||||
try:
|
||||
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not load SentenceTransformer model: {e}")
|
||||
sentence_model = None
|
||||
|
||||
|
||||
def simple_tokenize(text):
|
||||
"""Simple tokenization function."""
|
||||
# Convert to string if not already
|
||||
text = str(text)
|
||||
return text.lower().replace(".", " ").replace(",", " ").replace("!", " ").replace("?", " ").split()
|
||||
|
||||
|
||||
def calculate_rouge_scores(prediction: str, reference: str) -> Dict[str, float]:
|
||||
"""Calculate ROUGE scores for prediction against reference."""
|
||||
scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=True)
|
||||
scores = scorer.score(reference, prediction)
|
||||
return {
|
||||
"rouge1_f": scores["rouge1"].fmeasure,
|
||||
"rouge2_f": scores["rouge2"].fmeasure,
|
||||
"rougeL_f": scores["rougeL"].fmeasure,
|
||||
}
|
||||
|
||||
|
||||
def calculate_bleu_scores(prediction: str, reference: str) -> Dict[str, float]:
|
||||
"""Calculate BLEU scores with different n-gram settings."""
|
||||
pred_tokens = nltk.word_tokenize(prediction.lower())
|
||||
ref_tokens = [nltk.word_tokenize(reference.lower())]
|
||||
|
||||
weights_list = [(1, 0, 0, 0), (0.5, 0.5, 0, 0), (0.33, 0.33, 0.33, 0), (0.25, 0.25, 0.25, 0.25)]
|
||||
smooth = SmoothingFunction().method1
|
||||
|
||||
scores = {}
|
||||
for n, weights in enumerate(weights_list, start=1):
|
||||
try:
|
||||
score = sentence_bleu(ref_tokens, pred_tokens, weights=weights, smoothing_function=smooth)
|
||||
except Exception as e:
|
||||
print(f"Error calculating BLEU score: {e}")
|
||||
score = 0.0
|
||||
scores[f"bleu{n}"] = score
|
||||
|
||||
return scores
|
||||
|
||||
|
||||
def calculate_bert_scores(prediction: str, reference: str) -> Dict[str, float]:
|
||||
"""Calculate BERTScore for semantic similarity."""
|
||||
try:
|
||||
P, R, F1 = bert_score([prediction], [reference], lang="en", verbose=False)
|
||||
return {"bert_precision": P.item(), "bert_recall": R.item(), "bert_f1": F1.item()}
|
||||
except Exception as e:
|
||||
print(f"Error calculating BERTScore: {e}")
|
||||
return {"bert_precision": 0.0, "bert_recall": 0.0, "bert_f1": 0.0}
|
||||
|
||||
|
||||
def calculate_meteor_score(prediction: str, reference: str) -> float:
|
||||
"""Calculate METEOR score for the prediction."""
|
||||
try:
|
||||
return meteor_score([reference.split()], prediction.split())
|
||||
except Exception as e:
|
||||
print(f"Error calculating METEOR score: {e}")
|
||||
return 0.0
|
||||
|
||||
|
||||
def calculate_sentence_similarity(prediction: str, reference: str) -> float:
|
||||
"""Calculate sentence embedding similarity using SentenceBERT."""
|
||||
if sentence_model is None:
|
||||
return 0.0
|
||||
try:
|
||||
# Encode sentences
|
||||
embedding1 = sentence_model.encode([prediction], convert_to_tensor=True)
|
||||
embedding2 = sentence_model.encode([reference], convert_to_tensor=True)
|
||||
|
||||
# Calculate cosine similarity
|
||||
similarity = pytorch_cos_sim(embedding1, embedding2).item()
|
||||
return float(similarity)
|
||||
except Exception as e:
|
||||
print(f"Error calculating sentence similarity: {e}")
|
||||
return 0.0
|
||||
|
||||
|
||||
def calculate_metrics(prediction: str, reference: str) -> Dict[str, float]:
|
||||
"""Calculate comprehensive evaluation metrics for a prediction."""
|
||||
# Handle empty or None values
|
||||
if not prediction or not reference:
|
||||
return {
|
||||
"exact_match": 0,
|
||||
"f1": 0.0,
|
||||
"rouge1_f": 0.0,
|
||||
"rouge2_f": 0.0,
|
||||
"rougeL_f": 0.0,
|
||||
"bleu1": 0.0,
|
||||
"bleu2": 0.0,
|
||||
"bleu3": 0.0,
|
||||
"bleu4": 0.0,
|
||||
"bert_f1": 0.0,
|
||||
"meteor": 0.0,
|
||||
"sbert_similarity": 0.0,
|
||||
}
|
||||
|
||||
# Convert to strings if they're not already
|
||||
prediction = str(prediction).strip()
|
||||
reference = str(reference).strip()
|
||||
|
||||
# Calculate exact match
|
||||
exact_match = int(prediction.lower() == reference.lower())
|
||||
|
||||
# Calculate token-based F1 score
|
||||
pred_tokens = set(simple_tokenize(prediction))
|
||||
ref_tokens = set(simple_tokenize(reference))
|
||||
common_tokens = pred_tokens & ref_tokens
|
||||
|
||||
if not pred_tokens or not ref_tokens:
|
||||
f1 = 0.0
|
||||
else:
|
||||
precision = len(common_tokens) / len(pred_tokens)
|
||||
recall = len(common_tokens) / len(ref_tokens)
|
||||
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
|
||||
|
||||
# Calculate all scores
|
||||
bleu_scores = calculate_bleu_scores(prediction, reference)
|
||||
|
||||
# Combine all metrics
|
||||
metrics = {
|
||||
"exact_match": exact_match,
|
||||
"f1": f1,
|
||||
**bleu_scores,
|
||||
}
|
||||
|
||||
return metrics
|
||||
|
||||
|
||||
def aggregate_metrics(
|
||||
all_metrics: List[Dict[str, float]], all_categories: List[int]
|
||||
) -> Dict[str, Dict[str, Union[float, Dict[str, float]]]]:
|
||||
"""Calculate aggregate statistics for all metrics, split by category."""
|
||||
if not all_metrics:
|
||||
return {}
|
||||
|
||||
# Initialize aggregates for overall and per-category metrics
|
||||
aggregates = defaultdict(list)
|
||||
category_aggregates = defaultdict(lambda: defaultdict(list))
|
||||
|
||||
# Collect all values for each metric, both overall and per category
|
||||
for metrics, category in zip(all_metrics, all_categories):
|
||||
for metric_name, value in metrics.items():
|
||||
aggregates[metric_name].append(value)
|
||||
category_aggregates[category][metric_name].append(value)
|
||||
|
||||
# Calculate statistics for overall metrics
|
||||
results = {"overall": {}}
|
||||
|
||||
for metric_name, values in aggregates.items():
|
||||
results["overall"][metric_name] = {
|
||||
"mean": statistics.mean(values),
|
||||
"std": statistics.stdev(values) if len(values) > 1 else 0.0,
|
||||
"median": statistics.median(values),
|
||||
"min": min(values),
|
||||
"max": max(values),
|
||||
"count": len(values),
|
||||
}
|
||||
|
||||
# Calculate statistics for each category
|
||||
for category in sorted(category_aggregates.keys()):
|
||||
results[f"category_{category}"] = {}
|
||||
for metric_name, values in category_aggregates[category].items():
|
||||
if values: # Only calculate if we have values for this category
|
||||
results[f"category_{category}"][metric_name] = {
|
||||
"mean": statistics.mean(values),
|
||||
"std": statistics.stdev(values) if len(values) > 1 else 0.0,
|
||||
"median": statistics.median(values),
|
||||
"min": min(values),
|
||||
"max": max(values),
|
||||
"count": len(values),
|
||||
}
|
||||
|
||||
return results
|
||||
@@ -0,0 +1,147 @@
|
||||
ANSWER_PROMPT_GRAPH = """
|
||||
You are an intelligent memory assistant tasked with retrieving accurate information from
|
||||
conversation memories.
|
||||
|
||||
# CONTEXT:
|
||||
You have access to memories from two speakers in a conversation. These memories contain
|
||||
timestamped information that may be relevant to answering the question. You also have
|
||||
access to knowledge graph relations for each user, showing connections between entities,
|
||||
concepts, and events relevant to that user.
|
||||
|
||||
# INSTRUCTIONS:
|
||||
1. Carefully analyze all provided memories from both speakers
|
||||
2. Pay special attention to the timestamps to determine the answer
|
||||
3. If the question asks about a specific event or fact, look for direct evidence in the
|
||||
memories
|
||||
4. If the memories contain contradictory information, prioritize the most recent memory
|
||||
5. If there is a question about time references (like "last year", "two months ago",
|
||||
etc.), calculate the actual date based on the memory timestamp. For example, if a
|
||||
memory from 4 May 2022 mentions "went to India last year," then the trip occurred
|
||||
in 2021.
|
||||
6. Always convert relative time references to specific dates, months, or years. For
|
||||
example, convert "last year" to "2022" or "two months ago" to "March 2023" based
|
||||
on the memory timestamp. Ignore the reference while answering the question.
|
||||
7. Focus only on the content of the memories from both speakers. Do not confuse
|
||||
character names mentioned in memories with the actual users who created those
|
||||
memories.
|
||||
8. The answer should be less than 5-6 words.
|
||||
9. Use the knowledge graph relations to understand the user's knowledge network and
|
||||
identify important relationships between entities in the user's world.
|
||||
|
||||
# APPROACH (Think step by step):
|
||||
1. First, examine all memories that contain information related to the question
|
||||
2. Examine the timestamps and content of these memories carefully
|
||||
3. Look for explicit mentions of dates, times, locations, or events that answer the
|
||||
question
|
||||
4. If the answer requires calculation (e.g., converting relative time references),
|
||||
show your work
|
||||
5. Analyze the knowledge graph relations to understand the user's knowledge context
|
||||
6. Formulate a precise, concise answer based solely on the evidence in the memories
|
||||
7. Double-check that your answer directly addresses the question asked
|
||||
8. Ensure your final answer is specific and avoids vague time references
|
||||
|
||||
Memories for user {{speaker_1_user_id}}:
|
||||
|
||||
{{speaker_1_memories}}
|
||||
|
||||
Relations for user {{speaker_1_user_id}}:
|
||||
|
||||
{{speaker_1_graph_memories}}
|
||||
|
||||
Memories for user {{speaker_2_user_id}}:
|
||||
|
||||
{{speaker_2_memories}}
|
||||
|
||||
Relations for user {{speaker_2_user_id}}:
|
||||
|
||||
{{speaker_2_graph_memories}}
|
||||
|
||||
Question: {{question}}
|
||||
|
||||
Answer:
|
||||
"""
|
||||
|
||||
|
||||
ANSWER_PROMPT = """
|
||||
You are an intelligent memory assistant tasked with retrieving accurate information from conversation memories.
|
||||
|
||||
# CONTEXT:
|
||||
You have access to memories from two speakers in a conversation. These memories contain
|
||||
timestamped information that may be relevant to answering the question.
|
||||
|
||||
# INSTRUCTIONS:
|
||||
1. Carefully analyze all provided memories from both speakers
|
||||
2. Pay special attention to the timestamps to determine the answer
|
||||
3. If the question asks about a specific event or fact, look for direct evidence in the memories
|
||||
4. If the memories contain contradictory information, prioritize the most recent memory
|
||||
5. If there is a question about time references (like "last year", "two months ago", etc.),
|
||||
calculate the actual date based on the memory timestamp. For example, if a memory from
|
||||
4 May 2022 mentions "went to India last year," then the trip occurred in 2021.
|
||||
6. Always convert relative time references to specific dates, months, or years. For example,
|
||||
convert "last year" to "2022" or "two months ago" to "March 2023" based on the memory
|
||||
timestamp. Ignore the reference while answering the question.
|
||||
7. Focus only on the content of the memories from both speakers. Do not confuse character
|
||||
names mentioned in memories with the actual users who created those memories.
|
||||
8. The answer should be less than 5-6 words.
|
||||
|
||||
# APPROACH (Think step by step):
|
||||
1. First, examine all memories that contain information related to the question
|
||||
2. Examine the timestamps and content of these memories carefully
|
||||
3. Look for explicit mentions of dates, times, locations, or events that answer the question
|
||||
4. If the answer requires calculation (e.g., converting relative time references), show your work
|
||||
5. Formulate a precise, concise answer based solely on the evidence in the memories
|
||||
6. Double-check that your answer directly addresses the question asked
|
||||
7. Ensure your final answer is specific and avoids vague time references
|
||||
|
||||
Memories for user {{speaker_1_user_id}}:
|
||||
|
||||
{{speaker_1_memories}}
|
||||
|
||||
Memories for user {{speaker_2_user_id}}:
|
||||
|
||||
{{speaker_2_memories}}
|
||||
|
||||
Question: {{question}}
|
||||
|
||||
Answer:
|
||||
"""
|
||||
|
||||
|
||||
ANSWER_PROMPT_ZEP = """
|
||||
You are an intelligent memory assistant tasked with retrieving accurate information from conversation memories.
|
||||
|
||||
# CONTEXT:
|
||||
You have access to memories from a conversation. These memories contain
|
||||
timestamped information that may be relevant to answering the question.
|
||||
|
||||
# INSTRUCTIONS:
|
||||
1. Carefully analyze all provided memories
|
||||
2. Pay special attention to the timestamps to determine the answer
|
||||
3. If the question asks about a specific event or fact, look for direct evidence in the memories
|
||||
4. If the memories contain contradictory information, prioritize the most recent memory
|
||||
5. If there is a question about time references (like "last year", "two months ago", etc.),
|
||||
calculate the actual date based on the memory timestamp. For example, if a memory from
|
||||
4 May 2022 mentions "went to India last year," then the trip occurred in 2021.
|
||||
6. Always convert relative time references to specific dates, months, or years. For example,
|
||||
convert "last year" to "2022" or "two months ago" to "March 2023" based on the memory
|
||||
timestamp. Ignore the reference while answering the question.
|
||||
7. Focus only on the content of the memories. Do not confuse character
|
||||
names mentioned in memories with the actual users who created those memories.
|
||||
8. The answer should be less than 5-6 words.
|
||||
|
||||
# APPROACH (Think step by step):
|
||||
1. First, examine all memories that contain information related to the question
|
||||
2. Examine the timestamps and content of these memories carefully
|
||||
3. Look for explicit mentions of dates, times, locations, or events that answer the question
|
||||
4. If the answer requires calculation (e.g., converting relative time references), show your work
|
||||
5. Formulate a precise, concise answer based solely on the evidence in the memories
|
||||
6. Double-check that your answer directly addresses the question asked
|
||||
7. Ensure your final answer is specific and avoids vague time references
|
||||
|
||||
Memories:
|
||||
|
||||
{{memories}}
|
||||
|
||||
Question: {{question}}
|
||||
Answer:
|
||||
"""
|
||||
@@ -0,0 +1,75 @@
|
||||
import argparse
|
||||
import os
|
||||
|
||||
from src.langmem import LangMemManager
|
||||
from src.memzero.add import MemoryADD
|
||||
from src.memzero.search import MemorySearch
|
||||
from src.openai.predict import OpenAIPredict
|
||||
from src.rag import RAGManager
|
||||
from src.utils import METHODS, TECHNIQUES
|
||||
from src.zep.add import ZepAdd
|
||||
from src.zep.search import ZepSearch
|
||||
|
||||
|
||||
class Experiment:
|
||||
def __init__(self, technique_type, chunk_size):
|
||||
self.technique_type = technique_type
|
||||
self.chunk_size = chunk_size
|
||||
|
||||
def run(self):
|
||||
print(f"Running experiment with technique: {self.technique_type}, chunk size: {self.chunk_size}")
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Run memory experiments")
|
||||
parser.add_argument("--technique_type", choices=TECHNIQUES, default="mem0", help="Memory technique to use")
|
||||
parser.add_argument("--method", choices=METHODS, default="add", help="Method to use")
|
||||
parser.add_argument("--chunk_size", type=int, default=1000, help="Chunk size for processing")
|
||||
parser.add_argument("--output_folder", type=str, default="results/", help="Output path for results")
|
||||
parser.add_argument("--top_k", type=int, default=30, help="Number of top memories to retrieve")
|
||||
parser.add_argument("--filter_memories", action="store_true", default=False, help="Whether to filter memories")
|
||||
parser.add_argument("--is_graph", action="store_true", default=False, help="Whether to use graph-based search")
|
||||
parser.add_argument("--num_chunks", type=int, default=1, help="Number of chunks to process")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Add your experiment logic here
|
||||
print(f"Running experiments with technique: {args.technique_type}, chunk size: {args.chunk_size}")
|
||||
|
||||
if args.technique_type == "mem0":
|
||||
if args.method == "add":
|
||||
memory_manager = MemoryADD(data_path="dataset/locomo10.json", is_graph=args.is_graph)
|
||||
memory_manager.process_all_conversations()
|
||||
elif args.method == "search":
|
||||
output_file_path = os.path.join(
|
||||
args.output_folder,
|
||||
f"mem0_results_top_{args.top_k}_filter_{args.filter_memories}_graph_{args.is_graph}.json",
|
||||
)
|
||||
memory_searcher = MemorySearch(output_file_path, args.top_k, args.filter_memories, args.is_graph)
|
||||
memory_searcher.process_data_file("dataset/locomo10.json")
|
||||
elif args.technique_type == "rag":
|
||||
output_file_path = os.path.join(args.output_folder, f"rag_results_{args.chunk_size}_k{args.num_chunks}.json")
|
||||
rag_manager = RAGManager(data_path="dataset/locomo10_rag.json", chunk_size=args.chunk_size, k=args.num_chunks)
|
||||
rag_manager.process_all_conversations(output_file_path)
|
||||
elif args.technique_type == "langmem":
|
||||
output_file_path = os.path.join(args.output_folder, "langmem_results.json")
|
||||
langmem_manager = LangMemManager(dataset_path="dataset/locomo10_rag.json")
|
||||
langmem_manager.process_all_conversations(output_file_path)
|
||||
elif args.technique_type == "zep":
|
||||
if args.method == "add":
|
||||
zep_manager = ZepAdd(data_path="dataset/locomo10.json")
|
||||
zep_manager.process_all_conversations("1")
|
||||
elif args.method == "search":
|
||||
output_file_path = os.path.join(args.output_folder, "zep_search_results.json")
|
||||
zep_manager = ZepSearch()
|
||||
zep_manager.process_data_file("dataset/locomo10.json", "1", output_file_path)
|
||||
elif args.technique_type == "openai":
|
||||
output_file_path = os.path.join(args.output_folder, "openai_results.json")
|
||||
openai_manager = OpenAIPredict()
|
||||
openai_manager.process_data_file("dataset/locomo10.json", output_file_path)
|
||||
else:
|
||||
raise ValueError(f"Invalid technique type: {args.technique_type}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,185 @@
|
||||
import json
|
||||
import multiprocessing as mp
|
||||
import os
|
||||
import time
|
||||
from collections import defaultdict
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from jinja2 import Template
|
||||
from langgraph.checkpoint.memory import MemorySaver
|
||||
from langgraph.prebuilt import create_react_agent
|
||||
from langgraph.store.memory import InMemoryStore
|
||||
from langgraph.utils.config import get_store
|
||||
from langmem import create_manage_memory_tool, create_search_memory_tool
|
||||
from openai import OpenAI
|
||||
from prompts import ANSWER_PROMPT
|
||||
from tqdm import tqdm
|
||||
|
||||
load_dotenv()
|
||||
|
||||
client = OpenAI()
|
||||
|
||||
ANSWER_PROMPT_TEMPLATE = Template(ANSWER_PROMPT)
|
||||
|
||||
|
||||
def get_answer(question, speaker_1_user_id, speaker_1_memories, speaker_2_user_id, speaker_2_memories):
|
||||
prompt = ANSWER_PROMPT_TEMPLATE.render(
|
||||
question=question,
|
||||
speaker_1_user_id=speaker_1_user_id,
|
||||
speaker_1_memories=speaker_1_memories,
|
||||
speaker_2_user_id=speaker_2_user_id,
|
||||
speaker_2_memories=speaker_2_memories,
|
||||
)
|
||||
|
||||
t1 = time.time()
|
||||
response = client.chat.completions.create(
|
||||
model=os.getenv("MODEL"), messages=[{"role": "system", "content": prompt}], temperature=0.0
|
||||
)
|
||||
t2 = time.time()
|
||||
return response.choices[0].message.content, t2 - t1
|
||||
|
||||
|
||||
def prompt(state):
|
||||
"""Prepare the messages for the LLM."""
|
||||
store = get_store()
|
||||
memories = store.search(
|
||||
("memories",),
|
||||
query=state["messages"][-1].content,
|
||||
)
|
||||
system_msg = f"""You are a helpful assistant.
|
||||
|
||||
## Memories
|
||||
<memories>
|
||||
{memories}
|
||||
</memories>
|
||||
"""
|
||||
return [{"role": "system", "content": system_msg}, *state["messages"]]
|
||||
|
||||
|
||||
class LangMem:
|
||||
def __init__(
|
||||
self,
|
||||
):
|
||||
self.store = InMemoryStore(
|
||||
index={
|
||||
"dims": 1536,
|
||||
"embed": f"openai:{os.getenv('EMBEDDING_MODEL')}",
|
||||
}
|
||||
)
|
||||
self.checkpointer = MemorySaver() # Checkpoint graph state
|
||||
|
||||
self.agent = create_react_agent(
|
||||
f"openai:{os.getenv('MODEL')}",
|
||||
prompt=prompt,
|
||||
tools=[
|
||||
create_manage_memory_tool(namespace=("memories",)),
|
||||
create_search_memory_tool(namespace=("memories",)),
|
||||
],
|
||||
store=self.store,
|
||||
checkpointer=self.checkpointer,
|
||||
)
|
||||
|
||||
def add_memory(self, message, config):
|
||||
return self.agent.invoke({"messages": [{"role": "user", "content": message}]}, config=config)
|
||||
|
||||
def search_memory(self, query, config):
|
||||
try:
|
||||
t1 = time.time()
|
||||
response = self.agent.invoke({"messages": [{"role": "user", "content": query}]}, config=config)
|
||||
t2 = time.time()
|
||||
return response["messages"][-1].content, t2 - t1
|
||||
except Exception as e:
|
||||
print(f"Error in search_memory: {e}")
|
||||
return "", t2 - t1
|
||||
|
||||
|
||||
class LangMemManager:
|
||||
def __init__(self, dataset_path):
|
||||
self.dataset_path = dataset_path
|
||||
with open(self.dataset_path, "r") as f:
|
||||
self.data = json.load(f)
|
||||
|
||||
def process_all_conversations(self, output_file_path):
|
||||
OUTPUT = defaultdict(list)
|
||||
|
||||
# Process conversations in parallel with multiple workers
|
||||
def process_conversation(key_value_pair):
|
||||
key, value = key_value_pair
|
||||
result = defaultdict(list)
|
||||
|
||||
chat_history = value["conversation"]
|
||||
questions = value["question"]
|
||||
|
||||
agent1 = LangMem()
|
||||
agent2 = LangMem()
|
||||
config = {"configurable": {"thread_id": f"thread-{key}"}}
|
||||
speakers = set()
|
||||
|
||||
# Identify speakers
|
||||
for conv in chat_history:
|
||||
speakers.add(conv["speaker"])
|
||||
|
||||
if len(speakers) != 2:
|
||||
raise ValueError(f"Expected 2 speakers, got {len(speakers)}")
|
||||
|
||||
speaker1 = list(speakers)[0]
|
||||
speaker2 = list(speakers)[1]
|
||||
|
||||
# Add memories for each message
|
||||
for conv in tqdm(chat_history, desc=f"Processing messages {key}", leave=False):
|
||||
message = f"{conv['timestamp']} | {conv['speaker']}: {conv['text']}"
|
||||
if conv["speaker"] == speaker1:
|
||||
agent1.add_memory(message, config)
|
||||
elif conv["speaker"] == speaker2:
|
||||
agent2.add_memory(message, config)
|
||||
else:
|
||||
raise ValueError(f"Expected speaker1 or speaker2, got {conv['speaker']}")
|
||||
|
||||
# Process questions
|
||||
for q in tqdm(questions, desc=f"Processing questions {key}", leave=False):
|
||||
category = q["category"]
|
||||
|
||||
if int(category) == 5:
|
||||
continue
|
||||
|
||||
answer = q["answer"]
|
||||
question = q["question"]
|
||||
response1, speaker1_memory_time = agent1.search_memory(question, config)
|
||||
response2, speaker2_memory_time = agent2.search_memory(question, config)
|
||||
|
||||
generated_answer, response_time = get_answer(question, speaker1, response1, speaker2, response2)
|
||||
|
||||
result[key].append(
|
||||
{
|
||||
"question": question,
|
||||
"answer": answer,
|
||||
"response1": response1,
|
||||
"response2": response2,
|
||||
"category": category,
|
||||
"speaker1_memory_time": speaker1_memory_time,
|
||||
"speaker2_memory_time": speaker2_memory_time,
|
||||
"response_time": response_time,
|
||||
"response": generated_answer,
|
||||
}
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
# Use multiprocessing to process conversations in parallel
|
||||
with mp.Pool(processes=10) as pool:
|
||||
results = list(
|
||||
tqdm(
|
||||
pool.imap(process_conversation, list(self.data.items())),
|
||||
total=len(self.data),
|
||||
desc="Processing conversations",
|
||||
)
|
||||
)
|
||||
|
||||
# Combine results from all workers
|
||||
for result in results:
|
||||
for key, items in result.items():
|
||||
OUTPUT[key].extend(items)
|
||||
|
||||
# Save final results
|
||||
with open(output_file_path, "w") as f:
|
||||
json.dump(OUTPUT, f, indent=4)
|
||||
@@ -0,0 +1,141 @@
|
||||
import json
|
||||
import os
|
||||
import threading
|
||||
import time
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from tqdm import tqdm
|
||||
|
||||
from mem0 import MemoryClient
|
||||
|
||||
load_dotenv()
|
||||
|
||||
|
||||
# Update custom instructions
|
||||
custom_instructions = """
|
||||
Generate personal memories that follow these guidelines:
|
||||
|
||||
1. Each memory should be self-contained with complete context, including:
|
||||
- The person's name, do not use "user" while creating memories
|
||||
- Personal details (career aspirations, hobbies, life circumstances)
|
||||
- Emotional states and reactions
|
||||
- Ongoing journeys or future plans
|
||||
- Specific dates when events occurred
|
||||
|
||||
2. Include meaningful personal narratives focusing on:
|
||||
- Identity and self-acceptance journeys
|
||||
- Family planning and parenting
|
||||
- Creative outlets and hobbies
|
||||
- Mental health and self-care activities
|
||||
- Career aspirations and education goals
|
||||
- Important life events and milestones
|
||||
|
||||
3. Make each memory rich with specific details rather than general statements
|
||||
- Include timeframes (exact dates when possible)
|
||||
- Name specific activities (e.g., "charity race for mental health" rather than just "exercise")
|
||||
- Include emotional context and personal growth elements
|
||||
|
||||
4. Extract memories only from user messages, not incorporating assistant responses
|
||||
|
||||
5. Format each memory as a paragraph with a clear narrative structure that captures the person's experience, challenges, and aspirations
|
||||
"""
|
||||
|
||||
|
||||
class MemoryADD:
|
||||
def __init__(self, data_path=None, batch_size=2, is_graph=False):
|
||||
self.mem0_client = MemoryClient(
|
||||
api_key=os.getenv("MEM0_API_KEY"),
|
||||
org_id=os.getenv("MEM0_ORGANIZATION_ID"),
|
||||
project_id=os.getenv("MEM0_PROJECT_ID"),
|
||||
)
|
||||
|
||||
self.mem0_client.update_project(custom_instructions=custom_instructions)
|
||||
self.batch_size = batch_size
|
||||
self.data_path = data_path
|
||||
self.data = None
|
||||
self.is_graph = is_graph
|
||||
if data_path:
|
||||
self.load_data()
|
||||
|
||||
def load_data(self):
|
||||
with open(self.data_path, "r") as f:
|
||||
self.data = json.load(f)
|
||||
return self.data
|
||||
|
||||
def add_memory(self, user_id, message, metadata, retries=3):
|
||||
for attempt in range(retries):
|
||||
try:
|
||||
_ = self.mem0_client.add(
|
||||
message, user_id=user_id, version="v2", metadata=metadata, enable_graph=self.is_graph
|
||||
)
|
||||
return
|
||||
except Exception as e:
|
||||
if attempt < retries - 1:
|
||||
time.sleep(1) # Wait before retrying
|
||||
continue
|
||||
else:
|
||||
raise e
|
||||
|
||||
def add_memories_for_speaker(self, speaker, messages, timestamp, desc):
|
||||
for i in tqdm(range(0, len(messages), self.batch_size), desc=desc):
|
||||
batch_messages = messages[i : i + self.batch_size]
|
||||
self.add_memory(speaker, batch_messages, metadata={"timestamp": timestamp})
|
||||
|
||||
def process_conversation(self, item, idx):
|
||||
conversation = item["conversation"]
|
||||
speaker_a = conversation["speaker_a"]
|
||||
speaker_b = conversation["speaker_b"]
|
||||
|
||||
speaker_a_user_id = f"{speaker_a}_{idx}"
|
||||
speaker_b_user_id = f"{speaker_b}_{idx}"
|
||||
|
||||
# delete all memories for the two users
|
||||
self.mem0_client.delete_all(user_id=speaker_a_user_id)
|
||||
self.mem0_client.delete_all(user_id=speaker_b_user_id)
|
||||
|
||||
for key in conversation.keys():
|
||||
if key in ["speaker_a", "speaker_b"] or "date" in key or "timestamp" in key:
|
||||
continue
|
||||
|
||||
date_time_key = key + "_date_time"
|
||||
timestamp = conversation[date_time_key]
|
||||
chats = conversation[key]
|
||||
|
||||
messages = []
|
||||
messages_reverse = []
|
||||
for chat in chats:
|
||||
if chat["speaker"] == speaker_a:
|
||||
messages.append({"role": "user", "content": f"{speaker_a}: {chat['text']}"})
|
||||
messages_reverse.append({"role": "assistant", "content": f"{speaker_a}: {chat['text']}"})
|
||||
elif chat["speaker"] == speaker_b:
|
||||
messages.append({"role": "assistant", "content": f"{speaker_b}: {chat['text']}"})
|
||||
messages_reverse.append({"role": "user", "content": f"{speaker_b}: {chat['text']}"})
|
||||
else:
|
||||
raise ValueError(f"Unknown speaker: {chat['speaker']}")
|
||||
|
||||
# add memories for the two users on different threads
|
||||
thread_a = threading.Thread(
|
||||
target=self.add_memories_for_speaker,
|
||||
args=(speaker_a_user_id, messages, timestamp, "Adding Memories for Speaker A"),
|
||||
)
|
||||
thread_b = threading.Thread(
|
||||
target=self.add_memories_for_speaker,
|
||||
args=(speaker_b_user_id, messages_reverse, timestamp, "Adding Memories for Speaker B"),
|
||||
)
|
||||
|
||||
thread_a.start()
|
||||
thread_b.start()
|
||||
thread_a.join()
|
||||
thread_b.join()
|
||||
|
||||
print("Messages added successfully")
|
||||
|
||||
def process_all_conversations(self, max_workers=10):
|
||||
if not self.data:
|
||||
raise ValueError("No data loaded. Please set data_path and call load_data() first.")
|
||||
with ThreadPoolExecutor(max_workers=max_workers) as executor:
|
||||
futures = [executor.submit(self.process_conversation, item, idx) for idx, item in enumerate(self.data)]
|
||||
|
||||
for future in futures:
|
||||
future.result()
|
||||
@@ -0,0 +1,215 @@
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
from collections import defaultdict
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from jinja2 import Template
|
||||
from openai import OpenAI
|
||||
from prompts import ANSWER_PROMPT, ANSWER_PROMPT_GRAPH
|
||||
from tqdm import tqdm
|
||||
|
||||
from mem0 import MemoryClient
|
||||
|
||||
load_dotenv()
|
||||
|
||||
|
||||
class MemorySearch:
|
||||
def __init__(self, output_path="results.json", top_k=10, filter_memories=False, is_graph=False):
|
||||
self.mem0_client = MemoryClient(
|
||||
api_key=os.getenv("MEM0_API_KEY"),
|
||||
org_id=os.getenv("MEM0_ORGANIZATION_ID"),
|
||||
project_id=os.getenv("MEM0_PROJECT_ID"),
|
||||
)
|
||||
self.top_k = top_k
|
||||
self.openai_client = OpenAI()
|
||||
self.results = defaultdict(list)
|
||||
self.output_path = output_path
|
||||
self.filter_memories = filter_memories
|
||||
self.is_graph = is_graph
|
||||
|
||||
if self.is_graph:
|
||||
self.ANSWER_PROMPT = ANSWER_PROMPT_GRAPH
|
||||
else:
|
||||
self.ANSWER_PROMPT = ANSWER_PROMPT
|
||||
|
||||
def search_memory(self, user_id, query, max_retries=3, retry_delay=1):
|
||||
start_time = time.time()
|
||||
retries = 0
|
||||
while retries < max_retries:
|
||||
try:
|
||||
if self.is_graph:
|
||||
print("Searching with graph")
|
||||
memories = self.mem0_client.search(
|
||||
query,
|
||||
user_id=user_id,
|
||||
top_k=self.top_k,
|
||||
filter_memories=self.filter_memories,
|
||||
enable_graph=True,
|
||||
output_format="v1.1",
|
||||
)
|
||||
else:
|
||||
memories = self.mem0_client.search(
|
||||
query, user_id=user_id, top_k=self.top_k, filter_memories=self.filter_memories
|
||||
)
|
||||
break
|
||||
except Exception as e:
|
||||
print("Retrying...")
|
||||
retries += 1
|
||||
if retries >= max_retries:
|
||||
raise e
|
||||
time.sleep(retry_delay)
|
||||
|
||||
end_time = time.time()
|
||||
if not self.is_graph:
|
||||
semantic_memories = [
|
||||
{
|
||||
"memory": memory["memory"],
|
||||
"timestamp": memory["metadata"]["timestamp"],
|
||||
"score": round(memory["score"], 2),
|
||||
}
|
||||
for memory in memories
|
||||
]
|
||||
graph_memories = None
|
||||
else:
|
||||
semantic_memories = [
|
||||
{
|
||||
"memory": memory["memory"],
|
||||
"timestamp": memory["metadata"]["timestamp"],
|
||||
"score": round(memory["score"], 2),
|
||||
}
|
||||
for memory in memories["results"]
|
||||
]
|
||||
graph_memories = [
|
||||
{"source": relation["source"], "relationship": relation["relationship"], "target": relation["target"]}
|
||||
for relation in memories["relations"]
|
||||
]
|
||||
return semantic_memories, graph_memories, end_time - start_time
|
||||
|
||||
def answer_question(self, speaker_1_user_id, speaker_2_user_id, question, answer, category):
|
||||
speaker_1_memories, speaker_1_graph_memories, speaker_1_memory_time = self.search_memory(
|
||||
speaker_1_user_id, question
|
||||
)
|
||||
speaker_2_memories, speaker_2_graph_memories, speaker_2_memory_time = self.search_memory(
|
||||
speaker_2_user_id, question
|
||||
)
|
||||
|
||||
search_1_memory = [f"{item['timestamp']}: {item['memory']}" for item in speaker_1_memories]
|
||||
search_2_memory = [f"{item['timestamp']}: {item['memory']}" for item in speaker_2_memories]
|
||||
|
||||
template = Template(self.ANSWER_PROMPT)
|
||||
answer_prompt = template.render(
|
||||
speaker_1_user_id=speaker_1_user_id.split("_")[0],
|
||||
speaker_2_user_id=speaker_2_user_id.split("_")[0],
|
||||
speaker_1_memories=json.dumps(search_1_memory, indent=4),
|
||||
speaker_2_memories=json.dumps(search_2_memory, indent=4),
|
||||
speaker_1_graph_memories=json.dumps(speaker_1_graph_memories, indent=4),
|
||||
speaker_2_graph_memories=json.dumps(speaker_2_graph_memories, indent=4),
|
||||
question=question,
|
||||
)
|
||||
|
||||
t1 = time.time()
|
||||
response = self.openai_client.chat.completions.create(
|
||||
model=os.getenv("MODEL"), messages=[{"role": "system", "content": answer_prompt}], temperature=0.0
|
||||
)
|
||||
t2 = time.time()
|
||||
response_time = t2 - t1
|
||||
return (
|
||||
response.choices[0].message.content,
|
||||
speaker_1_memories,
|
||||
speaker_2_memories,
|
||||
speaker_1_memory_time,
|
||||
speaker_2_memory_time,
|
||||
speaker_1_graph_memories,
|
||||
speaker_2_graph_memories,
|
||||
response_time,
|
||||
)
|
||||
|
||||
def process_question(self, val, speaker_a_user_id, speaker_b_user_id):
|
||||
question = val.get("question", "")
|
||||
answer = val.get("answer", "")
|
||||
category = val.get("category", -1)
|
||||
evidence = val.get("evidence", [])
|
||||
adversarial_answer = val.get("adversarial_answer", "")
|
||||
|
||||
(
|
||||
response,
|
||||
speaker_1_memories,
|
||||
speaker_2_memories,
|
||||
speaker_1_memory_time,
|
||||
speaker_2_memory_time,
|
||||
speaker_1_graph_memories,
|
||||
speaker_2_graph_memories,
|
||||
response_time,
|
||||
) = self.answer_question(speaker_a_user_id, speaker_b_user_id, question, answer, category)
|
||||
|
||||
result = {
|
||||
"question": question,
|
||||
"answer": answer,
|
||||
"category": category,
|
||||
"evidence": evidence,
|
||||
"response": response,
|
||||
"adversarial_answer": adversarial_answer,
|
||||
"speaker_1_memories": speaker_1_memories,
|
||||
"speaker_2_memories": speaker_2_memories,
|
||||
"num_speaker_1_memories": len(speaker_1_memories),
|
||||
"num_speaker_2_memories": len(speaker_2_memories),
|
||||
"speaker_1_memory_time": speaker_1_memory_time,
|
||||
"speaker_2_memory_time": speaker_2_memory_time,
|
||||
"speaker_1_graph_memories": speaker_1_graph_memories,
|
||||
"speaker_2_graph_memories": speaker_2_graph_memories,
|
||||
"response_time": response_time,
|
||||
}
|
||||
|
||||
# Save results after each question is processed
|
||||
with open(self.output_path, "w") as f:
|
||||
json.dump(self.results, f, indent=4)
|
||||
|
||||
return result
|
||||
|
||||
def process_data_file(self, file_path):
|
||||
with open(file_path, "r") as f:
|
||||
data = json.load(f)
|
||||
|
||||
for idx, item in tqdm(enumerate(data), total=len(data), desc="Processing conversations"):
|
||||
qa = item["qa"]
|
||||
conversation = item["conversation"]
|
||||
speaker_a = conversation["speaker_a"]
|
||||
speaker_b = conversation["speaker_b"]
|
||||
|
||||
speaker_a_user_id = f"{speaker_a}_{idx}"
|
||||
speaker_b_user_id = f"{speaker_b}_{idx}"
|
||||
|
||||
for question_item in tqdm(
|
||||
qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False
|
||||
):
|
||||
result = self.process_question(question_item, speaker_a_user_id, speaker_b_user_id)
|
||||
self.results[idx].append(result)
|
||||
|
||||
# Save results after each question is processed
|
||||
with open(self.output_path, "w") as f:
|
||||
json.dump(self.results, f, indent=4)
|
||||
|
||||
# Final save at the end
|
||||
with open(self.output_path, "w") as f:
|
||||
json.dump(self.results, f, indent=4)
|
||||
|
||||
def process_questions_parallel(self, qa_list, speaker_a_user_id, speaker_b_user_id, max_workers=1):
|
||||
def process_single_question(val):
|
||||
result = self.process_question(val, speaker_a_user_id, speaker_b_user_id)
|
||||
# Save results after each question is processed
|
||||
with open(self.output_path, "w") as f:
|
||||
json.dump(self.results, f, indent=4)
|
||||
return result
|
||||
|
||||
with ThreadPoolExecutor(max_workers=max_workers) as executor:
|
||||
results = list(
|
||||
tqdm(executor.map(process_single_question, qa_list), total=len(qa_list), desc="Answering Questions")
|
||||
)
|
||||
|
||||
# Final save at the end
|
||||
with open(self.output_path, "w") as f:
|
||||
json.dump(self.results, f, indent=4)
|
||||
|
||||
return results
|
||||
@@ -0,0 +1,131 @@
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
from collections import defaultdict
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from jinja2 import Template
|
||||
from openai import OpenAI
|
||||
from tqdm import tqdm
|
||||
|
||||
load_dotenv()
|
||||
|
||||
|
||||
ANSWER_PROMPT = """
|
||||
You are an intelligent memory assistant tasked with retrieving accurate information from conversation memories.
|
||||
|
||||
# CONTEXT:
|
||||
You have access to memories from a conversation. These memories contain
|
||||
timestamped information that may be relevant to answering the question.
|
||||
|
||||
# INSTRUCTIONS:
|
||||
1. Carefully analyze all provided memories
|
||||
2. Pay special attention to the timestamps to determine the answer
|
||||
3. If the question asks about a specific event or fact, look for direct evidence in the memories
|
||||
4. If the memories contain contradictory information, prioritize the most recent memory
|
||||
5. If there is a question about time references (like "last year", "two months ago", etc.),
|
||||
calculate the actual date based on the memory timestamp. For example, if a memory from
|
||||
4 May 2022 mentions "went to India last year," then the trip occurred in 2021.
|
||||
6. Always convert relative time references to specific dates, months, or years. For example,
|
||||
convert "last year" to "2022" or "two months ago" to "March 2023" based on the memory
|
||||
timestamp. Ignore the reference while answering the question.
|
||||
7. Focus only on the content of the memories. Do not confuse character
|
||||
names mentioned in memories with the actual users who created those memories.
|
||||
8. The answer should be less than 5-6 words.
|
||||
|
||||
# APPROACH (Think step by step):
|
||||
1. First, examine all memories that contain information related to the question
|
||||
2. Examine the timestamps and content of these memories carefully
|
||||
3. Look for explicit mentions of dates, times, locations, or events that answer the question
|
||||
4. If the answer requires calculation (e.g., converting relative time references), show your work
|
||||
5. Formulate a precise, concise answer based solely on the evidence in the memories
|
||||
6. Double-check that your answer directly addresses the question asked
|
||||
7. Ensure your final answer is specific and avoids vague time references
|
||||
|
||||
Memories:
|
||||
|
||||
{{memories}}
|
||||
|
||||
Question: {{question}}
|
||||
Answer:
|
||||
"""
|
||||
|
||||
|
||||
class OpenAIPredict:
|
||||
def __init__(self, model="gpt-4o-mini"):
|
||||
self.model = model
|
||||
self.openai_client = OpenAI()
|
||||
self.results = defaultdict(list)
|
||||
|
||||
def search_memory(self, idx):
|
||||
with open(f"memories/{idx}.txt", "r") as file:
|
||||
memories = file.read()
|
||||
|
||||
return memories, 0
|
||||
|
||||
def process_question(self, val, idx):
|
||||
question = val.get("question", "")
|
||||
answer = val.get("answer", "")
|
||||
category = val.get("category", -1)
|
||||
evidence = val.get("evidence", [])
|
||||
adversarial_answer = val.get("adversarial_answer", "")
|
||||
|
||||
response, search_memory_time, response_time, context = self.answer_question(idx, question)
|
||||
|
||||
result = {
|
||||
"question": question,
|
||||
"answer": answer,
|
||||
"category": category,
|
||||
"evidence": evidence,
|
||||
"response": response,
|
||||
"adversarial_answer": adversarial_answer,
|
||||
"search_memory_time": search_memory_time,
|
||||
"response_time": response_time,
|
||||
"context": context,
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
def answer_question(self, idx, question):
|
||||
memories, search_memory_time = self.search_memory(idx)
|
||||
|
||||
template = Template(ANSWER_PROMPT)
|
||||
answer_prompt = template.render(memories=memories, question=question)
|
||||
|
||||
t1 = time.time()
|
||||
response = self.openai_client.chat.completions.create(
|
||||
model=os.getenv("MODEL"), messages=[{"role": "system", "content": answer_prompt}], temperature=0.0
|
||||
)
|
||||
t2 = time.time()
|
||||
response_time = t2 - t1
|
||||
return response.choices[0].message.content, search_memory_time, response_time, memories
|
||||
|
||||
def process_data_file(self, file_path, output_file_path):
|
||||
with open(file_path, "r") as f:
|
||||
data = json.load(f)
|
||||
|
||||
for idx, item in tqdm(enumerate(data), total=len(data), desc="Processing conversations"):
|
||||
qa = item["qa"]
|
||||
|
||||
for question_item in tqdm(
|
||||
qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False
|
||||
):
|
||||
result = self.process_question(question_item, idx)
|
||||
self.results[idx].append(result)
|
||||
|
||||
# Save results after each question is processed
|
||||
with open(output_file_path, "w") as f:
|
||||
json.dump(self.results, f, indent=4)
|
||||
|
||||
# Final save at the end
|
||||
with open(output_file_path, "w") as f:
|
||||
json.dump(self.results, f, indent=4)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--output_file_path", type=str, required=True)
|
||||
args = parser.parse_args()
|
||||
openai_predict = OpenAIPredict()
|
||||
openai_predict.process_data_file("../../dataset/locomo10.json", args.output_file_path)
|
||||
@@ -0,0 +1,183 @@
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
from collections import defaultdict
|
||||
|
||||
import numpy as np
|
||||
import tiktoken
|
||||
from dotenv import load_dotenv
|
||||
from jinja2 import Template
|
||||
from openai import OpenAI
|
||||
from tqdm import tqdm
|
||||
|
||||
load_dotenv()
|
||||
|
||||
PROMPT = """
|
||||
# Question:
|
||||
{{QUESTION}}
|
||||
|
||||
# Context:
|
||||
{{CONTEXT}}
|
||||
|
||||
# Short answer:
|
||||
"""
|
||||
|
||||
|
||||
class RAGManager:
|
||||
def __init__(self, data_path="dataset/locomo10_rag.json", chunk_size=500, k=1):
|
||||
self.model = os.getenv("MODEL")
|
||||
self.client = OpenAI()
|
||||
self.data_path = data_path
|
||||
self.chunk_size = chunk_size
|
||||
self.k = k
|
||||
|
||||
def generate_response(self, question, context):
|
||||
template = Template(PROMPT)
|
||||
prompt = template.render(CONTEXT=context, QUESTION=question)
|
||||
|
||||
max_retries = 3
|
||||
retries = 0
|
||||
|
||||
while retries <= max_retries:
|
||||
try:
|
||||
t1 = time.time()
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=[
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful assistant that can answer "
|
||||
"questions based on the provided context."
|
||||
"If the question involves timing, use the conversation date for reference."
|
||||
"Provide the shortest possible answer."
|
||||
"Use words directly from the conversation when possible."
|
||||
"Avoid using subjects in your answer.",
|
||||
},
|
||||
{"role": "user", "content": prompt},
|
||||
],
|
||||
temperature=0,
|
||||
)
|
||||
t2 = time.time()
|
||||
return response.choices[0].message.content.strip(), t2 - t1
|
||||
except Exception as e:
|
||||
retries += 1
|
||||
if retries > max_retries:
|
||||
raise e
|
||||
time.sleep(1) # Wait before retrying
|
||||
|
||||
def clean_chat_history(self, chat_history):
|
||||
cleaned_chat_history = ""
|
||||
for c in chat_history:
|
||||
cleaned_chat_history += f"{c['timestamp']} | {c['speaker']}: {c['text']}\n"
|
||||
|
||||
return cleaned_chat_history
|
||||
|
||||
def calculate_embedding(self, document):
|
||||
response = self.client.embeddings.create(model=os.getenv("EMBEDDING_MODEL"), input=document)
|
||||
return response.data[0].embedding
|
||||
|
||||
def calculate_similarity(self, embedding1, embedding2):
|
||||
return np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
|
||||
|
||||
def search(self, query, chunks, embeddings, k=1):
|
||||
"""
|
||||
Search for the top-k most similar chunks to the query.
|
||||
|
||||
Args:
|
||||
query: The query string
|
||||
chunks: List of text chunks
|
||||
embeddings: List of embeddings for each chunk
|
||||
k: Number of top chunks to return (default: 1)
|
||||
|
||||
Returns:
|
||||
combined_chunks: The combined text of the top-k chunks
|
||||
search_time: Time taken for the search
|
||||
"""
|
||||
t1 = time.time()
|
||||
query_embedding = self.calculate_embedding(query)
|
||||
similarities = [self.calculate_similarity(query_embedding, embedding) for embedding in embeddings]
|
||||
|
||||
# Get indices of top-k most similar chunks
|
||||
if k == 1:
|
||||
# Original behavior - just get the most similar chunk
|
||||
top_indices = [np.argmax(similarities)]
|
||||
else:
|
||||
# Get indices of top-k chunks
|
||||
top_indices = np.argsort(similarities)[-k:][::-1]
|
||||
|
||||
# Combine the top-k chunks
|
||||
combined_chunks = "\n<->\n".join([chunks[i] for i in top_indices])
|
||||
|
||||
t2 = time.time()
|
||||
return combined_chunks, t2 - t1
|
||||
|
||||
def create_chunks(self, chat_history, chunk_size=500):
|
||||
"""
|
||||
Create chunks using tiktoken for more accurate token counting
|
||||
"""
|
||||
# Get the encoding for the model
|
||||
encoding = tiktoken.encoding_for_model(os.getenv("EMBEDDING_MODEL"))
|
||||
|
||||
documents = self.clean_chat_history(chat_history)
|
||||
|
||||
if chunk_size == -1:
|
||||
return [documents], []
|
||||
|
||||
chunks = []
|
||||
|
||||
# Encode the document
|
||||
tokens = encoding.encode(documents)
|
||||
|
||||
# Split into chunks based on token count
|
||||
for i in range(0, len(tokens), chunk_size):
|
||||
chunk_tokens = tokens[i : i + chunk_size]
|
||||
chunk = encoding.decode(chunk_tokens)
|
||||
chunks.append(chunk)
|
||||
|
||||
embeddings = []
|
||||
for chunk in chunks:
|
||||
embedding = self.calculate_embedding(chunk)
|
||||
embeddings.append(embedding)
|
||||
|
||||
return chunks, embeddings
|
||||
|
||||
def process_all_conversations(self, output_file_path):
|
||||
with open(self.data_path, "r") as f:
|
||||
data = json.load(f)
|
||||
|
||||
FINAL_RESULTS = defaultdict(list)
|
||||
for key, value in tqdm(data.items(), desc="Processing conversations"):
|
||||
chat_history = value["conversation"]
|
||||
questions = value["question"]
|
||||
|
||||
chunks, embeddings = self.create_chunks(chat_history, self.chunk_size)
|
||||
|
||||
for item in tqdm(questions, desc="Answering questions", leave=False):
|
||||
question = item["question"]
|
||||
answer = item.get("answer", "")
|
||||
category = item["category"]
|
||||
|
||||
if self.chunk_size == -1:
|
||||
context = chunks[0]
|
||||
search_time = 0
|
||||
else:
|
||||
context, search_time = self.search(question, chunks, embeddings, k=self.k)
|
||||
response, response_time = self.generate_response(question, context)
|
||||
|
||||
FINAL_RESULTS[key].append(
|
||||
{
|
||||
"question": question,
|
||||
"answer": answer,
|
||||
"category": category,
|
||||
"context": context,
|
||||
"response": response,
|
||||
"search_time": search_time,
|
||||
"response_time": response_time,
|
||||
}
|
||||
)
|
||||
with open(output_file_path, "w+") as f:
|
||||
json.dump(FINAL_RESULTS, f, indent=4)
|
||||
|
||||
# Save results
|
||||
with open(output_file_path, "w+") as f:
|
||||
json.dump(FINAL_RESULTS, f, indent=4)
|
||||
@@ -0,0 +1,3 @@
|
||||
TECHNIQUES = ["mem0", "rag", "langmem", "zep", "openai"]
|
||||
|
||||
METHODS = ["add", "search"]
|
||||
@@ -0,0 +1,76 @@
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from tqdm import tqdm
|
||||
from zep_cloud import Message
|
||||
from zep_cloud.client import Zep
|
||||
|
||||
load_dotenv()
|
||||
|
||||
|
||||
class ZepAdd:
|
||||
def __init__(self, data_path=None):
|
||||
self.zep_client = Zep(api_key=os.getenv("ZEP_API_KEY"))
|
||||
self.data_path = data_path
|
||||
self.data = None
|
||||
if data_path:
|
||||
self.load_data()
|
||||
|
||||
def load_data(self):
|
||||
with open(self.data_path, "r") as f:
|
||||
self.data = json.load(f)
|
||||
return self.data
|
||||
|
||||
def process_conversation(self, run_id, item, idx):
|
||||
conversation = item["conversation"]
|
||||
|
||||
user_id = f"run_id_{run_id}_experiment_user_{idx}"
|
||||
session_id = f"run_id_{run_id}_experiment_session_{idx}"
|
||||
|
||||
# # delete all memories for the two users
|
||||
# self.zep_client.user.delete(user_id=user_id)
|
||||
# self.zep_client.memory.delete(session_id=session_id)
|
||||
|
||||
self.zep_client.user.add(user_id=user_id)
|
||||
self.zep_client.memory.add_session(
|
||||
user_id=user_id,
|
||||
session_id=session_id,
|
||||
)
|
||||
|
||||
print("Starting to add memories... for user", user_id)
|
||||
for key in tqdm(conversation.keys(), desc=f"Processing user {user_id}"):
|
||||
if key in ["speaker_a", "speaker_b"] or "date" in key:
|
||||
continue
|
||||
|
||||
date_time_key = key + "_date_time"
|
||||
timestamp = conversation[date_time_key]
|
||||
chats = conversation[key]
|
||||
|
||||
for chat in tqdm(chats, desc=f"Adding chats for {key}", leave=False):
|
||||
self.zep_client.memory.add(
|
||||
session_id=session_id,
|
||||
messages=[
|
||||
Message(
|
||||
role=chat["speaker"],
|
||||
role_type="user",
|
||||
content=f"{timestamp}: {chat['text']}",
|
||||
)
|
||||
],
|
||||
)
|
||||
|
||||
def process_all_conversations(self, run_id):
|
||||
if not self.data:
|
||||
raise ValueError("No data loaded. Please set data_path and call load_data() first.")
|
||||
for idx, item in tqdm(enumerate(self.data)):
|
||||
if idx == 0:
|
||||
self.process_conversation(run_id, item, idx)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--run_id", type=str, required=True)
|
||||
args = parser.parse_args()
|
||||
zep_add = ZepAdd(data_path="../../dataset/locomo10.json")
|
||||
zep_add.process_all_conversations(args.run_id)
|
||||
@@ -0,0 +1,140 @@
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
from collections import defaultdict
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from jinja2 import Template
|
||||
from openai import OpenAI
|
||||
from prompts import ANSWER_PROMPT_ZEP
|
||||
from tqdm import tqdm
|
||||
from zep_cloud import EntityEdge, EntityNode
|
||||
from zep_cloud.client import Zep
|
||||
|
||||
load_dotenv()
|
||||
|
||||
TEMPLATE = """
|
||||
FACTS and ENTITIES represent relevant context to the current conversation.
|
||||
|
||||
# These are the most relevant facts and their valid date ranges
|
||||
# format: FACT (Date range: from - to)
|
||||
|
||||
{facts}
|
||||
|
||||
|
||||
# These are the most relevant entities
|
||||
# ENTITY_NAME: entity summary
|
||||
|
||||
{entities}
|
||||
|
||||
"""
|
||||
|
||||
|
||||
class ZepSearch:
|
||||
def __init__(self):
|
||||
self.zep_client = Zep(api_key=os.getenv("ZEP_API_KEY"))
|
||||
self.results = defaultdict(list)
|
||||
self.openai_client = OpenAI()
|
||||
|
||||
def format_edge_date_range(self, edge: EntityEdge) -> str:
|
||||
# return f"{datetime(edge.valid_at).strftime('%Y-%m-%d %H:%M:%S') if edge.valid_at else 'date unknown'} - {(edge.invalid_at.strftime('%Y-%m-%d %H:%M:%S') if edge.invalid_at else 'present')}"
|
||||
return f"{edge.valid_at if edge.valid_at else 'date unknown'} - {(edge.invalid_at if edge.invalid_at else 'present')}"
|
||||
|
||||
def compose_search_context(self, edges: list[EntityEdge], nodes: list[EntityNode]) -> str:
|
||||
facts = [f" - {edge.fact} ({self.format_edge_date_range(edge)})" for edge in edges]
|
||||
entities = [f" - {node.name}: {node.summary}" for node in nodes]
|
||||
return TEMPLATE.format(facts="\n".join(facts), entities="\n".join(entities))
|
||||
|
||||
def search_memory(self, run_id, idx, query, max_retries=3, retry_delay=1):
|
||||
start_time = time.time()
|
||||
retries = 0
|
||||
while retries < max_retries:
|
||||
try:
|
||||
user_id = f"run_id_{run_id}_experiment_user_{idx}"
|
||||
edges_results = (
|
||||
self.zep_client.graph.search(
|
||||
user_id=user_id, reranker="cross_encoder", query=query, scope="edges", limit=20
|
||||
)
|
||||
).edges
|
||||
node_results = (
|
||||
self.zep_client.graph.search(user_id=user_id, reranker="rrf", query=query, scope="nodes", limit=20)
|
||||
).nodes
|
||||
context = self.compose_search_context(edges_results, node_results)
|
||||
break
|
||||
except Exception as e:
|
||||
print("Retrying...")
|
||||
retries += 1
|
||||
if retries >= max_retries:
|
||||
raise e
|
||||
time.sleep(retry_delay)
|
||||
|
||||
end_time = time.time()
|
||||
|
||||
return context, end_time - start_time
|
||||
|
||||
def process_question(self, run_id, val, idx):
|
||||
question = val.get("question", "")
|
||||
answer = val.get("answer", "")
|
||||
category = val.get("category", -1)
|
||||
evidence = val.get("evidence", [])
|
||||
adversarial_answer = val.get("adversarial_answer", "")
|
||||
|
||||
response, search_memory_time, response_time, context = self.answer_question(run_id, idx, question)
|
||||
|
||||
result = {
|
||||
"question": question,
|
||||
"answer": answer,
|
||||
"category": category,
|
||||
"evidence": evidence,
|
||||
"response": response,
|
||||
"adversarial_answer": adversarial_answer,
|
||||
"search_memory_time": search_memory_time,
|
||||
"response_time": response_time,
|
||||
"context": context,
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
def answer_question(self, run_id, idx, question):
|
||||
context, search_memory_time = self.search_memory(run_id, idx, question)
|
||||
|
||||
template = Template(ANSWER_PROMPT_ZEP)
|
||||
answer_prompt = template.render(memories=context, question=question)
|
||||
|
||||
t1 = time.time()
|
||||
response = self.openai_client.chat.completions.create(
|
||||
model=os.getenv("MODEL"), messages=[{"role": "system", "content": answer_prompt}], temperature=0.0
|
||||
)
|
||||
t2 = time.time()
|
||||
response_time = t2 - t1
|
||||
return response.choices[0].message.content, search_memory_time, response_time, context
|
||||
|
||||
def process_data_file(self, file_path, run_id, output_file_path):
|
||||
with open(file_path, "r") as f:
|
||||
data = json.load(f)
|
||||
|
||||
for idx, item in tqdm(enumerate(data), total=len(data), desc="Processing conversations"):
|
||||
qa = item["qa"]
|
||||
|
||||
for question_item in tqdm(
|
||||
qa, total=len(qa), desc=f"Processing questions for conversation {idx}", leave=False
|
||||
):
|
||||
result = self.process_question(run_id, question_item, idx)
|
||||
self.results[idx].append(result)
|
||||
|
||||
# Save results after each question is processed
|
||||
with open(output_file_path, "w") as f:
|
||||
json.dump(self.results, f, indent=4)
|
||||
|
||||
# Final save at the end
|
||||
with open(output_file_path, "w") as f:
|
||||
json.dump(self.results, f, indent=4)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--run_id", type=str, required=True)
|
||||
args = parser.parse_args()
|
||||
zep_search = ZepSearch()
|
||||
zep_search.process_data_file("../../dataset/locomo10.json", args.run_id, "results/zep_search_results.json")
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "mem0",
|
||||
"version": "0.2.10",
|
||||
"version": "0.2.9",
|
||||
"description": "Persistent memory for Claude Code. Remembers decisions, patterns, and preferences across sessions.",
|
||||
"author": {
|
||||
"name": "Mem0",
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "mem0",
|
||||
"version": "0.2.10",
|
||||
"version": "0.2.9",
|
||||
"description": "Persistent memory for Codex. Remembers decisions, patterns, and preferences across sessions.",
|
||||
"author": {
|
||||
"name": "Mem0",
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "mem0",
|
||||
"version": "0.2.10",
|
||||
"version": "0.2.9",
|
||||
"description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search using the Mem0 Platform MCP server.",
|
||||
"author": {
|
||||
"name": "Mem0",
|
||||
|
||||
@@ -2,14 +2,13 @@
|
||||
|
||||
All notable changes to the `@mem0/opencode-plugin` will be documented in this file.
|
||||
|
||||
## 0.1.3 — File-context injection, session summaries & activity timeline, anonymous telemetry
|
||||
## 0.1.3 — File-context injection, session summaries & activity timeline
|
||||
|
||||
### Added
|
||||
|
||||
- **File-context injection (`tool.execute.before` / Read):** Before the agent reads a file, the plugin searches mem0 for memories referencing that file path and injects prior work as system context. Gates on file size (>= 1,500 bytes). Gives the agent "I've worked on this file before" awareness automatically.
|
||||
- **Stop hook session summary (`experimental.session.compacting`):** Enhanced session compaction to store a structured `session_summary` memory with `infer=True`, letting the mem0 backend AI extract key facts (request, decisions, learnings, next steps). Previously only stored a raw stats string.
|
||||
- **SessionStart activity timeline:** The initial memory loading now formats recent memories with type icons (⚖️ decision, 🔴 bug_fix, 🔵 task_learning, etc.) and relative age indicators (2h ago, 1d ago) instead of bare text. Provides a visual "Recent Activity" timeline on first message.
|
||||
- **PostHog telemetry (`telemetry.ts`):** Anonymous, fire-and-forget usage events. Opt out with `MEM0_TELEMETRY=false`. Only fires when an API key is present; never sends memory content, prompts, or the API key — only an anonymized `sha256(apiKey)[:32]` identity plus event type, platform, and plugin version. Emits the same schema as the Mem0 editor plugin (`plugin.*` events, `source: "plugin"`, `platform: "opencode"`) so OpenCode appears as a `platform` in the shared plugin dashboard. Events: `plugin.session_start` (with memory count) and `plugin.tool_use` (`add` / `search` / `update` / `delete`).
|
||||
|
||||
### Changed
|
||||
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
"name": "@mem0/opencode-plugin",
|
||||
"dependencies": {
|
||||
"@opencode-ai/plugin": "^1.0.162",
|
||||
"mem0ai": "^3.0.7",
|
||||
"mem0ai": "^3.0.5",
|
||||
},
|
||||
"devDependencies": {
|
||||
"bun-types": ">=1.3.14",
|
||||
@@ -462,7 +462,7 @@
|
||||
|
||||
"md5": ["md5@2.3.0", "", { "dependencies": { "charenc": "0.0.2", "crypt": "0.0.2", "is-buffer": "~1.1.6" } }, "sha512-T1GITYmFaKuO91vxyoQMFETst+O71VUPEU3ze5GNzDm0OWdP8v1ziTaAEPUr/3kLsY3Sftgz242A1SetQiDL7g=="],
|
||||
|
||||
"mem0ai": ["mem0ai@3.0.7", "", { "dependencies": { "axios": "^1.16.0", "openai": "^4.93.0", "uuid": "9.0.1", "zod": "^3.24.1" }, "peerDependencies": { "@anthropic-ai/sdk": "^0.40.1", "@azure/identity": "^4.0.0", "@azure/search-documents": "^12.0.0", "@cloudflare/workers-types": "^4.20250504.0", "@google/genai": "^1.40.0", "@langchain/core": "^1.1.47", "@mistralai/mistralai": "^1.5.2", "@qdrant/js-client-rest": "^1.18.0", "@supabase/supabase-js": "^2.49.1", "@types/jest": "29.5.14", "@types/pg": "8.11.0", "better-sqlite3": "^12.6.2", "cloudflare": "^4.2.0", "compromise": "^14.0.0", "groq-sdk": "0.3.0", "natural": "^8.0.1", "ollama": "^0.5.14", "pg": "8.11.3", "redis": "^4.6.13" } }, "sha512-CUHzX7DyeKTHcI3aDsSqY9LXTD7GcFxf988796TuOa4yJgGuF2Xd2NROcBhVFRo3r9y8fVmbo3c5TF9jv1KlKw=="],
|
||||
"mem0ai": ["mem0ai@3.0.5", "", { "dependencies": { "axios": "^1.15.2", "openai": "^4.93.0", "uuid": "9.0.1", "zod": "^3.24.1" }, "peerDependencies": { "@anthropic-ai/sdk": "^0.40.1", "@azure/identity": "^4.0.0", "@azure/search-documents": "^12.0.0", "@cloudflare/workers-types": "^4.20250504.0", "@google/genai": "^1.2.0", "@langchain/core": "^1.1.47", "@mistralai/mistralai": "^1.5.2", "@qdrant/js-client-rest": "1.13.0", "@supabase/supabase-js": "^2.49.1", "@types/jest": "29.5.14", "@types/pg": "8.11.0", "better-sqlite3": "^12.6.2", "cloudflare": "^4.2.0", "compromise": "^14.0.0", "groq-sdk": "0.3.0", "natural": "^8.0.1", "ollama": "^0.5.14", "pg": "8.11.3", "redis": "^4.6.13" } }, "sha512-W/R59d5fMpUGHhPEnyoo36GSz5NFJbAs+vS4BxoIvE+t19mIJfoz/2FJSKIw80mT8AkeNYpDfzc/DYRu2IzYIw=="],
|
||||
|
||||
"memjs": ["memjs@1.3.2", "", {}, "sha512-qUEg2g8vxPe+zPn09KidjIStHPtoBO8Cttm8bgJFWWabbsjQ9Av9Ky+6UcvKx6ue0LLb/LEhtcyQpRyKfzeXcg=="],
|
||||
|
||||
|
||||
@@ -9,7 +9,6 @@ import { existsSync, readdirSync, cpSync, mkdirSync, readFileSync, writeFileSync
|
||||
import { homedir } from "os";
|
||||
import { join } from "path";
|
||||
import { createHash } from "crypto";
|
||||
import { captureEvent } from "./telemetry";
|
||||
|
||||
async function getUserId(): Promise<string> {
|
||||
if (process.env.MEM0_USER_ID) return process.env.MEM0_USER_ID;
|
||||
@@ -369,8 +368,6 @@ const Mem0Plugin: Plugin = async (ctx) => {
|
||||
});
|
||||
} catch {}
|
||||
}
|
||||
|
||||
captureEvent("session_start", { memory_count: memoryCount }, apiKey);
|
||||
}
|
||||
|
||||
if (NUDGE_RE.test(safeText)) {
|
||||
@@ -605,17 +602,6 @@ const Mem0Plugin: Plugin = async (ctx) => {
|
||||
if (MEM0_MCP_RE.test(toolName)) {
|
||||
if (toolName.includes("add_memory")) stats.adds++;
|
||||
if (toolName.includes("search")) stats.searches++;
|
||||
|
||||
const tool = toolName.includes("add_memory")
|
||||
? "add_memory"
|
||||
: toolName.includes("search")
|
||||
? "search_memories"
|
||||
: toolName.includes("delete")
|
||||
? "delete_memory"
|
||||
: toolName.includes("update")
|
||||
? "update_memory"
|
||||
: "other";
|
||||
captureEvent("tool_use", { tool }, apiKey);
|
||||
}
|
||||
|
||||
if (toolName === "bash" && toolOutput.length >= 50) {
|
||||
|
||||
@@ -59,7 +59,7 @@
|
||||
},
|
||||
"dependencies": {
|
||||
"@opencode-ai/plugin": "^1.0.162",
|
||||
"mem0ai": "^3.0.7"
|
||||
"mem0ai": "^3.0.5"
|
||||
},
|
||||
"devDependencies": {
|
||||
"bun-types": ">=1.3.14",
|
||||
|
||||
@@ -1,51 +0,0 @@
|
||||
import { afterEach, describe, expect, test } from "bun:test";
|
||||
import { buildEvent, captureEvent, isTelemetryEnabled } from "./telemetry";
|
||||
|
||||
const KEY = "m0-testkey123";
|
||||
|
||||
afterEach(() => {
|
||||
delete process.env.MEM0_TELEMETRY;
|
||||
});
|
||||
|
||||
describe("opencode telemetry", () => {
|
||||
test("buildEvent uses the shared plugin.* schema with platform=opencode", () => {
|
||||
const payload = buildEvent("session_start", { memory_count: 5 }, KEY);
|
||||
expect(payload).not.toBeNull();
|
||||
const props = payload!.properties as Record<string, unknown>;
|
||||
expect(payload!.event).toBe("plugin.session_start");
|
||||
expect(props.source).toBe("plugin");
|
||||
expect(props.platform).toBe("opencode");
|
||||
expect(props.memory_count).toBe(5);
|
||||
expect(props.$process_person_profile).toBe(false);
|
||||
expect(typeof props.plugin_version).toBe("string");
|
||||
});
|
||||
|
||||
test("distinct_id is sha256(apiKey)[:32] — matches the editor plugin", async () => {
|
||||
const { createHash } = await import("node:crypto");
|
||||
const expected = createHash("sha256").update(KEY).digest("hex").slice(0, 32);
|
||||
expect(buildEvent("session_start", {}, KEY)!.distinct_id).toBe(expected);
|
||||
});
|
||||
|
||||
test("system properties win over caller-supplied ones", () => {
|
||||
const props = buildEvent("x", { platform: "HACK", source: "HACK" }, KEY)!
|
||||
.properties as Record<string, unknown>;
|
||||
expect(props.platform).toBe("opencode");
|
||||
expect(props.source).toBe("plugin");
|
||||
});
|
||||
|
||||
test("returns null without an API key (no anonymous events)", () => {
|
||||
expect(buildEvent("session_start", {}, undefined)).toBeNull();
|
||||
});
|
||||
|
||||
test("opt-out via MEM0_TELEMETRY disables events", () => {
|
||||
process.env.MEM0_TELEMETRY = "false";
|
||||
expect(isTelemetryEnabled()).toBe(false);
|
||||
expect(buildEvent("session_start", {}, KEY)).toBeNull();
|
||||
});
|
||||
|
||||
test("captureEvent never throws (and sends nothing when opted out)", () => {
|
||||
process.env.MEM0_TELEMETRY = "false";
|
||||
expect(() => captureEvent("session_start", {}, KEY)).not.toThrow();
|
||||
expect(() => captureEvent("session_start", {}, undefined)).not.toThrow();
|
||||
});
|
||||
});
|
||||
@@ -1,104 +0,0 @@
|
||||
/**
|
||||
* Plugin telemetry for the Mem0 OpenCode plugin — anonymous usage tracking
|
||||
* via PostHog.
|
||||
*
|
||||
* Emits the SAME event schema as the Mem0 editor plugin's telemetry.py
|
||||
* (event names prefixed `plugin.`, `source: "plugin"`, `platform: "opencode"`,
|
||||
* `distinct_id = sha256(apiKey)[:32]`) so OpenCode shows up as just another
|
||||
* `platform` value in the shared plugin dashboard instead of a separate
|
||||
* event namespace.
|
||||
*
|
||||
* Fire-and-forget: never throws, never blocks, failures are swallowed. Only
|
||||
* fires when an API key is present (same as the editor plugin — anonymous
|
||||
* installs without a key emit nothing). Disable with MEM0_TELEMETRY=false.
|
||||
*
|
||||
* Never sends: memory content, API keys, raw user/project IDs. Only sends:
|
||||
* event type, platform, plugin version, anonymized hash of the API key.
|
||||
*/
|
||||
|
||||
import { createHash } from "node:crypto";
|
||||
import { readFileSync } from "node:fs";
|
||||
|
||||
const POSTHOG_API_KEY = "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX";
|
||||
const POSTHOG_HOST = "https://us.i.posthog.com/i/v0/e/";
|
||||
const REQUEST_TIMEOUT_MS = 2_000;
|
||||
|
||||
function _loadPluginVersion(): string {
|
||||
// Source context: telemetry.ts sits next to package.json (./).
|
||||
// Bundled context: dist/index.js sits one level below it (../).
|
||||
for (const rel of ["./package.json", "../package.json"]) {
|
||||
try {
|
||||
const pkg = JSON.parse(readFileSync(new URL(rel, import.meta.url), "utf-8"));
|
||||
if (pkg?.name === "@mem0/opencode-plugin" && pkg.version) return pkg.version;
|
||||
} catch {
|
||||
/* try next candidate */
|
||||
}
|
||||
}
|
||||
return "unknown";
|
||||
}
|
||||
|
||||
const PLUGIN_VERSION = _loadPluginVersion();
|
||||
|
||||
export function isTelemetryEnabled(): boolean {
|
||||
const val = process.env.MEM0_TELEMETRY;
|
||||
if (val === undefined) return true;
|
||||
const s = val.toLowerCase();
|
||||
return s !== "false" && s !== "0" && s !== "no" && s !== "off";
|
||||
}
|
||||
|
||||
function distinctId(apiKey: string): string {
|
||||
// Matches telemetry.py `_distinct_id()` so the same user is one person in
|
||||
// PostHog whether they use OpenCode or any other Mem0 editor plugin.
|
||||
return createHash("sha256").update(apiKey).digest("hex").slice(0, 32);
|
||||
}
|
||||
|
||||
/**
|
||||
* Build the PostHog event payload, or null when telemetry is disabled or no
|
||||
* API key is available. Pure (aside from env/version reads) and exported for
|
||||
* testing. System-controlled properties are applied last so a caller cannot
|
||||
* override `source`/`platform`/etc.
|
||||
*/
|
||||
export function buildEvent(
|
||||
eventType: string,
|
||||
properties: Record<string, unknown>,
|
||||
apiKey: string | undefined,
|
||||
): Record<string, unknown> | null {
|
||||
if (!isTelemetryEnabled() || !apiKey) return null;
|
||||
return {
|
||||
api_key: POSTHOG_API_KEY,
|
||||
distinct_id: distinctId(apiKey),
|
||||
event: `plugin.${eventType}`,
|
||||
properties: {
|
||||
...properties,
|
||||
source: "plugin",
|
||||
platform: "opencode",
|
||||
plugin_version: PLUGIN_VERSION,
|
||||
os: process.platform,
|
||||
sample_rate: 1.0,
|
||||
$process_person_profile: false,
|
||||
$lib: "posthog-node",
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
/** Send a usage event, fire-and-forget. Never throws, never blocks. */
|
||||
export function captureEvent(
|
||||
eventType: string,
|
||||
properties: Record<string, unknown>,
|
||||
apiKey: string | undefined,
|
||||
): void {
|
||||
const payload = buildEvent(eventType, properties, apiKey);
|
||||
if (!payload) return;
|
||||
try {
|
||||
void fetch(POSTHOG_HOST, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify(payload),
|
||||
signal: AbortSignal.timeout(REQUEST_TIMEOUT_MS),
|
||||
}).catch(() => {
|
||||
/* fire-and-forget */
|
||||
});
|
||||
} catch {
|
||||
/* never throw */
|
||||
}
|
||||
}
|
||||
@@ -16,5 +16,5 @@
|
||||
"emitDeclarationOnly": true
|
||||
},
|
||||
"include": ["**/*"],
|
||||
"exclude": ["node_modules", "dist", "**/*.test.ts"]
|
||||
"exclude": ["node_modules", "dist"]
|
||||
}
|
||||
|
||||
@@ -2,21 +2,6 @@
|
||||
|
||||
All notable changes to the Mem0 plugin will be documented in this file.
|
||||
|
||||
## 0.2.10 — Accurate per-editor telemetry attribution
|
||||
|
||||
### Fixed
|
||||
|
||||
- **Antigravity counted as Claude Code:** `detect_platform()` (`scripts/telemetry.py`) now checks `ANTIGRAVITY_PLUGIN_ROOT` before the `CLAUDE_PLUGIN_ROOT` branch. Antigravity sets both env vars for compatibility, so every Antigravity session was previously attributed to `claude-code`. Telemetry now reports `platform: "antigravity"`.
|
||||
- **Codex fell back to the generic `plugin` bucket:** Codex installs standalone hooks with absolute paths via `install_codex_hooks.py`, so `PLUGIN_ROOT` is never set at runtime and platform auto-detection failed. Each command in `hooks/codex-hooks.json` now pins `MEM0_PLATFORM=codex` inline (Codex runs hook commands through a shell). Telemetry now reports `platform: "codex"`.
|
||||
- **Cursor attribution depended on the host env:** Cursor's `*_cursor.sh` wrappers delegate to the shared hook scripts, whose platform detection relied on Cursor exporting `CURSOR_PLUGIN_ROOT` to the subprocess. All five Cursor wrappers now `export MEM0_PLATFORM=cursor` before delegating.
|
||||
- **`plugin_version` was identical for every editor:** telemetry read `.claude-plugin/plugin.json` for all bash-hook editors, so Antigravity reported `0.2.10` instead of its real `0.1.2`. `_load_plugin_version()` now reads the manifest matching the detected platform, so each editor reports its own version.
|
||||
|
||||
### Added
|
||||
|
||||
- **`MEM0_PLATFORM` override in `detect_platform()`:** An explicit platform marker that wins over env-var auto-detection, letting each editor label its telemetry reliably. New tests in `tests/test_telemetry.py` cover the override, Antigravity attribution, and the Cursor/Codex platform-pinning contracts.
|
||||
|
||||
> Attribution fixes apply to telemetry emitted after users upgrade to this version; PostHog does not backfill past events.
|
||||
|
||||
## 0.2.9 — File-context injection, session summaries & activity timeline
|
||||
|
||||
### Added
|
||||
|
||||
@@ -6,7 +6,7 @@
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "MEM0_PLATFORM=codex ${PLUGIN_ROOT}/scripts/block_memory_write.sh",
|
||||
"command": "${PLUGIN_ROOT}/scripts/block_memory_write.sh",
|
||||
"timeout": 3
|
||||
}
|
||||
]
|
||||
@@ -16,7 +16,7 @@
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "MEM0_PLATFORM=codex ${PLUGIN_ROOT}/scripts/enforce_metadata_defaults.sh",
|
||||
"command": "${PLUGIN_ROOT}/scripts/enforce_metadata_defaults.sh",
|
||||
"timeout": 3
|
||||
}
|
||||
]
|
||||
@@ -26,7 +26,7 @@
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "MEM0_PLATFORM=codex ${PLUGIN_ROOT}/scripts/on_file_read.sh",
|
||||
"command": "${PLUGIN_ROOT}/scripts/on_file_read.sh",
|
||||
"timeout": 5
|
||||
}
|
||||
]
|
||||
@@ -38,7 +38,7 @@
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "MEM0_PLATFORM=codex ${PLUGIN_ROOT}/scripts/on_session_start.sh",
|
||||
"command": "${PLUGIN_ROOT}/scripts/on_session_start.sh",
|
||||
"statusMessage": "Loading mem0 context..."
|
||||
}
|
||||
]
|
||||
@@ -49,7 +49,7 @@
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "MEM0_PLATFORM=codex ${PLUGIN_ROOT}/scripts/on_user_prompt.sh",
|
||||
"command": "${PLUGIN_ROOT}/scripts/on_user_prompt.sh",
|
||||
"statusMessage": "Checking memory relevance...",
|
||||
"timeout": 12
|
||||
}
|
||||
@@ -62,7 +62,7 @@
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "MEM0_PLATFORM=codex ${PLUGIN_ROOT}/scripts/on_post_tool_use.sh",
|
||||
"command": "${PLUGIN_ROOT}/scripts/on_post_tool_use.sh",
|
||||
"timeout": 3
|
||||
}
|
||||
]
|
||||
@@ -72,7 +72,7 @@
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "MEM0_PLATFORM=codex ${PLUGIN_ROOT}/scripts/on_bash_output.sh",
|
||||
"command": "${PLUGIN_ROOT}/scripts/on_bash_output.sh",
|
||||
"timeout": 12
|
||||
}
|
||||
]
|
||||
@@ -83,7 +83,7 @@
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "MEM0_PLATFORM=codex ${PLUGIN_ROOT}/scripts/on_stop.sh",
|
||||
"command": "${PLUGIN_ROOT}/scripts/on_stop.sh",
|
||||
"timeout": 30
|
||||
}
|
||||
]
|
||||
@@ -94,7 +94,7 @@
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "MEM0_PLATFORM=codex ${PLUGIN_ROOT}/scripts/on_pre_compact.sh",
|
||||
"command": "${PLUGIN_ROOT}/scripts/on_pre_compact.sh",
|
||||
"statusMessage": "Preparing pre-compaction summary..."
|
||||
}
|
||||
]
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
{
|
||||
"id": "mem0",
|
||||
"name": "mem0",
|
||||
"version": "0.1.2",
|
||||
"version": "0.1.1",
|
||||
"description": "Persistent semantic memory for Antigravity agents. Cross-session, user-level recall via the Mem0 Platform MCP server. 16 slash commands, lifecycle hooks for auto-capture and metadata enforcement.",
|
||||
"author": { "name": "Mem0", "email": "support@mem0.ai" },
|
||||
"publisher": "mem0ai",
|
||||
|
||||
@@ -8,9 +8,6 @@ set -uo pipefail
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
|
||||
|
||||
# Pin platform so the shared script's telemetry is attributed to cursor.
|
||||
export MEM0_PLATFORM=cursor
|
||||
|
||||
# Run the shared tracker (output is ignored)
|
||||
"$SCRIPT_DIR/on_post_tool_use.sh" 2>/dev/null || true
|
||||
|
||||
|
||||
@@ -8,9 +8,6 @@ set -uo pipefail
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
|
||||
|
||||
# Pin platform so the shared script's telemetry is attributed to cursor.
|
||||
export MEM0_PLATFORM=cursor
|
||||
|
||||
TEXT=$("$SCRIPT_DIR/on_pre_compact.sh" 2>/dev/null || echo "")
|
||||
|
||||
if [ -z "$TEXT" ]; then
|
||||
|
||||
@@ -8,9 +8,6 @@ set -uo pipefail
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
|
||||
|
||||
# Pin platform so the shared script's telemetry is attributed to cursor.
|
||||
export MEM0_PLATFORM=cursor
|
||||
|
||||
TEXT=$("$SCRIPT_DIR/on_session_start.sh" 2>/dev/null || echo "")
|
||||
|
||||
if [ -z "$TEXT" ]; then
|
||||
|
||||
@@ -18,8 +18,6 @@ if [ -n "$AGENT_ID" ]; then
|
||||
fi
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
|
||||
# Pin platform so this hook's telemetry is attributed to cursor.
|
||||
export MEM0_PLATFORM=cursor
|
||||
. "$SCRIPT_DIR/_identity.sh" 2>/dev/null || true
|
||||
|
||||
if [ -z "${MEM0_API_KEY:-}" ]; then
|
||||
|
||||
@@ -8,9 +8,6 @@ set -uo pipefail
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
|
||||
|
||||
# Pin platform so the shared script's telemetry is attributed to cursor.
|
||||
export MEM0_PLATFORM=cursor
|
||||
|
||||
TEXT=$("$SCRIPT_DIR/on_user_prompt.sh" 2>/dev/null || echo "")
|
||||
|
||||
if [ -z "$TEXT" ]; then
|
||||
|
||||
@@ -26,27 +26,16 @@ import sys
|
||||
import urllib.error
|
||||
import urllib.request
|
||||
|
||||
# Each editor surface ships its own manifest with its own version line
|
||||
# (Antigravity is on 0.1.x while Claude/Cursor/Codex are on 0.2.x), so we read
|
||||
# the manifest matching the detected platform rather than a single shared one.
|
||||
_PLATFORM_MANIFESTS = {
|
||||
"antigravity": ("..", "plugin.json"),
|
||||
"claude-code": ("..", ".claude-plugin", "plugin.json"),
|
||||
"cursor": ("..", ".cursor-plugin", "plugin.json"),
|
||||
"codex": ("..", ".codex-plugin", "plugin.json"),
|
||||
}
|
||||
_DEFAULT_MANIFEST = ("..", ".claude-plugin", "plugin.json")
|
||||
|
||||
|
||||
def _load_plugin_version(platform_name: str = "") -> str:
|
||||
parts = _PLATFORM_MANIFESTS.get(platform_name, _DEFAULT_MANIFEST)
|
||||
def _load_plugin_version() -> str:
|
||||
try:
|
||||
plugin_json = os.path.join(os.path.dirname(__file__), *parts)
|
||||
plugin_json = os.path.join(os.path.dirname(__file__), "..", ".claude-plugin", "plugin.json")
|
||||
with open(plugin_json) as f:
|
||||
return json.load(f).get("version", "unknown")
|
||||
except (OSError, json.JSONDecodeError, KeyError):
|
||||
return "unknown"
|
||||
|
||||
PLUGIN_VERSION = _load_plugin_version()
|
||||
|
||||
POSTHOG_API_KEY = "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX"
|
||||
POSTHOG_HOST = "https://us.i.posthog.com/i/v0/e/"
|
||||
@@ -69,11 +58,6 @@ def _distinct_id() -> str:
|
||||
|
||||
|
||||
def detect_platform() -> str:
|
||||
explicit = os.environ.get("MEM0_PLATFORM")
|
||||
if explicit:
|
||||
return explicit
|
||||
if os.environ.get("ANTIGRAVITY_PLUGIN_ROOT"):
|
||||
return "antigravity"
|
||||
if os.environ.get("PLUGIN_ROOT"):
|
||||
return "codex"
|
||||
if os.environ.get("CLAUDECODE") or os.environ.get("CLAUDE_PLUGIN_ROOT"):
|
||||
@@ -91,7 +75,6 @@ def is_enabled() -> bool:
|
||||
|
||||
def build_posthog_payload(event_name: str, properties: dict | None = None) -> dict:
|
||||
project_id = os.environ.get("MEM0_PROJECT_ID") or "unknown"
|
||||
plat = detect_platform()
|
||||
return {
|
||||
"api_key": POSTHOG_API_KEY,
|
||||
"distinct_id": _distinct_id(),
|
||||
@@ -99,8 +82,8 @@ def build_posthog_payload(event_name: str, properties: dict | None = None) -> di
|
||||
"properties": {
|
||||
**(properties or {}),
|
||||
"source": "plugin",
|
||||
"platform": plat,
|
||||
"plugin_version": _load_plugin_version(plat),
|
||||
"platform": detect_platform(),
|
||||
"plugin_version": PLUGIN_VERSION,
|
||||
"project_hash": _sha256(project_id),
|
||||
"os": sys.platform,
|
||||
"os_version": platform.version(),
|
||||
|
||||
@@ -89,7 +89,7 @@ def test_system_props_override_caller_props(monkeypatch):
|
||||
# System props must win
|
||||
assert props["source"] == "plugin"
|
||||
assert props["platform"] == telemetry.detect_platform()
|
||||
assert props["plugin_version"] == telemetry._load_plugin_version(telemetry.detect_platform())
|
||||
assert props["plugin_version"] == telemetry.PLUGIN_VERSION
|
||||
# Caller-only props still present
|
||||
assert props["memory_count"] == 42
|
||||
|
||||
@@ -125,31 +125,25 @@ def test_hash_deterministic():
|
||||
def test_platform_claude_code(monkeypatch):
|
||||
import telemetry
|
||||
|
||||
monkeypatch.delenv("MEM0_PLATFORM", raising=False)
|
||||
monkeypatch.delenv("ANTIGRAVITY_PLUGIN_ROOT", raising=False)
|
||||
monkeypatch.delenv("PLUGIN_ROOT", raising=False)
|
||||
monkeypatch.delenv("CURSOR_PLUGIN_ROOT", raising=False)
|
||||
monkeypatch.setenv("CLAUDECODE", "1")
|
||||
monkeypatch.delenv("CURSOR_PLUGIN_ROOT", raising=False)
|
||||
monkeypatch.delenv("CODEX_PLUGIN_ROOT", raising=False)
|
||||
assert telemetry.detect_platform() == "claude-code"
|
||||
|
||||
|
||||
def test_platform_cursor(monkeypatch):
|
||||
import telemetry
|
||||
|
||||
monkeypatch.delenv("MEM0_PLATFORM", raising=False)
|
||||
monkeypatch.delenv("ANTIGRAVITY_PLUGIN_ROOT", raising=False)
|
||||
monkeypatch.delenv("PLUGIN_ROOT", raising=False)
|
||||
monkeypatch.delenv("CLAUDECODE", raising=False)
|
||||
monkeypatch.delenv("CLAUDE_PLUGIN_ROOT", raising=False)
|
||||
monkeypatch.setenv("CURSOR_PLUGIN_ROOT", "/path")
|
||||
monkeypatch.delenv("CODEX_PLUGIN_ROOT", raising=False)
|
||||
assert telemetry.detect_platform() == "cursor"
|
||||
|
||||
|
||||
def test_platform_codex(monkeypatch):
|
||||
import telemetry
|
||||
|
||||
monkeypatch.delenv("MEM0_PLATFORM", raising=False)
|
||||
monkeypatch.delenv("ANTIGRAVITY_PLUGIN_ROOT", raising=False)
|
||||
monkeypatch.delenv("CLAUDECODE", raising=False)
|
||||
monkeypatch.delenv("CLAUDE_PLUGIN_ROOT", raising=False)
|
||||
monkeypatch.delenv("CURSOR_PLUGIN_ROOT", raising=False)
|
||||
@@ -157,48 +151,6 @@ def test_platform_codex(monkeypatch):
|
||||
assert telemetry.detect_platform() == "codex"
|
||||
|
||||
|
||||
def test_platform_explicit_override(monkeypatch):
|
||||
"""MEM0_PLATFORM wins over auto-detection so each editor can label
|
||||
itself reliably even when host env vars are ambiguous or absent."""
|
||||
import telemetry
|
||||
|
||||
monkeypatch.setenv("CLAUDE_PLUGIN_ROOT", "/path") # conflicting auto-signal
|
||||
monkeypatch.setenv("MEM0_PLATFORM", "cursor")
|
||||
assert telemetry.detect_platform() == "cursor"
|
||||
|
||||
|
||||
def test_platform_antigravity(monkeypatch):
|
||||
"""Antigravity sets CLAUDE_PLUGIN_ROOT for compatibility but must be
|
||||
attributed to its own platform, not claude-code."""
|
||||
import telemetry
|
||||
|
||||
monkeypatch.delenv("MEM0_PLATFORM", raising=False)
|
||||
monkeypatch.setenv("ANTIGRAVITY_PLUGIN_ROOT", "/ext")
|
||||
monkeypatch.setenv("CLAUDE_PLUGIN_ROOT", "/ext") # antigravity sets both
|
||||
assert telemetry.detect_platform() == "antigravity"
|
||||
|
||||
|
||||
def test_plugin_version_is_per_editor(monkeypatch):
|
||||
"""Each editor reports the version from its OWN manifest. Antigravity is on
|
||||
a 0.1.x line while Claude/Cursor/Codex are on 0.2.x, so they must not all
|
||||
report the same shared version."""
|
||||
import telemetry
|
||||
|
||||
plugin_dir = os.path.join(os.path.dirname(__file__), "..")
|
||||
manifests = {
|
||||
"antigravity": "plugin.json",
|
||||
"claude-code": os.path.join(".claude-plugin", "plugin.json"),
|
||||
"cursor": os.path.join(".cursor-plugin", "plugin.json"),
|
||||
"codex": os.path.join(".codex-plugin", "plugin.json"),
|
||||
}
|
||||
for plat, rel in manifests.items():
|
||||
monkeypatch.setenv("MEM0_PLATFORM", plat)
|
||||
with open(os.path.join(plugin_dir, rel)) as f:
|
||||
expected = json.load(f)["version"]
|
||||
payload = telemetry.build_posthog_payload("plugin.test")
|
||||
assert payload["properties"]["plugin_version"] == expected, f"{plat} should report {expected} from {rel}"
|
||||
|
||||
|
||||
def test_send_fails_silently(monkeypatch):
|
||||
import telemetry
|
||||
|
||||
@@ -223,39 +175,3 @@ def test_cli_no_args_exits_nonzero(monkeypatch):
|
||||
monkeypatch.delenv("MEM0_TELEMETRY", raising=False)
|
||||
monkeypatch.setattr(sys, "argv", ["telemetry.py"])
|
||||
assert telemetry.main() == 1
|
||||
|
||||
|
||||
def test_cursor_wrappers_pin_platform():
|
||||
"""Cursor wrappers delegate to the shared scripts, which auto-detect the
|
||||
platform from host env vars. Since Cursor may not export CURSOR_PLUGIN_ROOT
|
||||
to the subprocess, each wrapper must pin MEM0_PLATFORM=cursor so the
|
||||
delegated telemetry is attributed to cursor, not the 'plugin' fallback."""
|
||||
scripts_dir = os.path.join(os.path.dirname(__file__), "..", "scripts")
|
||||
cursor_wrappers = [
|
||||
"on_session_start_cursor.sh",
|
||||
"on_user_prompt_cursor.sh",
|
||||
"on_post_tool_use_cursor.sh",
|
||||
"on_pre_compact_cursor.sh",
|
||||
"on_stop_cursor.sh",
|
||||
]
|
||||
for name in cursor_wrappers:
|
||||
with open(os.path.join(scripts_dir, name)) as f:
|
||||
content = f.read()
|
||||
assert "export MEM0_PLATFORM=cursor" in content, f"{name} must `export MEM0_PLATFORM=cursor` before delegating"
|
||||
|
||||
|
||||
def test_codex_hooks_pin_platform():
|
||||
"""Codex installs standalone hooks (absolute paths) via install_codex_hooks.py,
|
||||
so PLUGIN_ROOT is not set at runtime and the platform falls back to 'plugin'.
|
||||
Codex runs hook commands through a shell, so every command pins
|
||||
MEM0_PLATFORM=codex inline for correct attribution."""
|
||||
hooks_path = os.path.join(os.path.dirname(__file__), "..", "hooks", "codex-hooks.json")
|
||||
with open(hooks_path) as f:
|
||||
config = json.load(f)
|
||||
|
||||
commands = [
|
||||
h["command"] for entries in config["hooks"].values() for entry in entries for h in entry.get("hooks", [])
|
||||
]
|
||||
assert commands, "expected at least one codex hook command"
|
||||
for cmd in commands:
|
||||
assert "MEM0_PLATFORM=codex" in cmd, f"codex hook command missing platform pin: {cmd}"
|
||||
|
||||
@@ -25,11 +25,7 @@ import type {
|
||||
AddOptions,
|
||||
SearchOptions,
|
||||
} from "./types.ts";
|
||||
import {
|
||||
createProvider,
|
||||
customCategoryMapToList,
|
||||
providerToBackend,
|
||||
} from "./providers.ts";
|
||||
import { createProvider, providerToBackend } from "./providers.ts";
|
||||
import { mem0ConfigSchema } from "./config.ts";
|
||||
import type { FileConfig } from "./config.ts";
|
||||
import { createPublicArtifactsProvider } from "./public-artifacts.ts";
|
||||
@@ -293,8 +289,7 @@ const memoryPlugin = definePluginEntry({
|
||||
if (runId) opts.run_id = runId;
|
||||
// Pass customInstructions and customCategories to control what Mem0 extracts
|
||||
if (cfg.customInstructions) opts.custom_instructions = cfg.customInstructions;
|
||||
const customCategories = customCategoryMapToList(cfg.customCategories);
|
||||
if (customCategories) opts.custom_categories = customCategories;
|
||||
if (cfg.customCategories) opts.custom_categories = cfg.customCategories;
|
||||
return opts;
|
||||
}
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "@mem0/openclaw-mem0",
|
||||
"version": "1.0.13",
|
||||
"version": "1.0.12",
|
||||
"type": "module",
|
||||
"description": "Mem0 memory backend for OpenClaw — platform or self-hosted open-source",
|
||||
"license": "Apache-2.0",
|
||||
@@ -35,7 +35,7 @@
|
||||
},
|
||||
"dependencies": {
|
||||
"@sinclair/typebox": "0.34.47",
|
||||
"mem0ai": "3.0.7"
|
||||
"mem0ai": "3.0.6"
|
||||
},
|
||||
"openclaw": {
|
||||
"extensions": [
|
||||
|
||||
Generated
+144
-7
@@ -20,8 +20,8 @@ importers:
|
||||
specifier: 0.34.47
|
||||
version: 0.34.47
|
||||
mem0ai:
|
||||
specifier: 3.0.7
|
||||
version: 3.0.7(@anthropic-ai/sdk@0.40.1)(@azure/identity@4.13.1)(@azure/search-documents@12.2.0)(@cloudflare/workers-types@4.20260610.1)(@google/genai@1.52.0)(@langchain/core@1.1.48(openai@4.104.0(ws@8.21.0)(zod@3.25.76))(ws@8.21.0))(@mistralai/mistralai@1.15.1)(@qdrant/js-client-rest@1.18.0(typescript@5.9.3))(@supabase/supabase-js@2.108.1)(@types/jest@29.5.14)(@types/pg@8.11.0)(better-sqlite3@12.10.0)(cloudflare@4.5.0)(compromise@14.15.1)(groq-sdk@0.3.0)(natural@8.1.1)(ollama@0.5.18)(pg@8.21.0)(redis@5.12.1)(ws@8.21.0)
|
||||
specifier: 3.0.6
|
||||
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|
||||
pg-native: '>=3.0.1'
|
||||
peerDependenciesMeta:
|
||||
pg-native:
|
||||
optional: true
|
||||
|
||||
pg@8.21.0:
|
||||
resolution: {integrity: sha512-AUP1EYJuHraQGsVoCQVIcM7TEJVGtDzxWtGFZd8rds9d+CCXlU5Js1rYgfLNvxy9iJrpHjGrRjoi/3BT9fRyiA==}
|
||||
engines: {node: '>= 16.0.0'}
|
||||
@@ -1796,6 +1868,9 @@ packages:
|
||||
resolution: {integrity: sha512-GDhwkLfywWL2s6vEjyhri+eXmfH6j1L7JE27WhqLeYzoh/A3DBaYGEj2H/HFZCn/kMfim73FXxEJTw06WtxQwg==}
|
||||
engines: {node: '>= 14.18.0'}
|
||||
|
||||
redis@4.7.1:
|
||||
resolution: {integrity: sha512-S1bJDnqLftzHXHP8JsT5II/CtHWQrASX5K96REjWjlmWKrviSOLWmM7QnRLstAWsu1VBBV1ffV6DzCvxNP0UJQ==}
|
||||
|
||||
redis@5.12.1:
|
||||
resolution: {integrity: sha512-LDsoVvb/CpoV9EN3FXvgvSHNJWuCIzl9MiO3ppOevuGLpSGJhwfQjpEwfFJcQvNSddHADDdZaWx0HnmMxRXG7g==}
|
||||
engines: {node: '>= 18.19.0'}
|
||||
@@ -2150,6 +2225,9 @@ packages:
|
||||
resolution: {integrity: sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==}
|
||||
engines: {node: '>=0.4'}
|
||||
|
||||
yallist@4.0.0:
|
||||
resolution: {integrity: sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==}
|
||||
|
||||
zod-to-json-schema@3.25.2:
|
||||
resolution: {integrity: sha512-O/PgfnpT1xKSDeQYSCfRI5Gy3hPf91mKVDuYLUHZJMiDFptvP41MSnWofm8dnCm0256ZNfZIM7DSzuSMAFnjHA==}
|
||||
peerDependencies:
|
||||
@@ -2511,22 +2589,48 @@ snapshots:
|
||||
|
||||
'@qdrant/openapi-typescript-fetch@1.2.6': {}
|
||||
|
||||
'@redis/bloom@1.2.0(@redis/client@1.6.1)':
|
||||
dependencies:
|
||||
'@redis/client': 1.6.1
|
||||
|
||||
'@redis/bloom@5.12.1(@redis/client@5.12.1)':
|
||||
dependencies:
|
||||
'@redis/client': 5.12.1
|
||||
|
||||
'@redis/client@1.6.1':
|
||||
dependencies:
|
||||
cluster-key-slot: 1.1.2
|
||||
generic-pool: 3.9.0
|
||||
yallist: 4.0.0
|
||||
|
||||
'@redis/client@5.12.1':
|
||||
dependencies:
|
||||
cluster-key-slot: 1.1.2
|
||||
|
||||
'@redis/graph@1.1.1(@redis/client@1.6.1)':
|
||||
dependencies:
|
||||
'@redis/client': 1.6.1
|
||||
|
||||
'@redis/json@1.0.7(@redis/client@1.6.1)':
|
||||
dependencies:
|
||||
'@redis/client': 1.6.1
|
||||
|
||||
'@redis/json@5.12.1(@redis/client@5.12.1)':
|
||||
dependencies:
|
||||
'@redis/client': 5.12.1
|
||||
|
||||
'@redis/search@1.2.0(@redis/client@1.6.1)':
|
||||
dependencies:
|
||||
'@redis/client': 1.6.1
|
||||
|
||||
'@redis/search@5.12.1(@redis/client@5.12.1)':
|
||||
dependencies:
|
||||
'@redis/client': 5.12.1
|
||||
|
||||
'@redis/time-series@1.1.0(@redis/client@1.6.1)':
|
||||
dependencies:
|
||||
'@redis/client': 1.6.1
|
||||
|
||||
'@redis/time-series@5.12.1(@redis/client@5.12.1)':
|
||||
dependencies:
|
||||
'@redis/client': 5.12.1
|
||||
@@ -2905,6 +3009,8 @@ snapshots:
|
||||
|
||||
buffer-equal-constant-time@1.0.1: {}
|
||||
|
||||
buffer-writer@2.0.0: {}
|
||||
|
||||
buffer@5.7.1:
|
||||
dependencies:
|
||||
base64-js: 1.5.1
|
||||
@@ -3170,6 +3276,8 @@ snapshots:
|
||||
transitivePeerDependencies:
|
||||
- supports-color
|
||||
|
||||
generic-pool@3.9.0: {}
|
||||
|
||||
get-intrinsic@1.3.0:
|
||||
dependencies:
|
||||
call-bind-apply-helpers: 1.0.2
|
||||
@@ -3474,7 +3582,7 @@ snapshots:
|
||||
crypt: 0.0.2
|
||||
is-buffer: 1.1.6
|
||||
|
||||
mem0ai@3.0.7(@anthropic-ai/sdk@0.40.1)(@azure/identity@4.13.1)(@azure/search-documents@12.2.0)(@cloudflare/workers-types@4.20260610.1)(@google/genai@1.52.0)(@langchain/core@1.1.48(openai@4.104.0(ws@8.21.0)(zod@3.25.76))(ws@8.21.0))(@mistralai/mistralai@1.15.1)(@qdrant/js-client-rest@1.18.0(typescript@5.9.3))(@supabase/supabase-js@2.108.1)(@types/jest@29.5.14)(@types/pg@8.11.0)(better-sqlite3@12.10.0)(cloudflare@4.5.0)(compromise@14.15.1)(groq-sdk@0.3.0)(natural@8.1.1)(ollama@0.5.18)(pg@8.21.0)(redis@5.12.1)(ws@8.21.0):
|
||||
mem0ai@3.0.6(@anthropic-ai/sdk@0.40.1)(@azure/identity@4.13.1)(@azure/search-documents@12.2.0)(@cloudflare/workers-types@4.20260610.1)(@google/genai@1.52.0)(@langchain/core@1.1.48(openai@4.104.0(ws@8.21.0)(zod@3.25.76))(ws@8.21.0))(@mistralai/mistralai@1.15.1)(@qdrant/js-client-rest@1.18.0(typescript@5.9.3))(@supabase/supabase-js@2.108.1)(@types/jest@29.5.14)(@types/pg@8.11.0)(better-sqlite3@12.10.0)(cloudflare@4.5.0)(compromise@14.15.1)(groq-sdk@0.3.0)(natural@8.1.1)(ollama@0.5.18)(pg@8.11.3)(redis@4.7.1)(ws@8.21.0):
|
||||
dependencies:
|
||||
'@anthropic-ai/sdk': 0.40.1
|
||||
'@azure/identity': 4.13.1
|
||||
@@ -3495,8 +3603,8 @@ snapshots:
|
||||
natural: 8.1.1
|
||||
ollama: 0.5.18
|
||||
openai: 4.104.0(ws@8.21.0)(zod@3.25.76)
|
||||
pg: 8.21.0
|
||||
redis: 5.12.1
|
||||
pg: 8.11.3
|
||||
redis: 4.7.1
|
||||
uuid: 14.0.0
|
||||
zod: 3.25.76
|
||||
transitivePeerDependencies:
|
||||
@@ -3675,6 +3783,8 @@ snapshots:
|
||||
dependencies:
|
||||
p-finally: 1.0.0
|
||||
|
||||
packet-reader@1.0.0: {}
|
||||
|
||||
pathe@2.0.3: {}
|
||||
|
||||
pg-cloudflare@1.4.0:
|
||||
@@ -3686,6 +3796,10 @@ snapshots:
|
||||
|
||||
pg-numeric@1.0.2: {}
|
||||
|
||||
pg-pool@3.14.0(pg@8.11.3):
|
||||
dependencies:
|
||||
pg: 8.11.3
|
||||
|
||||
pg-pool@3.14.0(pg@8.21.0):
|
||||
dependencies:
|
||||
pg: 8.21.0
|
||||
@@ -3710,6 +3824,18 @@ snapshots:
|
||||
postgres-interval: 3.0.0
|
||||
postgres-range: 1.1.4
|
||||
|
||||
pg@8.11.3:
|
||||
dependencies:
|
||||
buffer-writer: 2.0.0
|
||||
packet-reader: 1.0.0
|
||||
pg-connection-string: 2.13.0
|
||||
pg-pool: 3.14.0(pg@8.11.3)
|
||||
pg-protocol: 1.14.0
|
||||
pg-types: 2.2.0
|
||||
pgpass: 1.0.5
|
||||
optionalDependencies:
|
||||
pg-cloudflare: 1.4.0
|
||||
|
||||
pg@8.21.0:
|
||||
dependencies:
|
||||
pg-connection-string: 2.13.0
|
||||
@@ -3834,6 +3960,15 @@ snapshots:
|
||||
|
||||
readdirp@4.1.2: {}
|
||||
|
||||
redis@4.7.1:
|
||||
dependencies:
|
||||
'@redis/bloom': 1.2.0(@redis/client@1.6.1)
|
||||
'@redis/client': 1.6.1
|
||||
'@redis/graph': 1.1.1(@redis/client@1.6.1)
|
||||
'@redis/json': 1.0.7(@redis/client@1.6.1)
|
||||
'@redis/search': 1.2.0(@redis/client@1.6.1)
|
||||
'@redis/time-series': 1.1.0(@redis/client@1.6.1)
|
||||
|
||||
redis@5.12.1:
|
||||
dependencies:
|
||||
'@redis/bloom': 5.12.1(@redis/client@5.12.1)
|
||||
@@ -4146,6 +4281,8 @@ snapshots:
|
||||
|
||||
xtend@4.0.2: {}
|
||||
|
||||
yallist@4.0.0: {}
|
||||
|
||||
zod-to-json-schema@3.25.2(zod@4.4.3):
|
||||
dependencies:
|
||||
zod: 4.4.3
|
||||
|
||||
@@ -13,16 +13,6 @@ import type {
|
||||
AddResult,
|
||||
} from "./types.ts";
|
||||
|
||||
export function customCategoryMapToList(
|
||||
categories?: Record<string, string>,
|
||||
): Array<Record<string, string>> | undefined {
|
||||
if (!categories || typeof categories !== "object") return undefined;
|
||||
const items = Object.entries(categories).map(([name, description]) => ({
|
||||
[name]: description,
|
||||
}));
|
||||
return items.length ? items : undefined;
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// Result Normalizers
|
||||
// ============================================================================
|
||||
|
||||
@@ -6,11 +6,7 @@
|
||||
*/
|
||||
import { describe, it, expect, vi, beforeEach } from "vitest";
|
||||
|
||||
import {
|
||||
createProvider,
|
||||
customCategoryMapToList,
|
||||
providerToBackend,
|
||||
} from "../providers.ts";
|
||||
import { providerToBackend } from "../providers.ts";
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Mock provider factory
|
||||
@@ -41,77 +37,6 @@ beforeEach(() => {
|
||||
vi.resetAllMocks();
|
||||
});
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// customCategoryMapToList
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
describe("customCategoryMapToList", () => {
|
||||
it("converts category maps to the Mem0 SDK list shape", () => {
|
||||
expect(
|
||||
customCategoryMapToList({
|
||||
preference: "User preferences",
|
||||
work: "Work context",
|
||||
}),
|
||||
).toEqual([
|
||||
{ preference: "User preferences" },
|
||||
{ work: "Work context" },
|
||||
]);
|
||||
});
|
||||
|
||||
it("returns undefined for empty or missing category maps", () => {
|
||||
expect(customCategoryMapToList()).toBeUndefined();
|
||||
expect(customCategoryMapToList({})).toBeUndefined();
|
||||
});
|
||||
});
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// PlatformProvider
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
describe("PlatformProvider", () => {
|
||||
it("passes custom_categories as a list to the Mem0 SDK", async () => {
|
||||
let addOptions: Record<string, unknown> | undefined;
|
||||
|
||||
vi.doMock("mem0ai", () => ({
|
||||
default: class MockMemoryClient {
|
||||
async add(_messages: unknown, opts: Record<string, unknown>) {
|
||||
addOptions = opts;
|
||||
return { results: [] };
|
||||
}
|
||||
},
|
||||
}));
|
||||
|
||||
const provider = createProvider(
|
||||
{
|
||||
mode: "platform",
|
||||
apiKey: "test-key",
|
||||
userId: "test-user",
|
||||
autoCapture: true,
|
||||
autoRecall: true,
|
||||
customInstructions: "Store durable facts.",
|
||||
customCategories: {},
|
||||
searchThreshold: 0.1,
|
||||
topK: 5,
|
||||
},
|
||||
{ resolvePath: (p: string) => p } as any,
|
||||
);
|
||||
|
||||
await provider.add([{ role: "user", content: "Remember this" }], {
|
||||
user_id: "test-user",
|
||||
custom_categories: customCategoryMapToList({
|
||||
preference: "User preferences",
|
||||
}),
|
||||
source: "OPENCLAW",
|
||||
});
|
||||
|
||||
expect(addOptions?.customCategories).toEqual([
|
||||
{ preference: "User preferences" },
|
||||
]);
|
||||
|
||||
vi.doUnmock("mem0ai");
|
||||
});
|
||||
});
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// search
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
@@ -36,7 +36,7 @@ export interface AddOptions {
|
||||
user_id: string;
|
||||
run_id?: string;
|
||||
custom_instructions?: string;
|
||||
custom_categories?: Array<Record<string, string>>;
|
||||
custom_categories?: Record<string, string>;
|
||||
source?: string;
|
||||
// Agentic harness additions
|
||||
infer?: boolean;
|
||||
|
||||
@@ -1,56 +0,0 @@
|
||||
import { afterEach, beforeEach, describe, expect, it } from "vitest";
|
||||
import {
|
||||
_getEventQueue,
|
||||
_resetForTesting,
|
||||
captureCommandEvent,
|
||||
captureEvent,
|
||||
captureToolEvent,
|
||||
} from "./telemetry.ts";
|
||||
|
||||
const CTX = { apiKey: "m0-testkey123" };
|
||||
|
||||
function findEvent(name: string): Record<string, unknown> | undefined {
|
||||
return _getEventQueue().find((e) => (e as Record<string, unknown>).event === name) as
|
||||
| Record<string, unknown>
|
||||
| undefined;
|
||||
}
|
||||
|
||||
beforeEach(() => {
|
||||
delete process.env.MEM0_TELEMETRY;
|
||||
_resetForTesting();
|
||||
});
|
||||
|
||||
afterEach(() => {
|
||||
delete process.env.MEM0_TELEMETRY;
|
||||
_resetForTesting();
|
||||
});
|
||||
|
||||
describe("pi-agent telemetry", () => {
|
||||
it.each(["add", "search", "update", "delete"])(
|
||||
"captureToolEvent tracks the %s operation as pi.tool.mem0_memory",
|
||||
(action) => {
|
||||
captureToolEvent(action, { success: true }, CTX);
|
||||
const ev = findEvent("pi.tool.mem0_memory");
|
||||
expect(ev).toBeDefined();
|
||||
const props = ev!.properties as Record<string, unknown>;
|
||||
expect(props.action).toBe(action);
|
||||
expect(props.success).toBe(true);
|
||||
expect(props.source).toBe("PI_AGENT_PLUGIN");
|
||||
expect(props.$process_person_profile).toBe(false);
|
||||
},
|
||||
);
|
||||
|
||||
it("captureCommandEvent emits a namespaced pi.command.* event", () => {
|
||||
captureCommandEvent("mem0-search", { result_count: 3 }, CTX);
|
||||
const ev = findEvent("pi.command.mem0-search");
|
||||
expect(ev).toBeDefined();
|
||||
expect((ev!.properties as Record<string, unknown>).result_count).toBe(3);
|
||||
});
|
||||
|
||||
it("respects the MEM0_TELEMETRY opt-out", () => {
|
||||
process.env.MEM0_TELEMETRY = "false";
|
||||
captureEvent("pi.session.start", {}, CTX);
|
||||
captureToolEvent("add", {}, CTX);
|
||||
expect(_getEventQueue()).toHaveLength(0);
|
||||
});
|
||||
});
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "mem0ai",
|
||||
"version": "3.0.8",
|
||||
"version": "3.0.7",
|
||||
"description": "The Memory Layer For Your AI Apps",
|
||||
"main": "./dist/index.js",
|
||||
"module": "./dist/index.mjs",
|
||||
|
||||
@@ -36,29 +36,10 @@ import {
|
||||
SearchMemoryOptions,
|
||||
DeleteAllMemoryOptions,
|
||||
GetAllMemoryOptions,
|
||||
UpdateProjectOptions,
|
||||
} from "./memory.types";
|
||||
import { parse_vision_messages } from "../utils/memory";
|
||||
import { HistoryManager } from "../storage/base";
|
||||
import { captureClientEvent } from "../utils/telemetry";
|
||||
import {
|
||||
detectScaleThresholdFromAddResult,
|
||||
detectScaleThresholdFromTopK,
|
||||
detectPerformanceSlowQuery,
|
||||
detectTemporalUsageFromMetadata,
|
||||
detectTemporalUsageFromSearch,
|
||||
displayDecayUsageNotice,
|
||||
displayFirstRunNotice,
|
||||
displayPerformanceSlowQueryNotice,
|
||||
displayScaleThresholdNotice,
|
||||
displayTemporalUsageNotice,
|
||||
getDecayFeatureErrorMessage,
|
||||
getDecayUsageDeleteCountAfterSuccess,
|
||||
getTemporalFeatureErrorMessage,
|
||||
isDecayUsageDeleteEligible,
|
||||
PerformanceSlowQueryTrigger,
|
||||
ScaleThresholdTrigger,
|
||||
} from "../utils/notices";
|
||||
import { lemmatizeForBm25 } from "../utils/lemmatization";
|
||||
import {
|
||||
extractEntities,
|
||||
@@ -513,73 +494,6 @@ export class Memory {
|
||||
}
|
||||
}
|
||||
|
||||
private async _displayFirstRunNotice(triggerFunction: string) {
|
||||
try {
|
||||
await this._getTelemetryId();
|
||||
await displayFirstRunNotice(this, triggerFunction);
|
||||
} catch {}
|
||||
}
|
||||
|
||||
private async _displayDecayUsageNotice(trigger: {
|
||||
triggerFunction: "delete" | "delete_all";
|
||||
triggerSource: "delete_count" | "delete_all";
|
||||
triggerReason: "repeated_deletes" | "bulk_delete";
|
||||
deleteCount?: number;
|
||||
deletedCount?: number;
|
||||
}) {
|
||||
try {
|
||||
await this._getTelemetryId();
|
||||
await displayDecayUsageNotice(this, trigger);
|
||||
} catch {}
|
||||
}
|
||||
|
||||
private async _displayTemporalUsageNotice(trigger: {
|
||||
triggerFunction: "add" | "search";
|
||||
triggerSource: "metadata" | "query" | "filter";
|
||||
triggerReason:
|
||||
| "date_like_metadata"
|
||||
| "relative_phrase"
|
||||
| "date_like_query"
|
||||
| "date_range_filter";
|
||||
}) {
|
||||
try {
|
||||
await this._getTelemetryId();
|
||||
await displayTemporalUsageNotice(this, trigger);
|
||||
} catch {}
|
||||
}
|
||||
|
||||
private async _displayScaleThresholdNotice(trigger: ScaleThresholdTrigger) {
|
||||
try {
|
||||
await this._getTelemetryId();
|
||||
await displayScaleThresholdNotice(this, trigger);
|
||||
} catch {}
|
||||
}
|
||||
|
||||
private async _displayPerformanceSlowQueryNotice(
|
||||
trigger: PerformanceSlowQueryTrigger,
|
||||
) {
|
||||
try {
|
||||
await this._getTelemetryId();
|
||||
await displayPerformanceSlowQueryNotice(this, trigger);
|
||||
} catch {}
|
||||
}
|
||||
|
||||
private async _getNoticeTelemetryId() {
|
||||
try {
|
||||
if (
|
||||
!this.telemetryId ||
|
||||
this.telemetryId === "anonymous" ||
|
||||
this.telemetryId === "anonymous-supabase"
|
||||
) {
|
||||
this.telemetryId = (await getOrCreateMem0UserId()) || "anonymous";
|
||||
}
|
||||
return this.telemetryId;
|
||||
} catch {
|
||||
this.telemetryId = "anonymous";
|
||||
return this.telemetryId;
|
||||
}
|
||||
}
|
||||
|
||||
static fromConfig(configDict: Record<string, any>): Memory {
|
||||
try {
|
||||
const config = MemoryConfigSchema.parse(configDict);
|
||||
@@ -590,29 +504,10 @@ export class Memory {
|
||||
}
|
||||
}
|
||||
|
||||
async updateProject(options: UpdateProjectOptions = {}): Promise<never> {
|
||||
if (options?.decay === true) {
|
||||
await this._getNoticeTelemetryId();
|
||||
throw new Error(await getDecayFeatureErrorMessage(this));
|
||||
}
|
||||
|
||||
throw new Error("Project updates are not supported by the OSS Memory SDK.");
|
||||
}
|
||||
|
||||
async add(
|
||||
messages: string | Message[],
|
||||
config: AddMemoryOptions,
|
||||
): Promise<SearchResult> {
|
||||
if (config?.timestamp !== undefined) {
|
||||
await this._getNoticeTelemetryId();
|
||||
throw new Error(
|
||||
await getTemporalFeatureErrorMessage(this, {
|
||||
triggerFunction: "add",
|
||||
triggerParameter: "timestamp",
|
||||
}),
|
||||
);
|
||||
}
|
||||
|
||||
// Validate messages input
|
||||
if (messages === undefined || messages === null) {
|
||||
throw new Error(
|
||||
@@ -620,10 +515,6 @@ export class Memory {
|
||||
);
|
||||
}
|
||||
|
||||
const temporalUsageNotice = detectTemporalUsageFromMetadata(
|
||||
config?.metadata,
|
||||
);
|
||||
|
||||
await this._ensureInitialized();
|
||||
await this._captureEvent("add", {
|
||||
message_count: Array.isArray(messages) ? messages.length : 1,
|
||||
@@ -663,27 +554,6 @@ export class Memory {
|
||||
infer,
|
||||
);
|
||||
|
||||
if (temporalUsageNotice) {
|
||||
await this._displayTemporalUsageNotice({
|
||||
triggerFunction: "add",
|
||||
triggerSource: temporalUsageNotice.triggerSource,
|
||||
triggerReason: temporalUsageNotice.triggerReason,
|
||||
});
|
||||
} else {
|
||||
const scaleThresholdNotice = await detectScaleThresholdFromAddResult(
|
||||
this,
|
||||
vectorStoreResult,
|
||||
);
|
||||
if (scaleThresholdNotice) {
|
||||
await this._displayScaleThresholdNotice({
|
||||
triggerFunction: "add",
|
||||
...scaleThresholdNotice,
|
||||
});
|
||||
} else {
|
||||
await this._displayFirstRunNotice("add");
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
results: vectorStoreResult,
|
||||
};
|
||||
@@ -1126,10 +996,7 @@ export class Memory {
|
||||
async get(memoryId: string): Promise<MemoryItem | null> {
|
||||
await this._ensureInitialized();
|
||||
const memory = await this.vectorStore.get(memoryId);
|
||||
if (!memory) {
|
||||
await this._displayFirstRunNotice("get");
|
||||
return null;
|
||||
}
|
||||
if (!memory) return null;
|
||||
|
||||
const filters = {
|
||||
...(memory.payload.user_id && { user_id: memory.payload.user_id }),
|
||||
@@ -1164,30 +1031,13 @@ export class Memory {
|
||||
}
|
||||
}
|
||||
|
||||
const result = { ...memoryItem, ...filters };
|
||||
await this._displayFirstRunNotice("get");
|
||||
return result;
|
||||
return { ...memoryItem, ...filters };
|
||||
}
|
||||
|
||||
async search(
|
||||
query: string,
|
||||
config: SearchMemoryOptions,
|
||||
): Promise<SearchResult> {
|
||||
if (config?.referenceDate !== undefined) {
|
||||
await this._getNoticeTelemetryId();
|
||||
throw new Error(
|
||||
await getTemporalFeatureErrorMessage(this, {
|
||||
triggerFunction: "search",
|
||||
triggerParameter: "referenceDate",
|
||||
}),
|
||||
);
|
||||
}
|
||||
|
||||
const temporalUsageNotice = detectTemporalUsageFromSearch(
|
||||
query,
|
||||
config?.filters,
|
||||
);
|
||||
|
||||
// Reject top-level entity params - must use filters instead
|
||||
rejectTopLevelEntityParams(config as Record<string, any>, "search");
|
||||
|
||||
@@ -1255,8 +1105,6 @@ export class Memory {
|
||||
);
|
||||
}
|
||||
|
||||
const searchStartMs = Date.now();
|
||||
|
||||
// Step 1: Preprocess query
|
||||
const queryLemmatized = lemmatizeForBm25(query);
|
||||
const queryEntities = extractEntities(query);
|
||||
@@ -1432,41 +1280,9 @@ export class Memory {
|
||||
};
|
||||
});
|
||||
|
||||
const result = {
|
||||
return {
|
||||
results,
|
||||
};
|
||||
const searchElapsedMs = Date.now() - searchStartMs;
|
||||
if (temporalUsageNotice) {
|
||||
await this._displayTemporalUsageNotice({
|
||||
triggerFunction: "search",
|
||||
triggerSource: temporalUsageNotice.triggerSource,
|
||||
triggerReason: temporalUsageNotice.triggerReason,
|
||||
});
|
||||
} else {
|
||||
const scaleThresholdNotice = detectScaleThresholdFromTopK(topK);
|
||||
if (scaleThresholdNotice) {
|
||||
await this._displayScaleThresholdNotice({
|
||||
triggerFunction: "search",
|
||||
...scaleThresholdNotice,
|
||||
});
|
||||
} else {
|
||||
const performanceSlowQueryNotice = detectPerformanceSlowQuery(
|
||||
searchElapsedMs,
|
||||
topK,
|
||||
results.length,
|
||||
);
|
||||
if (performanceSlowQueryNotice) {
|
||||
await this._displayPerformanceSlowQueryNotice({
|
||||
triggerFunction: "search",
|
||||
triggerReason: "slow_query",
|
||||
...performanceSlowQueryNotice,
|
||||
});
|
||||
} else {
|
||||
await this._displayFirstRunNotice("search");
|
||||
}
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
async update(memoryId: string, data: string): Promise<{ message: string }> {
|
||||
@@ -1474,28 +1290,14 @@ export class Memory {
|
||||
await this._captureEvent("update", { memory_id: memoryId });
|
||||
const embedding = await this.embedder.embed(data);
|
||||
await this.updateMemory(memoryId, data, { [data]: embedding });
|
||||
const result = { message: "Memory updated successfully!" };
|
||||
await this._displayFirstRunNotice("update");
|
||||
return result;
|
||||
return { message: "Memory updated successfully!" };
|
||||
}
|
||||
|
||||
async delete(memoryId: string): Promise<{ message: string }> {
|
||||
await this._ensureInitialized();
|
||||
await this._captureEvent("delete", { memory_id: memoryId });
|
||||
await this.deleteMemory(memoryId);
|
||||
const result = { message: "Memory deleted successfully!" };
|
||||
const deleteCount = getDecayUsageDeleteCountAfterSuccess();
|
||||
if (isDecayUsageDeleteEligible(deleteCount)) {
|
||||
await this._displayDecayUsageNotice({
|
||||
triggerFunction: "delete",
|
||||
triggerSource: "delete_count",
|
||||
triggerReason: "repeated_deletes",
|
||||
deleteCount,
|
||||
});
|
||||
} else {
|
||||
await this._displayFirstRunNotice("delete");
|
||||
}
|
||||
return result;
|
||||
return { message: "Memory deleted successfully!" };
|
||||
}
|
||||
|
||||
async deleteAll(
|
||||
@@ -1526,25 +1328,12 @@ export class Memory {
|
||||
await this.deleteMemory(memory.id);
|
||||
}
|
||||
|
||||
const result = { message: "Memories deleted successfully!" };
|
||||
if (memories.length > 0) {
|
||||
await this._displayDecayUsageNotice({
|
||||
triggerFunction: "delete_all",
|
||||
triggerSource: "delete_all",
|
||||
triggerReason: "bulk_delete",
|
||||
deletedCount: memories.length,
|
||||
});
|
||||
} else {
|
||||
await this._displayFirstRunNotice("delete_all");
|
||||
}
|
||||
return result;
|
||||
return { message: "Memories deleted successfully!" };
|
||||
}
|
||||
|
||||
async history(memoryId: string): Promise<any[]> {
|
||||
await this._ensureInitialized();
|
||||
const result = await this.db.getHistory(memoryId);
|
||||
await this._displayFirstRunNotice("history");
|
||||
return result;
|
||||
return this.db.getHistory(memoryId);
|
||||
}
|
||||
|
||||
async reset(): Promise<void> {
|
||||
@@ -1596,7 +1385,6 @@ export class Memory {
|
||||
console.error(this._initError);
|
||||
});
|
||||
await this._initPromise;
|
||||
await this._displayFirstRunNotice("reset");
|
||||
}
|
||||
|
||||
async getAll(config: GetAllMemoryOptions): Promise<SearchResult> {
|
||||
@@ -1664,17 +1452,7 @@ export class Memory {
|
||||
...(mem.payload.run_id && { run_id: mem.payload.run_id }),
|
||||
}));
|
||||
|
||||
const result = { results };
|
||||
const scaleThresholdNotice = detectScaleThresholdFromTopK(topK);
|
||||
if (scaleThresholdNotice) {
|
||||
await this._displayScaleThresholdNotice({
|
||||
triggerFunction: "get_all",
|
||||
...scaleThresholdNotice,
|
||||
});
|
||||
} else {
|
||||
await this._displayFirstRunNotice("get_all");
|
||||
}
|
||||
return result;
|
||||
return { results };
|
||||
}
|
||||
|
||||
private async createMemory(
|
||||
|
||||
@@ -11,7 +11,6 @@ export interface AddMemoryOptions extends Entity {
|
||||
metadata?: Record<string, any>;
|
||||
filters?: SearchFilters;
|
||||
infer?: boolean;
|
||||
timestamp?: number | string | Date | null;
|
||||
}
|
||||
|
||||
export interface SearchMemoryOptions {
|
||||
@@ -19,7 +18,6 @@ export interface SearchMemoryOptions {
|
||||
filters?: SearchFilters;
|
||||
threshold?: number;
|
||||
explain?: boolean;
|
||||
referenceDate?: number | string | Date | null;
|
||||
}
|
||||
|
||||
export interface GetAllMemoryOptions {
|
||||
@@ -28,8 +26,3 @@ export interface GetAllMemoryOptions {
|
||||
}
|
||||
|
||||
export interface DeleteAllMemoryOptions extends Entity {}
|
||||
|
||||
export interface UpdateProjectOptions {
|
||||
decay?: boolean;
|
||||
[key: string]: any;
|
||||
}
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -13,12 +13,10 @@ let version =
|
||||
// Safely check for process.env in different environments
|
||||
let MEM0_TELEMETRY = true;
|
||||
try {
|
||||
MEM0_TELEMETRY =
|
||||
process?.env?.MEM0_TELEMETRY?.toLowerCase() === "false" ? false : true;
|
||||
MEM0_TELEMETRY = process?.env?.MEM0_TELEMETRY === "false" ? false : true;
|
||||
} catch (error) {}
|
||||
const POSTHOG_API_KEY = "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX";
|
||||
const POSTHOG_HOST = "https://us.i.posthog.com/i/v0/e/";
|
||||
const NOTICE_EVENT_NAME = "mem0.notice_displayed";
|
||||
|
||||
// Default sampling rate for hot-path OSS events. Lifecycle events always fire at 100%.
|
||||
// Override via MEM0_TELEMETRY_SAMPLE_RATE env var. Mirrors mem0/memory/telemetry.py.
|
||||
@@ -37,11 +35,7 @@ const MEM0_TELEMETRY_SAMPLE_RATE: number = ((): number => {
|
||||
})();
|
||||
|
||||
// Events that bypass sampling. Keep in sync with _captureEvent call sites in memory/index.ts.
|
||||
const ALWAYS_SEND_EVENTS: ReadonlySet<string> = new Set([
|
||||
"init",
|
||||
"reset",
|
||||
"notice_displayed",
|
||||
]);
|
||||
const LIFECYCLE_EVENTS: ReadonlySet<string> = new Set(["init", "reset"]);
|
||||
|
||||
class UnifiedTelemetry implements TelemetryClient {
|
||||
private apiKey: string;
|
||||
@@ -97,10 +91,6 @@ class UnifiedTelemetry implements TelemetryClient {
|
||||
|
||||
const telemetry = new UnifiedTelemetry(POSTHOG_API_KEY, POSTHOG_HOST);
|
||||
|
||||
function isTelemetryEnabled(): boolean {
|
||||
return MEM0_TELEMETRY;
|
||||
}
|
||||
|
||||
async function captureClientEvent(
|
||||
eventName: string,
|
||||
instance: TelemetryInstance,
|
||||
@@ -112,8 +102,8 @@ async function captureClientEvent(
|
||||
}
|
||||
|
||||
// >= so that rate=0 drops everything and rate=1 keeps everything (Math.random() ∈ [0, 1)).
|
||||
const alwaysSend = ALWAYS_SEND_EVENTS.has(eventName);
|
||||
if (!alwaysSend && Math.random() >= MEM0_TELEMETRY_SAMPLE_RATE) {
|
||||
const isLifecycle = LIFECYCLE_EVENTS.has(eventName);
|
||||
if (!isLifecycle && Math.random() >= MEM0_TELEMETRY_SAMPLE_RATE) {
|
||||
return;
|
||||
}
|
||||
|
||||
@@ -126,7 +116,7 @@ async function captureClientEvent(
|
||||
client_source: "nodejs",
|
||||
...additionalData,
|
||||
// sample_rate set AFTER the spread so callers can never override it
|
||||
sample_rate: alwaysSend ? 1.0 : MEM0_TELEMETRY_SAMPLE_RATE,
|
||||
sample_rate: isLifecycle ? 1.0 : MEM0_TELEMETRY_SAMPLE_RATE,
|
||||
};
|
||||
|
||||
await telemetry.captureEvent(
|
||||
@@ -136,36 +126,4 @@ async function captureClientEvent(
|
||||
);
|
||||
}
|
||||
|
||||
async function captureNoticeEvent(
|
||||
instance: TelemetryInstance,
|
||||
properties: Record<string, any> = {},
|
||||
) {
|
||||
if (!instance.telemetryId) return;
|
||||
|
||||
const eventData: TelemetryEventData = {
|
||||
function: `${instance.constructor.name}`,
|
||||
method: "notice_displayed",
|
||||
api_host: instance.host,
|
||||
timestamp: new Date().toISOString(),
|
||||
client_version: version,
|
||||
client_source: "nodejs",
|
||||
...properties,
|
||||
sample_rate: 1.0,
|
||||
};
|
||||
|
||||
await telemetry.captureEvent(
|
||||
instance.telemetryId,
|
||||
NOTICE_EVENT_NAME,
|
||||
eventData,
|
||||
);
|
||||
}
|
||||
|
||||
export {
|
||||
POSTHOG_API_KEY,
|
||||
POSTHOG_HOST,
|
||||
NOTICE_EVENT_NAME,
|
||||
telemetry,
|
||||
captureClientEvent,
|
||||
captureNoticeEvent,
|
||||
isTelemetryEnabled,
|
||||
};
|
||||
export { telemetry, captureClientEvent };
|
||||
|
||||
@@ -418,7 +418,6 @@ describe("Memory – LM Studio end-to-end flow", () => {
|
||||
}));
|
||||
jest.doMock("../src/utils/telemetry", () => ({
|
||||
captureClientEvent: jest.fn().mockResolvedValue(undefined),
|
||||
isTelemetryEnabled: jest.fn(() => false),
|
||||
}));
|
||||
|
||||
MemoryClass = require("../src/memory").Memory;
|
||||
|
||||
@@ -290,7 +290,6 @@ describe("Memory – auto-initialization", () => {
|
||||
|
||||
jest.doMock("../src/utils/telemetry", () => ({
|
||||
captureClientEvent: jest.fn().mockResolvedValue(undefined),
|
||||
isTelemetryEnabled: jest.fn(() => false),
|
||||
}));
|
||||
|
||||
MemoryClass = require("../src/memory").Memory;
|
||||
|
||||
@@ -1,387 +0,0 @@
|
||||
/// <reference types="jest" />
|
||||
import * as fs from "fs";
|
||||
import * as os from "os";
|
||||
import * as path from "path";
|
||||
|
||||
jest.setTimeout(15000);
|
||||
|
||||
jest.mock("../src/embeddings/google", () => ({
|
||||
GoogleEmbedder: jest.fn(),
|
||||
}));
|
||||
jest.mock("../src/llms/google", () => ({
|
||||
GoogleLLM: jest.fn(),
|
||||
}));
|
||||
|
||||
const mockEmbedding = new Array(1536).fill(0.1);
|
||||
jest.mock("../src/embeddings/openai", () => ({
|
||||
OpenAIEmbedder: jest.fn().mockImplementation(() => ({
|
||||
embed: jest.fn().mockResolvedValue(mockEmbedding),
|
||||
embedBatch: jest
|
||||
.fn()
|
||||
.mockImplementation((texts: string[]) =>
|
||||
Promise.resolve(texts.map(() => mockEmbedding)),
|
||||
),
|
||||
embeddingDims: 1536,
|
||||
})),
|
||||
}));
|
||||
|
||||
jest.mock("../src/llms/openai", () => ({
|
||||
OpenAILLM: jest.fn().mockImplementation(() => ({
|
||||
generateResponse: jest.fn(),
|
||||
})),
|
||||
}));
|
||||
|
||||
const DECAY_COPY =
|
||||
'Memory decay requires Mem0 Platform. Get a free API key at https://app.mem0.ai?utm_source=oss_sdk&utm_medium=in_product&utm_campaign=decay_stub&utm_content=node_error and use `import MemoryClient from "mem0ai"`.';
|
||||
const PLAIN_DECAY_ERROR =
|
||||
"The decay parameter is not supported by the OSS Memory SDK.";
|
||||
const PROJECT_UPDATE_ERROR =
|
||||
"Project updates are not supported by the OSS Memory SDK.";
|
||||
|
||||
function makeTempMem0Dir(): string {
|
||||
return fs.mkdtempSync(path.join(os.tmpdir(), "mem0-node-decay-feature-"));
|
||||
}
|
||||
|
||||
function decayPayload(overrides: Record<string, any> = {}) {
|
||||
return {
|
||||
notices: {
|
||||
decay_stub: {
|
||||
enabled: true,
|
||||
notice_type: "error",
|
||||
copy: DECAY_COPY,
|
||||
...overrides,
|
||||
},
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
function createFetchMock(options: {
|
||||
variant?: string;
|
||||
payload?: unknown;
|
||||
failFlags?: boolean;
|
||||
flagEnabled?: boolean;
|
||||
}) {
|
||||
const calls: any[] = [];
|
||||
const fetchMock = jest.fn(async (url: string | URL, init?: RequestInit) => {
|
||||
const target = String(url);
|
||||
|
||||
if (target.includes("/flags")) {
|
||||
if (options.failFlags) {
|
||||
throw new Error("flag evaluation failed");
|
||||
}
|
||||
return {
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({
|
||||
flags: {
|
||||
"mem0-oss-notices": {
|
||||
key: "mem0-oss-notices",
|
||||
enabled: options.flagEnabled ?? true,
|
||||
variant: options.variant ?? "displayed",
|
||||
metadata: {
|
||||
payload:
|
||||
options.payload === undefined
|
||||
? JSON.stringify(decayPayload())
|
||||
: options.payload,
|
||||
},
|
||||
},
|
||||
},
|
||||
}),
|
||||
};
|
||||
}
|
||||
|
||||
if (target.includes("/i/v0/e/")) {
|
||||
calls.push(JSON.parse(String(init?.body)));
|
||||
return {
|
||||
ok: true,
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({}),
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
};
|
||||
});
|
||||
|
||||
return { fetchMock, calls };
|
||||
}
|
||||
|
||||
function noticeEvents(calls: any[]) {
|
||||
return calls.filter((call) => call.event === "mem0.notice_displayed");
|
||||
}
|
||||
|
||||
async function createMemory() {
|
||||
const { Memory } = await import("../src/memory");
|
||||
return new Memory({
|
||||
version: "v1.1",
|
||||
embedder: {
|
||||
provider: "openai",
|
||||
config: { apiKey: "test-key", model: "text-embedding-3-small" },
|
||||
},
|
||||
vectorStore: {
|
||||
provider: "memory",
|
||||
config: {
|
||||
collectionName: `test-decay-feature-${Date.now()}-${Math.random()}`,
|
||||
dimension: 1536,
|
||||
dbPath: ":memory:",
|
||||
},
|
||||
},
|
||||
llm: {
|
||||
provider: "openai",
|
||||
config: { apiKey: "test-key", model: "gpt-5-mini" },
|
||||
},
|
||||
historyDbPath: ":memory:",
|
||||
});
|
||||
}
|
||||
|
||||
describe("Node OSS decay feature error notice", () => {
|
||||
let originalMem0Dir: string | undefined;
|
||||
let originalTelemetry: string | undefined;
|
||||
let originalSampleRate: string | undefined;
|
||||
let originalFetch: typeof global.fetch;
|
||||
|
||||
beforeEach(() => {
|
||||
originalMem0Dir = process.env.MEM0_DIR;
|
||||
originalTelemetry = process.env.MEM0_TELEMETRY;
|
||||
originalSampleRate = process.env.MEM0_TELEMETRY_SAMPLE_RATE;
|
||||
originalFetch = global.fetch;
|
||||
|
||||
process.env.MEM0_DIR = makeTempMem0Dir();
|
||||
process.env.MEM0_TELEMETRY = "true";
|
||||
process.env.MEM0_TELEMETRY_SAMPLE_RATE = "1";
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
afterEach(() => {
|
||||
if (originalMem0Dir === undefined) delete process.env.MEM0_DIR;
|
||||
else process.env.MEM0_DIR = originalMem0Dir;
|
||||
|
||||
if (originalTelemetry === undefined) delete process.env.MEM0_TELEMETRY;
|
||||
else process.env.MEM0_TELEMETRY = originalTelemetry;
|
||||
|
||||
if (originalSampleRate === undefined) {
|
||||
delete process.env.MEM0_TELEMETRY_SAMPLE_RATE;
|
||||
} else {
|
||||
process.env.MEM0_TELEMETRY_SAMPLE_RATE = originalSampleRate;
|
||||
}
|
||||
|
||||
global.fetch = originalFetch;
|
||||
jest.restoreAllMocks();
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
it("raises CTA copy for displayed and emits displayed=true", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(memory.updateProject({ decay: true })).rejects.toThrow(
|
||||
DECAY_COPY,
|
||||
);
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "decay_stub",
|
||||
notice_type: "error",
|
||||
flag_key: "mem0-oss-notices",
|
||||
variant: "displayed",
|
||||
displayed: true,
|
||||
payload: DECAY_COPY,
|
||||
notice_config_found: true,
|
||||
sync_type: "async",
|
||||
trigger_function: "update_project",
|
||||
trigger_parameter: "decay",
|
||||
sample_rate: 1,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("raises CTA copy for holdout and emits displayed=true", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "holdout" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(memory.updateProject({ decay: true })).rejects.toThrow(
|
||||
DECAY_COPY,
|
||||
);
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "decay_stub",
|
||||
variant: "holdout",
|
||||
displayed: true,
|
||||
trigger_function: "update_project",
|
||||
trigger_parameter: "decay",
|
||||
}),
|
||||
);
|
||||
expect(notices[0].properties.bypass_reason).toBeUndefined();
|
||||
});
|
||||
|
||||
it("uses plain error for unknown future variants and emits not_displayed", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "silent" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(memory.updateProject({ decay: true })).rejects.toThrow(
|
||||
PLAIN_DECAY_ERROR,
|
||||
);
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "decay_stub",
|
||||
variant: "silent",
|
||||
displayed: false,
|
||||
bypass_reason: "not_displayed",
|
||||
payload: DECAY_COPY,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("uses plain error for disabled payload and emits payload_disabled", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "holdout",
|
||||
payload: JSON.stringify(decayPayload({ enabled: false })),
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(memory.updateProject({ decay: true })).rejects.toThrow(
|
||||
PLAIN_DECAY_ERROR,
|
||||
);
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "decay_stub",
|
||||
displayed: false,
|
||||
bypass_reason: "payload_disabled",
|
||||
disabled_reason: "payload_disabled",
|
||||
notice_config_found: true,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it.each([
|
||||
[
|
||||
"missing config",
|
||||
JSON.stringify({ notices: {} }),
|
||||
"missing_notice_config",
|
||||
],
|
||||
[
|
||||
"missing copy",
|
||||
JSON.stringify(decayPayload({ copy: "" })),
|
||||
"missing_copy",
|
||||
],
|
||||
["malformed payload", "{not-json", "missing_notice_config"],
|
||||
])("uses plain error for %s", async (_label, payload, bypassReason) => {
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
payload,
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(memory.updateProject({ decay: true })).rejects.toThrow(
|
||||
PLAIN_DECAY_ERROR,
|
||||
);
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "decay_stub",
|
||||
displayed: false,
|
||||
bypass_reason: bypassReason,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("uses plain error and emits no event when the blunt flag is disabled", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
flagEnabled: false,
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(memory.updateProject({ decay: true })).rejects.toThrow(
|
||||
PLAIN_DECAY_ERROR,
|
||||
);
|
||||
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
});
|
||||
|
||||
it("uses plain error and emits no event when PostHog fails", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ failFlags: true });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(memory.updateProject({ decay: true })).rejects.toThrow(
|
||||
PLAIN_DECAY_ERROR,
|
||||
);
|
||||
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
});
|
||||
|
||||
it("uses plain error and skips flag evaluation when telemetry is off", async () => {
|
||||
process.env.MEM0_TELEMETRY = "False";
|
||||
jest.resetModules();
|
||||
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(memory.updateProject({ decay: true })).rejects.toThrow(
|
||||
PLAIN_DECAY_ERROR,
|
||||
);
|
||||
|
||||
expect(fetchMock).not.toHaveBeenCalled();
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
});
|
||||
|
||||
it.each([
|
||||
["empty options", {}],
|
||||
["decay false", { decay: false }],
|
||||
["non-decay options", { customInstructions: "Updated" }],
|
||||
])("does not emit notice telemetry for %s", async (_label, options) => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(memory.updateProject(options)).rejects.toThrow(
|
||||
PROJECT_UPDATE_ERROR,
|
||||
);
|
||||
|
||||
expect(
|
||||
fetchMock.mock.calls.filter(([url]) => String(url).includes("/flags")),
|
||||
).toHaveLength(0);
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
});
|
||||
|
||||
it("does not trigger first-run notice or update_project telemetry", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(memory.updateProject({ decay: true })).rejects.toThrow(
|
||||
DECAY_COPY,
|
||||
);
|
||||
|
||||
const eventNames = calls.map((call) => call.event);
|
||||
expect(eventNames).toContain("mem0.notice_displayed");
|
||||
expect(eventNames).not.toContain("mem0.update_project");
|
||||
expect(
|
||||
noticeEvents(calls).some(
|
||||
(call) => call.properties.notice_id === "first_run",
|
||||
),
|
||||
).toBe(false);
|
||||
});
|
||||
});
|
||||
@@ -1,526 +0,0 @@
|
||||
/// <reference types="jest" />
|
||||
import * as fs from "fs";
|
||||
import * as os from "os";
|
||||
import * as path from "path";
|
||||
|
||||
jest.setTimeout(15000);
|
||||
|
||||
jest.mock("../src/embeddings/google", () => ({
|
||||
GoogleEmbedder: jest.fn(),
|
||||
}));
|
||||
jest.mock("../src/llms/google", () => ({
|
||||
GoogleLLM: jest.fn(),
|
||||
}));
|
||||
|
||||
const mockEmbedding = new Array(1536).fill(0.1);
|
||||
jest.mock("../src/embeddings/openai", () => ({
|
||||
OpenAIEmbedder: jest.fn().mockImplementation(() => ({
|
||||
embed: jest.fn().mockResolvedValue(mockEmbedding),
|
||||
embedBatch: jest
|
||||
.fn()
|
||||
.mockImplementation((texts: string[]) =>
|
||||
Promise.resolve(texts.map(() => mockEmbedding)),
|
||||
),
|
||||
embeddingDims: 1536,
|
||||
})),
|
||||
}));
|
||||
|
||||
jest.mock("../src/llms/openai", () => ({
|
||||
OpenAILLM: jest.fn().mockImplementation(() => ({
|
||||
generateResponse: jest.fn(),
|
||||
})),
|
||||
}));
|
||||
|
||||
const DECAY_USAGE_COPY =
|
||||
"Tip: Python fallback copy with memory.project.update(decay=True).";
|
||||
|
||||
function makeTempMem0Dir(): string {
|
||||
return fs.mkdtempSync(path.join(os.tmpdir(), "mem0-node-decay-usage-"));
|
||||
}
|
||||
|
||||
function configPath(): string {
|
||||
return path.join(process.env.MEM0_DIR as string, "config.json");
|
||||
}
|
||||
|
||||
function readConfig(): Record<string, any> {
|
||||
const file = configPath();
|
||||
if (!fs.existsSync(file)) return {};
|
||||
return JSON.parse(fs.readFileSync(file, "utf8"));
|
||||
}
|
||||
|
||||
function writeConfig(config: Record<string, any>) {
|
||||
fs.mkdirSync(path.dirname(configPath()), { recursive: true });
|
||||
fs.writeFileSync(configPath(), JSON.stringify(config, null, 4));
|
||||
}
|
||||
|
||||
function consumeFirstRun() {
|
||||
writeConfig({
|
||||
user_id: "node-decay-usage-test-user",
|
||||
notice_state: {
|
||||
first_run: {
|
||||
consumed: true,
|
||||
trigger_function: "test_setup",
|
||||
variant: "test",
|
||||
},
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
function decayUsagePayload(overrides: Record<string, any> = {}) {
|
||||
return {
|
||||
notices: {
|
||||
decay_usage: {
|
||||
enabled: true,
|
||||
notice_type: "log_line",
|
||||
copy: DECAY_USAGE_COPY,
|
||||
...overrides,
|
||||
},
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
function createFetchMock(options: {
|
||||
variant?: string;
|
||||
payload?: unknown;
|
||||
failFlags?: boolean;
|
||||
flagEnabled?: boolean;
|
||||
}) {
|
||||
const calls: any[] = [];
|
||||
const fetchMock = jest.fn(async (url: string | URL, init?: RequestInit) => {
|
||||
const target = String(url);
|
||||
|
||||
if (target.includes("/flags")) {
|
||||
if (options.failFlags) {
|
||||
throw new Error("flag evaluation failed");
|
||||
}
|
||||
const payload =
|
||||
options.payload === undefined
|
||||
? JSON.stringify(decayUsagePayload())
|
||||
: options.payload;
|
||||
return {
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({
|
||||
flags: {
|
||||
"mem0-oss-notices": {
|
||||
key: "mem0-oss-notices",
|
||||
enabled: options.flagEnabled ?? true,
|
||||
variant: options.variant ?? "displayed",
|
||||
metadata: { payload },
|
||||
},
|
||||
},
|
||||
}),
|
||||
};
|
||||
}
|
||||
|
||||
if (target.includes("/i/v0/e/")) {
|
||||
calls.push(JSON.parse(String(init?.body)));
|
||||
return {
|
||||
ok: true,
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({}),
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
};
|
||||
});
|
||||
|
||||
return { fetchMock, calls };
|
||||
}
|
||||
|
||||
function noticeEvents(calls: any[]) {
|
||||
return calls.filter((call) => call.event === "mem0.notice_displayed");
|
||||
}
|
||||
|
||||
function flagRequestCount(fetchMock: jest.Mock) {
|
||||
return fetchMock.mock.calls.filter(([url]) => String(url).includes("/flags"))
|
||||
.length;
|
||||
}
|
||||
|
||||
async function createMemory() {
|
||||
const { Memory } = await import("../src/memory");
|
||||
return new Memory({
|
||||
version: "v1.1",
|
||||
embedder: {
|
||||
provider: "openai",
|
||||
config: { apiKey: "test-key", model: "text-embedding-3-small" },
|
||||
},
|
||||
vectorStore: {
|
||||
provider: "memory",
|
||||
config: {
|
||||
collectionName: `test-decay-usage-${Date.now()}-${Math.random()}`,
|
||||
dimension: 1536,
|
||||
dbPath: ":memory:",
|
||||
},
|
||||
},
|
||||
llm: {
|
||||
provider: "openai",
|
||||
config: { apiKey: "test-key", model: "gpt-5-mini" },
|
||||
},
|
||||
historyDbPath: ":memory:",
|
||||
});
|
||||
}
|
||||
|
||||
async function addMemories(memory: any, userId: string, count: number) {
|
||||
const ids: string[] = [];
|
||||
for (let index = 0; index < count; index++) {
|
||||
const result = await memory.add(`Decay usage memory ${index}`, {
|
||||
userId,
|
||||
infer: false,
|
||||
});
|
||||
ids.push(result.results[0].id);
|
||||
}
|
||||
return ids;
|
||||
}
|
||||
|
||||
describe("Node OSS decay usage notice", () => {
|
||||
let originalMem0Dir: string | undefined;
|
||||
let originalTelemetry: string | undefined;
|
||||
let originalSampleRate: string | undefined;
|
||||
let originalFetch: typeof global.fetch;
|
||||
let stderrSpy: jest.SpyInstance;
|
||||
|
||||
beforeEach(() => {
|
||||
originalMem0Dir = process.env.MEM0_DIR;
|
||||
originalTelemetry = process.env.MEM0_TELEMETRY;
|
||||
originalSampleRate = process.env.MEM0_TELEMETRY_SAMPLE_RATE;
|
||||
originalFetch = global.fetch;
|
||||
|
||||
process.env.MEM0_DIR = makeTempMem0Dir();
|
||||
process.env.MEM0_TELEMETRY = "true";
|
||||
process.env.MEM0_TELEMETRY_SAMPLE_RATE = "1";
|
||||
stderrSpy = jest
|
||||
.spyOn(process.stderr, "write")
|
||||
.mockImplementation(() => true);
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
afterEach(() => {
|
||||
if (originalMem0Dir === undefined) delete process.env.MEM0_DIR;
|
||||
else process.env.MEM0_DIR = originalMem0Dir;
|
||||
|
||||
if (originalTelemetry === undefined) delete process.env.MEM0_TELEMETRY;
|
||||
else process.env.MEM0_TELEMETRY = originalTelemetry;
|
||||
|
||||
if (originalSampleRate === undefined) {
|
||||
delete process.env.MEM0_TELEMETRY_SAMPLE_RATE;
|
||||
} else {
|
||||
process.env.MEM0_TELEMETRY_SAMPLE_RATE = originalSampleRate;
|
||||
}
|
||||
|
||||
global.fetch = originalFetch;
|
||||
jest.restoreAllMocks();
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
it("does not evaluate or write decay state before the 5th successful delete", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
const ids = await addMemories(memory, "decay-delete-user", 4);
|
||||
|
||||
for (const id of ids) {
|
||||
await memory.delete(id);
|
||||
}
|
||||
|
||||
expect(flagRequestCount(fetchMock)).toBe(0);
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.decay_usage).toBeUndefined();
|
||||
expect(stderrSpy).not.toHaveBeenCalledWith(
|
||||
expect.stringContaining(DECAY_USAGE_COPY),
|
||||
);
|
||||
});
|
||||
|
||||
it("evaluates on the 5th successful delete and records delete_count", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
const ids = await addMemories(memory, "decay-delete-user", 5);
|
||||
|
||||
for (const id of ids) {
|
||||
await memory.delete(id);
|
||||
}
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "decay_usage",
|
||||
notice_type: "log_line",
|
||||
flag_key: "mem0-oss-notices",
|
||||
variant: "displayed",
|
||||
displayed: true,
|
||||
payload: DECAY_USAGE_COPY,
|
||||
notice_config_found: true,
|
||||
sync_type: "async",
|
||||
trigger_function: "delete",
|
||||
trigger_source: "delete_count",
|
||||
trigger_reason: "repeated_deletes",
|
||||
delete_count: 5,
|
||||
sample_rate: 1,
|
||||
}),
|
||||
);
|
||||
expect(stderrSpy.mock.calls.flat().join("")).toContain(DECAY_USAGE_COPY);
|
||||
expect(readConfig().notice_state.decay_usage.events).toHaveLength(1);
|
||||
});
|
||||
|
||||
it("does not count or emit when delete fails", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
jest
|
||||
.spyOn(memory as any, "deleteMemory")
|
||||
.mockRejectedValue(new Error("delete failed"));
|
||||
|
||||
await expect(memory.delete("memory-id")).rejects.toThrow("delete failed");
|
||||
|
||||
expect(flagRequestCount(fetchMock)).toBe(0);
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.decay_usage).toBeUndefined();
|
||||
});
|
||||
|
||||
it("evaluates deleteAll after deleting at least one memory", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addMemories(memory, "decay-delete-all-user", 3);
|
||||
|
||||
await memory.deleteAll({ userId: "decay-delete-all-user" });
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "decay_usage",
|
||||
variant: "displayed",
|
||||
displayed: true,
|
||||
payload: DECAY_USAGE_COPY,
|
||||
trigger_function: "delete_all",
|
||||
trigger_source: "delete_all",
|
||||
trigger_reason: "bulk_delete",
|
||||
deleted_count: 3,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("does not evaluate deleteAll when no memories are deleted", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.deleteAll({ userId: "empty-delete-all-user" });
|
||||
|
||||
expect(flagRequestCount(fetchMock)).toBe(0);
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.decay_usage).toBeUndefined();
|
||||
});
|
||||
|
||||
it("does not evaluate when deleteAll fails", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(memory.deleteAll({} as any)).rejects.toThrow(
|
||||
"At least one filter is required",
|
||||
);
|
||||
|
||||
expect(flagRequestCount(fetchMock)).toBe(0);
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.decay_usage).toBeUndefined();
|
||||
});
|
||||
|
||||
it("is silent for holdout and emits displayed=false", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "holdout" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addMemories(memory, "decay-holdout-user", 3);
|
||||
|
||||
await memory.deleteAll({ userId: "decay-holdout-user" });
|
||||
|
||||
expect(stderrSpy).not.toHaveBeenCalledWith(
|
||||
expect.stringContaining(DECAY_USAGE_COPY),
|
||||
);
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "decay_usage",
|
||||
variant: "holdout",
|
||||
displayed: false,
|
||||
bypass_reason: "holdout",
|
||||
trigger_function: "delete_all",
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it.each([
|
||||
[
|
||||
"disabled payload",
|
||||
JSON.stringify(decayUsagePayload({ enabled: false })),
|
||||
"payload_disabled",
|
||||
"payload_disabled",
|
||||
],
|
||||
[
|
||||
"missing config",
|
||||
JSON.stringify({ notices: {} }),
|
||||
"missing_notice_config",
|
||||
undefined,
|
||||
],
|
||||
[
|
||||
"missing copy",
|
||||
JSON.stringify({
|
||||
notices: {
|
||||
decay_usage: { enabled: true, notice_type: "log_line" },
|
||||
},
|
||||
}),
|
||||
"missing_copy",
|
||||
undefined,
|
||||
],
|
||||
["malformed payload", "{not-json", "missing_notice_config", undefined],
|
||||
])(
|
||||
"stays silent and emits safe bypass for %s",
|
||||
async (_label, payload, bypassReason, disabledReason) => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
payload,
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addMemories(memory, "decay-bypass-user", 3);
|
||||
|
||||
await memory.deleteAll({ userId: "decay-bypass-user" });
|
||||
|
||||
expect(stderrSpy).not.toHaveBeenCalledWith(
|
||||
expect.stringContaining(DECAY_USAGE_COPY),
|
||||
);
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "decay_usage",
|
||||
displayed: false,
|
||||
bypass_reason: bypassReason,
|
||||
...(disabledReason && { disabled_reason: disabledReason }),
|
||||
}),
|
||||
);
|
||||
},
|
||||
);
|
||||
|
||||
it("does not emit or consume cap when the blunt flag is disabled", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
flagEnabled: false,
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addMemories(memory, "decay-flag-disabled-user", 3);
|
||||
|
||||
await memory.deleteAll({ userId: "decay-flag-disabled-user" });
|
||||
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.decay_usage).toBeUndefined();
|
||||
});
|
||||
|
||||
it("does not emit or consume cap when PostHog fails", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ failFlags: true });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addMemories(memory, "decay-posthog-failure-user", 3);
|
||||
|
||||
await memory.deleteAll({ userId: "decay-posthog-failure-user" });
|
||||
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.decay_usage).toBeUndefined();
|
||||
});
|
||||
|
||||
it("skips flag evaluation, event emission, and state writes when telemetry is off", async () => {
|
||||
process.env.MEM0_TELEMETRY = "False";
|
||||
jest.resetModules();
|
||||
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addMemories(memory, "decay-telemetry-off-user", 3);
|
||||
|
||||
await memory.deleteAll({ userId: "decay-telemetry-off-user" });
|
||||
|
||||
expect(fetchMock).not.toHaveBeenCalled();
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.decay_usage).toBeUndefined();
|
||||
});
|
||||
|
||||
it("blocks the 11th evaluated opportunity before flag evaluation", async () => {
|
||||
consumeFirstRun();
|
||||
const now = new Date();
|
||||
writeConfig({
|
||||
...readConfig(),
|
||||
notice_state: {
|
||||
...readConfig().notice_state,
|
||||
first_run: readConfig().notice_state.first_run,
|
||||
decay_usage: {
|
||||
events: Array.from({ length: 10 }, (_, index) => ({
|
||||
evaluated_at: new Date(now.getTime() - index * 1000).toISOString(),
|
||||
variant: "displayed",
|
||||
})),
|
||||
},
|
||||
},
|
||||
});
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addMemories(memory, "decay-cap-user", 1);
|
||||
|
||||
await memory.deleteAll({ userId: "decay-cap-user" });
|
||||
|
||||
expect(flagRequestCount(fetchMock)).toBe(0);
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state.decay_usage.events).toHaveLength(10);
|
||||
});
|
||||
|
||||
it("does not consume first-run on a qualifying decay usage call", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
payload: JSON.stringify({
|
||||
notices: {
|
||||
first_run: {
|
||||
enabled: true,
|
||||
notice_type: "log_line",
|
||||
copy: "First-run should not appear",
|
||||
},
|
||||
decay_usage: {
|
||||
enabled: true,
|
||||
notice_type: "log_line",
|
||||
copy: DECAY_USAGE_COPY,
|
||||
},
|
||||
},
|
||||
}),
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addMemories(memory, "decay-priority-user", 3);
|
||||
const config = readConfig();
|
||||
delete config.notice_state.first_run;
|
||||
writeConfig(config);
|
||||
|
||||
await memory.deleteAll({ userId: "decay-priority-user" });
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties.notice_id).toBe("decay_usage");
|
||||
expect(readConfig().notice_state.first_run).toBeUndefined();
|
||||
expect(readConfig().notice_state.decay_usage.events).toHaveLength(1);
|
||||
});
|
||||
});
|
||||
@@ -1,409 +0,0 @@
|
||||
/// <reference types="jest" />
|
||||
import * as fs from "fs";
|
||||
import * as os from "os";
|
||||
import * as path from "path";
|
||||
|
||||
jest.setTimeout(15000);
|
||||
|
||||
jest.mock("../src/embeddings/google", () => ({
|
||||
GoogleEmbedder: jest.fn(),
|
||||
}));
|
||||
jest.mock("../src/llms/google", () => ({
|
||||
GoogleLLM: jest.fn(),
|
||||
}));
|
||||
|
||||
jest.mock("../src/llms/openai", () => ({
|
||||
OpenAILLM: jest.fn().mockImplementation(() => ({
|
||||
generateResponse: jest.fn().mockResolvedValue(
|
||||
JSON.stringify({
|
||||
memory: [{ id: "0", text: "stored fact", attributed_to: "user" }],
|
||||
}),
|
||||
),
|
||||
})),
|
||||
}));
|
||||
|
||||
const mockEmbedding = new Array(1536).fill(0.1);
|
||||
jest.mock("../src/embeddings/openai", () => ({
|
||||
OpenAIEmbedder: jest.fn().mockImplementation(() => ({
|
||||
embed: jest.fn().mockResolvedValue(mockEmbedding),
|
||||
embedBatch: jest
|
||||
.fn()
|
||||
.mockImplementation((texts: string[]) =>
|
||||
Promise.resolve(texts.map(() => mockEmbedding)),
|
||||
),
|
||||
embeddingDims: 1536,
|
||||
})),
|
||||
}));
|
||||
|
||||
const FIRST_RUN_COPY = "First-run CTA from PostHog";
|
||||
|
||||
function makeTempMem0Dir(): string {
|
||||
return fs.mkdtempSync(path.join(os.tmpdir(), "mem0-node-first-run-"));
|
||||
}
|
||||
|
||||
function firstRunPayload(overrides: Record<string, any> = {}) {
|
||||
return {
|
||||
notices: {
|
||||
first_run: {
|
||||
enabled: true,
|
||||
notice_type: "log_line",
|
||||
copy: FIRST_RUN_COPY,
|
||||
...overrides,
|
||||
},
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
function createFetchMock(options: {
|
||||
variant?: string;
|
||||
payload?: Record<string, any>;
|
||||
failFlags?: boolean;
|
||||
}) {
|
||||
const calls: any[] = [];
|
||||
const fetchMock = jest.fn(async (url: string | URL, init?: RequestInit) => {
|
||||
const target = String(url);
|
||||
|
||||
if (target.includes("/flags")) {
|
||||
if (options.failFlags) {
|
||||
throw new Error("flag evaluation failed");
|
||||
}
|
||||
return {
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({
|
||||
flags: {
|
||||
"mem0-oss-notices": {
|
||||
key: "mem0-oss-notices",
|
||||
enabled: true,
|
||||
variant: options.variant ?? "displayed",
|
||||
metadata: {
|
||||
payload: JSON.stringify(options.payload ?? firstRunPayload()),
|
||||
},
|
||||
},
|
||||
},
|
||||
}),
|
||||
};
|
||||
}
|
||||
|
||||
if (target.includes("/i/v0/e/")) {
|
||||
calls.push(JSON.parse(String(init?.body)));
|
||||
return {
|
||||
ok: true,
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({}),
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
};
|
||||
});
|
||||
|
||||
return { fetchMock, calls };
|
||||
}
|
||||
|
||||
function noticeEvents(calls: any[]) {
|
||||
return calls.filter((call) => call.event === "mem0.notice_displayed");
|
||||
}
|
||||
|
||||
async function createMemory() {
|
||||
const { Memory } = await import("../src/memory");
|
||||
return new Memory({
|
||||
version: "v1.1",
|
||||
embedder: {
|
||||
provider: "openai",
|
||||
config: { apiKey: "test-key", model: "text-embedding-3-small" },
|
||||
},
|
||||
vectorStore: {
|
||||
provider: "memory",
|
||||
config: {
|
||||
collectionName: `test-first-run-${Date.now()}-${Math.random()}`,
|
||||
dimension: 1536,
|
||||
dbPath: ":memory:",
|
||||
},
|
||||
},
|
||||
llm: {
|
||||
provider: "openai",
|
||||
config: { apiKey: "test-key", model: "gpt-5-mini" },
|
||||
},
|
||||
historyDbPath: ":memory:",
|
||||
});
|
||||
}
|
||||
|
||||
describe("Node OSS first-run notice", () => {
|
||||
let originalMem0Dir: string | undefined;
|
||||
let originalTelemetry: string | undefined;
|
||||
let originalSampleRate: string | undefined;
|
||||
let originalFetch: typeof global.fetch;
|
||||
let stderrSpy: jest.SpyInstance;
|
||||
|
||||
beforeEach(() => {
|
||||
originalMem0Dir = process.env.MEM0_DIR;
|
||||
originalTelemetry = process.env.MEM0_TELEMETRY;
|
||||
originalSampleRate = process.env.MEM0_TELEMETRY_SAMPLE_RATE;
|
||||
originalFetch = global.fetch;
|
||||
|
||||
process.env.MEM0_DIR = makeTempMem0Dir();
|
||||
process.env.MEM0_TELEMETRY = "true";
|
||||
process.env.MEM0_TELEMETRY_SAMPLE_RATE = "1";
|
||||
stderrSpy = jest
|
||||
.spyOn(process.stderr, "write")
|
||||
.mockImplementation(() => true);
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
afterEach(() => {
|
||||
if (originalMem0Dir === undefined) delete process.env.MEM0_DIR;
|
||||
else process.env.MEM0_DIR = originalMem0Dir;
|
||||
|
||||
if (originalTelemetry === undefined) delete process.env.MEM0_TELEMETRY;
|
||||
else process.env.MEM0_TELEMETRY = originalTelemetry;
|
||||
|
||||
if (originalSampleRate === undefined) {
|
||||
delete process.env.MEM0_TELEMETRY_SAMPLE_RATE;
|
||||
} else {
|
||||
process.env.MEM0_TELEMETRY_SAMPLE_RATE = originalSampleRate;
|
||||
}
|
||||
|
||||
global.fetch = originalFetch;
|
||||
jest.restoreAllMocks();
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
it("shows the displayed first-run copy once after a successful public call", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.add("Direct storage content", {
|
||||
userId: "first-run-user",
|
||||
infer: false,
|
||||
});
|
||||
await memory.add("Second direct storage content", {
|
||||
userId: "first-run-user",
|
||||
infer: false,
|
||||
});
|
||||
|
||||
const stderrOutput = stderrSpy.mock.calls.flat().join("");
|
||||
expect(stderrOutput.match(new RegExp(FIRST_RUN_COPY, "g"))).toHaveLength(1);
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "first_run",
|
||||
notice_type: "log_line",
|
||||
flag_key: "mem0-oss-notices",
|
||||
variant: "displayed",
|
||||
displayed: true,
|
||||
payload: FIRST_RUN_COPY,
|
||||
notice_config_found: true,
|
||||
sync_type: "async",
|
||||
trigger_function: "add",
|
||||
sample_rate: 1,
|
||||
}),
|
||||
);
|
||||
|
||||
const { __noticeTestHooks } = await import("../src/utils/notices");
|
||||
const config = __noticeTestHooks.loadMem0Config();
|
||||
expect(config.notice_state.first_run).toEqual(
|
||||
expect.objectContaining({
|
||||
consumed: true,
|
||||
trigger_function: "add",
|
||||
variant: "displayed",
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("treats missing enabled as enabled, matching Python payload semantics", async () => {
|
||||
const payload = firstRunPayload();
|
||||
delete (payload.notices.first_run as Record<string, any>).enabled;
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
payload,
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.add("Missing enabled payload content", {
|
||||
userId: "first-run-missing-enabled",
|
||||
infer: false,
|
||||
});
|
||||
|
||||
const stderrOutput = stderrSpy.mock.calls.flat().join("");
|
||||
expect(stderrOutput).toContain(FIRST_RUN_COPY);
|
||||
expect(noticeEvents(calls)[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "first_run",
|
||||
displayed: true,
|
||||
payload: FIRST_RUN_COPY,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("is silent for holdout but still emits and consumes the opportunity", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "holdout" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.add("Holdout content", {
|
||||
userId: "first-run-holdout",
|
||||
infer: false,
|
||||
});
|
||||
|
||||
expect(stderrSpy).not.toHaveBeenCalledWith(
|
||||
expect.stringContaining(FIRST_RUN_COPY),
|
||||
);
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "first_run",
|
||||
variant: "holdout",
|
||||
displayed: false,
|
||||
bypass_reason: "holdout",
|
||||
notice_config_found: true,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("is silent for disabled payloads and records a safe bypass", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
payload: firstRunPayload({ enabled: false }),
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.add("Disabled payload content", {
|
||||
userId: "first-run-disabled",
|
||||
infer: false,
|
||||
});
|
||||
|
||||
const stderrOutput = stderrSpy.mock.calls.flat().join("");
|
||||
expect(stderrOutput).not.toContain(FIRST_RUN_COPY);
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "first_run",
|
||||
displayed: false,
|
||||
bypass_reason: "payload_disabled",
|
||||
disabled_reason: "payload_disabled",
|
||||
notice_config_found: true,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("lets the payload disabled kill switch take precedence over holdout", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "holdout",
|
||||
payload: firstRunPayload({ enabled: false }),
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.add("Disabled holdout payload content", {
|
||||
userId: "first-run-disabled-holdout",
|
||||
infer: false,
|
||||
});
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "first_run",
|
||||
variant: "holdout",
|
||||
displayed: false,
|
||||
bypass_reason: "payload_disabled",
|
||||
disabled_reason: "payload_disabled",
|
||||
notice_config_found: true,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("does not evaluate flags, emit, or write state when telemetry is off", async () => {
|
||||
process.env.MEM0_TELEMETRY = "false";
|
||||
jest.resetModules();
|
||||
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.add("Telemetry off content", {
|
||||
userId: "first-run-off",
|
||||
infer: false,
|
||||
});
|
||||
|
||||
expect(fetchMock).not.toHaveBeenCalled();
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
const { __noticeTestHooks } = await import("../src/utils/notices");
|
||||
expect(__noticeTestHooks.loadMem0Config().notice_state).toBeUndefined();
|
||||
});
|
||||
|
||||
it("does not export internal notice test hooks from the public OSS entrypoint", async () => {
|
||||
const publicOssEntry = await import("../src");
|
||||
|
||||
expect(publicOssEntry).not.toHaveProperty("__noticeTestHooks");
|
||||
});
|
||||
|
||||
it("treats uppercase False as telemetry off", async () => {
|
||||
process.env.MEM0_TELEMETRY = "False";
|
||||
jest.resetModules();
|
||||
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.add("Uppercase telemetry off content", {
|
||||
userId: "first-run-off-uppercase",
|
||||
infer: false,
|
||||
});
|
||||
|
||||
expect(fetchMock).not.toHaveBeenCalled();
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
const { __noticeTestHooks } = await import("../src/utils/notices");
|
||||
expect(__noticeTestHooks.loadMem0Config().notice_state).toBeUndefined();
|
||||
});
|
||||
|
||||
it("does not run after a failed public call", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(memory.add("Missing owner", {} as any)).rejects.toThrow(
|
||||
"One of the filters: userId, agentId or runId is required!",
|
||||
);
|
||||
|
||||
expect(
|
||||
fetchMock.mock.calls.filter(([url]) => String(url).includes("/flags")),
|
||||
).toHaveLength(0);
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
});
|
||||
|
||||
it("does not break the successful operation when flag evaluation fails", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ failFlags: true });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
const result = await memory.add("Flag failure content", {
|
||||
userId: "first-run-failure",
|
||||
infer: false,
|
||||
});
|
||||
|
||||
expect(result.results).toHaveLength(1);
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
});
|
||||
|
||||
it("can trigger from get() when get is the first successful public call", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(memory.get("missing-memory-id")).resolves.toBeNull();
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties.trigger_function).toBe("get");
|
||||
});
|
||||
});
|
||||
@@ -1,227 +0,0 @@
|
||||
/// <reference types="jest" />
|
||||
import * as fs from "fs";
|
||||
import * as os from "os";
|
||||
import * as path from "path";
|
||||
import type { TelemetryInstance } from "../src/utils/telemetry.types";
|
||||
|
||||
function makeTempMem0Dir(): string {
|
||||
return fs.mkdtempSync(path.join(os.tmpdir(), "mem0-node-notices-"));
|
||||
}
|
||||
|
||||
function makeInstance(
|
||||
overrides: Partial<TelemetryInstance> = {},
|
||||
): TelemetryInstance {
|
||||
return {
|
||||
telemetryId: "notice-test-user",
|
||||
constructor: { name: "Memory" },
|
||||
host: "https://test.example.com",
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
describe("Node OSS notice foundation", () => {
|
||||
let originalMem0Dir: string | undefined;
|
||||
let originalTelemetry: string | undefined;
|
||||
let originalFetch: typeof global.fetch;
|
||||
|
||||
beforeEach(() => {
|
||||
originalMem0Dir = process.env.MEM0_DIR;
|
||||
originalTelemetry = process.env.MEM0_TELEMETRY;
|
||||
originalFetch = global.fetch;
|
||||
process.env.MEM0_DIR = makeTempMem0Dir();
|
||||
process.env.MEM0_TELEMETRY = "true";
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
afterEach(() => {
|
||||
if (originalMem0Dir === undefined) delete process.env.MEM0_DIR;
|
||||
else process.env.MEM0_DIR = originalMem0Dir;
|
||||
|
||||
if (originalTelemetry === undefined) delete process.env.MEM0_TELEMETRY;
|
||||
else process.env.MEM0_TELEMETRY = originalTelemetry;
|
||||
|
||||
global.fetch = originalFetch;
|
||||
jest.restoreAllMocks();
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
it("writes notice state into isolated MEM0_DIR config and preserves user_id", async () => {
|
||||
const notices = await import("../src/utils/notices");
|
||||
const configPath = notices.getMem0ConfigPath();
|
||||
|
||||
fs.mkdirSync(path.dirname(configPath), { recursive: true });
|
||||
fs.writeFileSync(configPath, JSON.stringify({ user_id: "existing-user" }));
|
||||
|
||||
expect(
|
||||
notices.recordNoticeOpportunity("foundation_notice", {
|
||||
variant: "displayed",
|
||||
}),
|
||||
).toBe(true);
|
||||
|
||||
const config = JSON.parse(fs.readFileSync(configPath, "utf8"));
|
||||
expect(config.user_id).toBe("existing-user");
|
||||
expect(config.notice_state.foundation_notice.events).toHaveLength(1);
|
||||
});
|
||||
|
||||
it("writes config through a temp-file path and leaves no temp file behind", async () => {
|
||||
const notices = await import("../src/utils/notices");
|
||||
|
||||
expect(notices.writeMem0ConfigAtomic({ user_id: "atomic-user" })).toBe(
|
||||
true,
|
||||
);
|
||||
|
||||
const configDir = path.dirname(notices.getMem0ConfigPath());
|
||||
const tempFiles = fs
|
||||
.readdirSync(configDir)
|
||||
.filter((name) => name.endsWith(".tmp"));
|
||||
expect(tempFiles).toHaveLength(0);
|
||||
expect(notices.loadMem0Config().user_id).toBe("atomic-user");
|
||||
});
|
||||
|
||||
it("allows 10 evaluated opportunities in a rolling window and blocks the 11th", async () => {
|
||||
const notices = await import("../src/utils/notices");
|
||||
const now = new Date("2026-06-11T12:00:00.000Z");
|
||||
let state: Record<string, any> = {};
|
||||
|
||||
for (let i = 0; i < 10; i++) {
|
||||
const nextState = notices.appendNoticeCapEvent(
|
||||
state,
|
||||
{ variant: "displayed", index: i },
|
||||
{ now: new Date(now.getTime() + i) },
|
||||
);
|
||||
expect(nextState).not.toBeNull();
|
||||
state = nextState!;
|
||||
}
|
||||
|
||||
expect(notices.hasNoticeCapRoom(state, { now })).toBe(false);
|
||||
expect(
|
||||
notices.appendNoticeCapEvent(state, { variant: "displayed" }, { now }),
|
||||
).toBeNull();
|
||||
});
|
||||
|
||||
it("drops old cap events outside the rolling window", async () => {
|
||||
const notices = await import("../src/utils/notices");
|
||||
const oldEvent = {
|
||||
evaluated_at: "2026-06-01T00:00:00.000Z",
|
||||
variant: "displayed",
|
||||
};
|
||||
const now = new Date("2026-06-11T12:00:00.000Z");
|
||||
|
||||
expect(notices.hasNoticeCapRoom({ events: [oldEvent] }, { now })).toBe(
|
||||
true,
|
||||
);
|
||||
const nextState = notices.appendNoticeCapEvent(
|
||||
{ events: [oldEvent] },
|
||||
{ variant: "displayed" },
|
||||
{ now },
|
||||
);
|
||||
expect(nextState?.events).toHaveLength(1);
|
||||
});
|
||||
|
||||
it("does not evaluate flags or write notice state when telemetry is off", async () => {
|
||||
process.env.MEM0_TELEMETRY = "false";
|
||||
jest.resetModules();
|
||||
|
||||
const fetchMock = jest.fn();
|
||||
global.fetch = fetchMock as any;
|
||||
const notices = await import("../src/utils/notices");
|
||||
|
||||
await expect(
|
||||
notices.evaluateNoticeFlag("notice-test-user", { fetchImpl: fetchMock }),
|
||||
).resolves.toBeNull();
|
||||
expect(fetchMock).not.toHaveBeenCalled();
|
||||
expect(
|
||||
notices.recordNoticeOpportunity("foundation_notice", {
|
||||
variant: "displayed",
|
||||
}),
|
||||
).toBe(false);
|
||||
expect(fs.existsSync(notices.getMem0ConfigPath())).toBe(false);
|
||||
});
|
||||
|
||||
it("returns null when PostHog flag evaluation fails", async () => {
|
||||
const notices = await import("../src/utils/notices");
|
||||
const fetchMock = jest.fn().mockRejectedValue(new Error("network down"));
|
||||
|
||||
await expect(
|
||||
notices.evaluateNoticeFlag("notice-test-user", { fetchImpl: fetchMock }),
|
||||
).resolves.toBeNull();
|
||||
});
|
||||
|
||||
it("returns null when PostHog flag evaluation times out", async () => {
|
||||
const notices = await import("../src/utils/notices");
|
||||
const fetchMock = jest.fn(
|
||||
(_url: string | URL | Request, init?: RequestInit) =>
|
||||
new Promise((_resolve, reject) => {
|
||||
init?.signal?.addEventListener("abort", () => {
|
||||
reject(new Error("aborted"));
|
||||
});
|
||||
}),
|
||||
);
|
||||
|
||||
await expect(
|
||||
notices.evaluateNoticeFlag("notice-test-user", {
|
||||
fetchImpl: fetchMock as any,
|
||||
timeoutMs: 1,
|
||||
}),
|
||||
).resolves.toBeNull();
|
||||
});
|
||||
|
||||
it("parses displayed variant and JSON payload from PostHog flags response", async () => {
|
||||
const notices = await import("../src/utils/notices");
|
||||
const payload = {
|
||||
notices: {
|
||||
foundation_notice: {
|
||||
enabled: true,
|
||||
notice_type: "log_line",
|
||||
copy: "Foundation notice",
|
||||
},
|
||||
},
|
||||
};
|
||||
const fetchMock = jest.fn().mockResolvedValue({
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({
|
||||
flags: {
|
||||
"mem0-oss-notices": {
|
||||
key: "mem0-oss-notices",
|
||||
enabled: true,
|
||||
variant: "displayed",
|
||||
metadata: { payload: JSON.stringify(payload) },
|
||||
},
|
||||
},
|
||||
}),
|
||||
});
|
||||
|
||||
const result = await notices.evaluateNoticeFlag("notice-test-user", {
|
||||
fetchImpl: fetchMock,
|
||||
});
|
||||
expect(result?.variant).toBe("displayed");
|
||||
const parsed = notices.getNoticeConfigFromPayload(
|
||||
result?.payload,
|
||||
"foundation_notice",
|
||||
);
|
||||
expect(parsed.found).toBe(true);
|
||||
expect(parsed.config?.copy).toBe("Foundation notice");
|
||||
});
|
||||
|
||||
it("emits mem0.notice_displayed with sample_rate=1", async () => {
|
||||
const fetchMock = jest.fn().mockResolvedValue({
|
||||
ok: true,
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const notices = await import("../src/utils/notices");
|
||||
|
||||
await notices.emitNoticeDisplayed(makeInstance(), {
|
||||
notice_id: "foundation_notice",
|
||||
notice_type: "log_line",
|
||||
flag_key: "mem0-oss-notices",
|
||||
variant: "displayed",
|
||||
displayed: true,
|
||||
});
|
||||
|
||||
const body = JSON.parse(fetchMock.mock.calls[0][1].body);
|
||||
expect(body.event).toBe("mem0.notice_displayed");
|
||||
expect(body.properties.sample_rate).toBe(1);
|
||||
expect(body.properties.notice_id).toBe("foundation_notice");
|
||||
});
|
||||
});
|
||||
@@ -1,492 +0,0 @@
|
||||
/// <reference types="jest" />
|
||||
import * as fs from "fs";
|
||||
import * as os from "os";
|
||||
import * as path from "path";
|
||||
|
||||
jest.setTimeout(15000);
|
||||
|
||||
jest.mock("../src/embeddings/google", () => ({
|
||||
GoogleEmbedder: jest.fn(),
|
||||
}));
|
||||
jest.mock("../src/llms/google", () => ({
|
||||
GoogleLLM: jest.fn(),
|
||||
}));
|
||||
|
||||
const mockEmbedding = new Array(1536).fill(0.1);
|
||||
jest.mock("../src/embeddings/openai", () => ({
|
||||
OpenAIEmbedder: jest.fn().mockImplementation(() => ({
|
||||
embed: jest.fn().mockResolvedValue(mockEmbedding),
|
||||
embedBatch: jest
|
||||
.fn()
|
||||
.mockImplementation((texts: string[]) =>
|
||||
Promise.resolve(texts.map(() => mockEmbedding)),
|
||||
),
|
||||
embeddingDims: 1536,
|
||||
})),
|
||||
}));
|
||||
|
||||
jest.mock("../src/llms/openai", () => ({
|
||||
OpenAILLM: jest.fn().mockImplementation(() => ({
|
||||
generateResponse: jest.fn(),
|
||||
})),
|
||||
}));
|
||||
|
||||
const PERFORMANCE_COPY =
|
||||
"Mem0 Platform is optimized for this type of workload. Its retrieval benchmarked at ~0.8-1.09s p50 across LoCoMo, LongMemEval, and BEAM 1M/10M workloads; you can use it for free by getting an API key at: https://app.mem0.ai";
|
||||
|
||||
function makeTempMem0Dir(): string {
|
||||
return fs.mkdtempSync(path.join(os.tmpdir(), "mem0-node-performance-"));
|
||||
}
|
||||
|
||||
function configPath(): string {
|
||||
return path.join(process.env.MEM0_DIR as string, "config.json");
|
||||
}
|
||||
|
||||
function readConfig(): Record<string, any> {
|
||||
const file = configPath();
|
||||
if (!fs.existsSync(file)) return {};
|
||||
return JSON.parse(fs.readFileSync(file, "utf8"));
|
||||
}
|
||||
|
||||
function writeConfig(config: Record<string, any>) {
|
||||
fs.mkdirSync(path.dirname(configPath()), { recursive: true });
|
||||
fs.writeFileSync(configPath(), JSON.stringify(config, null, 4));
|
||||
}
|
||||
|
||||
function consumeFirstRun() {
|
||||
writeConfig({
|
||||
user_id: "node-performance-test-user",
|
||||
notice_state: {
|
||||
first_run: {
|
||||
consumed: true,
|
||||
trigger_function: "test_setup",
|
||||
variant: "test",
|
||||
},
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
function performancePayload(overrides: Record<string, any> = {}) {
|
||||
return {
|
||||
notices: {
|
||||
performance_slow_query: {
|
||||
enabled: true,
|
||||
notice_type: "log_line",
|
||||
copy: PERFORMANCE_COPY,
|
||||
...overrides,
|
||||
},
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
function createFetchMock(options: {
|
||||
variant?: string;
|
||||
payload?: unknown;
|
||||
failFlags?: boolean;
|
||||
flagEnabled?: boolean;
|
||||
}) {
|
||||
const calls: any[] = [];
|
||||
const fetchMock = jest.fn(async (url: string | URL, init?: RequestInit) => {
|
||||
const target = String(url);
|
||||
|
||||
if (target.includes("/flags")) {
|
||||
if (options.failFlags) {
|
||||
throw new Error("flag evaluation failed");
|
||||
}
|
||||
const payload =
|
||||
options.payload === undefined
|
||||
? JSON.stringify(performancePayload())
|
||||
: options.payload;
|
||||
return {
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({
|
||||
flags: {
|
||||
"mem0-oss-notices": {
|
||||
key: "mem0-oss-notices",
|
||||
enabled: options.flagEnabled ?? true,
|
||||
variant: options.variant ?? "displayed",
|
||||
metadata: { payload },
|
||||
},
|
||||
},
|
||||
}),
|
||||
};
|
||||
}
|
||||
|
||||
if (target.includes("/i/v0/e/")) {
|
||||
calls.push(JSON.parse(String(init?.body)));
|
||||
return {
|
||||
ok: true,
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({}),
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
};
|
||||
});
|
||||
|
||||
return { fetchMock, calls };
|
||||
}
|
||||
|
||||
function noticeEvents(calls: any[]) {
|
||||
return calls.filter((call) => call.event === "mem0.notice_displayed");
|
||||
}
|
||||
|
||||
function performanceEvents(calls: any[]) {
|
||||
return noticeEvents(calls).filter(
|
||||
(call) => call.properties.notice_id === "performance_slow_query",
|
||||
);
|
||||
}
|
||||
|
||||
function flagRequestCount(fetchMock: jest.Mock) {
|
||||
return fetchMock.mock.calls.filter(([url]) => String(url).includes("/flags"))
|
||||
.length;
|
||||
}
|
||||
|
||||
function mockSearchElapsed(elapsedMs: number) {
|
||||
let now = 1000;
|
||||
jest.spyOn(Date, "now").mockImplementation(() => {
|
||||
const current = now;
|
||||
now += elapsedMs;
|
||||
return current;
|
||||
});
|
||||
}
|
||||
|
||||
async function createMemory() {
|
||||
const { Memory } = await import("../src/memory");
|
||||
return new Memory({
|
||||
version: "v1.1",
|
||||
embedder: {
|
||||
provider: "openai",
|
||||
config: { apiKey: "test-key", model: "text-embedding-3-small" },
|
||||
},
|
||||
vectorStore: {
|
||||
provider: "memory",
|
||||
config: {
|
||||
collectionName: `test-performance-${Date.now()}-${Math.random()}`,
|
||||
dimension: 1536,
|
||||
dbPath: ":memory:",
|
||||
},
|
||||
},
|
||||
llm: {
|
||||
provider: "openai",
|
||||
config: { apiKey: "test-key", model: "gpt-5-mini" },
|
||||
},
|
||||
historyDbPath: ":memory:",
|
||||
});
|
||||
}
|
||||
|
||||
async function addSeed(memory: any, userId = "performance-user") {
|
||||
await memory.add("The user's favorite drink is green tea.", {
|
||||
userId,
|
||||
infer: false,
|
||||
});
|
||||
}
|
||||
|
||||
describe("Node OSS performance slow query notice", () => {
|
||||
let originalMem0Dir: string | undefined;
|
||||
let originalTelemetry: string | undefined;
|
||||
let originalSampleRate: string | undefined;
|
||||
let originalFetch: typeof global.fetch;
|
||||
let stderrSpy: jest.SpyInstance;
|
||||
|
||||
beforeEach(() => {
|
||||
originalMem0Dir = process.env.MEM0_DIR;
|
||||
originalTelemetry = process.env.MEM0_TELEMETRY;
|
||||
originalSampleRate = process.env.MEM0_TELEMETRY_SAMPLE_RATE;
|
||||
originalFetch = global.fetch;
|
||||
|
||||
process.env.MEM0_DIR = makeTempMem0Dir();
|
||||
process.env.MEM0_TELEMETRY = "true";
|
||||
process.env.MEM0_TELEMETRY_SAMPLE_RATE = "1";
|
||||
stderrSpy = jest
|
||||
.spyOn(process.stderr, "write")
|
||||
.mockImplementation(() => true);
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
afterEach(() => {
|
||||
if (originalMem0Dir === undefined) delete process.env.MEM0_DIR;
|
||||
else process.env.MEM0_DIR = originalMem0Dir;
|
||||
|
||||
if (originalTelemetry === undefined) delete process.env.MEM0_TELEMETRY;
|
||||
else process.env.MEM0_TELEMETRY = originalTelemetry;
|
||||
|
||||
if (originalSampleRate === undefined) {
|
||||
delete process.env.MEM0_TELEMETRY_SAMPLE_RATE;
|
||||
} else {
|
||||
process.env.MEM0_TELEMETRY_SAMPLE_RATE = originalSampleRate;
|
||||
}
|
||||
|
||||
global.fetch = originalFetch;
|
||||
jest.restoreAllMocks();
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
it("displays and emits safe fields after a slow successful search", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addSeed(memory);
|
||||
stderrSpy.mockClear();
|
||||
mockSearchElapsed(2345);
|
||||
|
||||
const result = await memory.search("favorite drink private text", {
|
||||
filters: { user_id: "performance-user" },
|
||||
topK: 3,
|
||||
});
|
||||
|
||||
expect(result.results.length).toBeGreaterThan(0);
|
||||
expect(stderrSpy).toHaveBeenCalledWith(`${PERFORMANCE_COPY}\n`);
|
||||
const event = performanceEvents(calls)[0];
|
||||
expect(event.properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "performance_slow_query",
|
||||
notice_type: "log_line",
|
||||
flag_key: "mem0-oss-notices",
|
||||
variant: "displayed",
|
||||
displayed: true,
|
||||
payload: PERFORMANCE_COPY,
|
||||
notice_config_found: true,
|
||||
sync_type: "async",
|
||||
trigger_function: "search",
|
||||
trigger_reason: "slow_query",
|
||||
elapsed_ms: 2345,
|
||||
threshold_ms: 2000,
|
||||
top_k: 3,
|
||||
result_count: result.results.length,
|
||||
sample_rate: 1,
|
||||
}),
|
||||
);
|
||||
const serialized = JSON.stringify(event.properties);
|
||||
expect(serialized).not.toContain("private text");
|
||||
expect(serialized).not.toContain("performance-user");
|
||||
});
|
||||
|
||||
it("is silent for holdout and emits displayed=false", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "holdout" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addSeed(memory);
|
||||
stderrSpy.mockClear();
|
||||
mockSearchElapsed(2345);
|
||||
|
||||
await memory.search("favorite drink", {
|
||||
filters: { user_id: "performance-user" },
|
||||
topK: 3,
|
||||
});
|
||||
|
||||
expect(stderrSpy).not.toHaveBeenCalled();
|
||||
expect(performanceEvents(calls)[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "performance_slow_query",
|
||||
variant: "holdout",
|
||||
displayed: false,
|
||||
bypass_reason: "holdout",
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it.each([
|
||||
[
|
||||
"disabled payload",
|
||||
JSON.stringify(performancePayload({ enabled: false })),
|
||||
"payload_disabled",
|
||||
],
|
||||
[
|
||||
"missing config",
|
||||
JSON.stringify({ notices: {} }),
|
||||
"missing_notice_config",
|
||||
],
|
||||
[
|
||||
"missing copy",
|
||||
JSON.stringify(performancePayload({ copy: "" })),
|
||||
"missing_copy",
|
||||
],
|
||||
["malformed payload", "{not-json", "missing_notice_config"],
|
||||
])("is silent and safe for %s", async (_label, payload, bypassReason) => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
payload,
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addSeed(memory);
|
||||
stderrSpy.mockClear();
|
||||
mockSearchElapsed(2345);
|
||||
|
||||
await memory.search("favorite drink", {
|
||||
filters: { user_id: "performance-user" },
|
||||
topK: 3,
|
||||
});
|
||||
|
||||
expect(stderrSpy).not.toHaveBeenCalled();
|
||||
expect(performanceEvents(calls)[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
displayed: false,
|
||||
bypass_reason: bypassReason,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("does not evaluate fast searches", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addSeed(memory);
|
||||
mockSearchElapsed(100);
|
||||
|
||||
await memory.search("favorite drink", {
|
||||
filters: { user_id: "performance-user" },
|
||||
topK: 3,
|
||||
});
|
||||
|
||||
expect(flagRequestCount(fetchMock)).toBe(0);
|
||||
expect(performanceEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.performance_slow_query).toBeUndefined();
|
||||
});
|
||||
|
||||
it("does not evaluate failed searches", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
mockSearchElapsed(2345);
|
||||
|
||||
await expect(
|
||||
memory.search("private slow query", {
|
||||
filters: {},
|
||||
topK: 3,
|
||||
}),
|
||||
).rejects.toThrow("filters must contain");
|
||||
|
||||
expect(flagRequestCount(fetchMock)).toBe(0);
|
||||
expect(performanceEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.performance_slow_query).toBeUndefined();
|
||||
});
|
||||
|
||||
it("does not consume cap or emit when the blunt flag is disabled", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
flagEnabled: false,
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addSeed(memory);
|
||||
mockSearchElapsed(2345);
|
||||
|
||||
await memory.search("favorite drink", {
|
||||
filters: { user_id: "performance-user" },
|
||||
topK: 3,
|
||||
});
|
||||
|
||||
expect(performanceEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.performance_slow_query).toBeUndefined();
|
||||
});
|
||||
|
||||
it("does not consume cap or emit when PostHog fails", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ failFlags: true });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addSeed(memory);
|
||||
mockSearchElapsed(2345);
|
||||
|
||||
await memory.search("favorite drink", {
|
||||
filters: { user_id: "performance-user" },
|
||||
topK: 3,
|
||||
});
|
||||
|
||||
expect(performanceEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.performance_slow_query).toBeUndefined();
|
||||
});
|
||||
|
||||
it("does nothing when telemetry is off", async () => {
|
||||
process.env.MEM0_TELEMETRY = "false";
|
||||
jest.resetModules();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addSeed(memory);
|
||||
mockSearchElapsed(2345);
|
||||
|
||||
await memory.search("favorite drink", {
|
||||
filters: { user_id: "performance-user" },
|
||||
topK: 3,
|
||||
});
|
||||
|
||||
expect(fetchMock).not.toHaveBeenCalled();
|
||||
expect(performanceEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state).toBeUndefined();
|
||||
});
|
||||
|
||||
it("caps evaluated opportunities at 10 per rolling week", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addSeed(memory);
|
||||
stderrSpy.mockClear();
|
||||
mockSearchElapsed(2345);
|
||||
|
||||
for (let index = 1; index <= 11; index++) {
|
||||
await memory.search(`favorite drink ${index}`, {
|
||||
filters: { user_id: "performance-user" },
|
||||
topK: 3,
|
||||
});
|
||||
}
|
||||
|
||||
expect(performanceEvents(calls)).toHaveLength(10);
|
||||
expect(flagRequestCount(fetchMock)).toBe(10);
|
||||
expect(stderrSpy).toHaveBeenCalledTimes(10);
|
||||
expect(
|
||||
readConfig().notice_state.performance_slow_query.events,
|
||||
).toHaveLength(10);
|
||||
});
|
||||
|
||||
it("lets temporal usage and scale threshold take priority", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addSeed(memory);
|
||||
mockSearchElapsed(2345);
|
||||
|
||||
await memory.search("what happened last week?", {
|
||||
filters: { user_id: "performance-user" },
|
||||
topK: 3,
|
||||
});
|
||||
await memory.search("favorite drink", {
|
||||
filters: { user_id: "performance-user" },
|
||||
topK: 50,
|
||||
});
|
||||
|
||||
expect(performanceEvents(calls)).toHaveLength(0);
|
||||
const noticeIds = noticeEvents(calls).map(
|
||||
(call) => call.properties.notice_id,
|
||||
);
|
||||
expect(noticeIds).toEqual(["temporal_usage", "scale_threshold"]);
|
||||
});
|
||||
|
||||
it("does not consume first-run on the same qualifying performance call", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
mockSearchElapsed(2345);
|
||||
|
||||
await memory.search("favorite drink", {
|
||||
filters: { user_id: "performance-user" },
|
||||
topK: 3,
|
||||
});
|
||||
|
||||
expect(performanceEvents(calls)).toHaveLength(1);
|
||||
expect(readConfig().notice_state.first_run).toBeUndefined();
|
||||
});
|
||||
});
|
||||
@@ -1,463 +0,0 @@
|
||||
/// <reference types="jest" />
|
||||
import * as fs from "fs";
|
||||
import * as os from "os";
|
||||
import * as path from "path";
|
||||
|
||||
jest.setTimeout(15000);
|
||||
|
||||
jest.mock("../src/embeddings/google", () => ({
|
||||
GoogleEmbedder: jest.fn(),
|
||||
}));
|
||||
jest.mock("../src/llms/google", () => ({
|
||||
GoogleLLM: jest.fn(),
|
||||
}));
|
||||
|
||||
const mockEmbedding = new Array(1536).fill(0.1);
|
||||
jest.mock("../src/embeddings/openai", () => ({
|
||||
OpenAIEmbedder: jest.fn().mockImplementation(() => ({
|
||||
embed: jest.fn().mockResolvedValue(mockEmbedding),
|
||||
embedBatch: jest
|
||||
.fn()
|
||||
.mockImplementation((texts: string[]) =>
|
||||
Promise.resolve(texts.map(() => mockEmbedding)),
|
||||
),
|
||||
embeddingDims: 1536,
|
||||
})),
|
||||
}));
|
||||
|
||||
jest.mock("../src/llms/openai", () => ({
|
||||
OpenAILLM: jest.fn().mockImplementation(() => ({
|
||||
generateResponse: jest.fn(),
|
||||
})),
|
||||
}));
|
||||
|
||||
const SCALE_TOP_K_COPY = "Scale top {top_k}";
|
||||
const SCALE_MEMORY_COUNT_COPY = "Scale count {memory_count}";
|
||||
|
||||
function makeTempMem0Dir(): string {
|
||||
return fs.mkdtempSync(path.join(os.tmpdir(), "mem0-node-scale-"));
|
||||
}
|
||||
|
||||
function configPath(): string {
|
||||
return path.join(process.env.MEM0_DIR as string, "config.json");
|
||||
}
|
||||
|
||||
function readConfig(): Record<string, any> {
|
||||
const file = configPath();
|
||||
if (!fs.existsSync(file)) return {};
|
||||
return JSON.parse(fs.readFileSync(file, "utf8"));
|
||||
}
|
||||
|
||||
function writeConfig(config: Record<string, any>) {
|
||||
fs.mkdirSync(path.dirname(configPath()), { recursive: true });
|
||||
fs.writeFileSync(configPath(), JSON.stringify(config, null, 4));
|
||||
}
|
||||
|
||||
function consumeFirstRun(scaleState: Record<string, any> = {}) {
|
||||
writeConfig({
|
||||
user_id: "node-scale-test-user",
|
||||
notice_state: {
|
||||
first_run: {
|
||||
consumed: true,
|
||||
trigger_function: "test_setup",
|
||||
variant: "test",
|
||||
},
|
||||
...(Object.keys(scaleState).length > 0 && {
|
||||
scale_threshold: scaleState,
|
||||
}),
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
function scalePayload(overrides: Record<string, any> = {}) {
|
||||
return {
|
||||
notices: {
|
||||
scale_threshold: {
|
||||
enabled: true,
|
||||
notice_type: "log_line",
|
||||
copies: {
|
||||
top_k: SCALE_TOP_K_COPY,
|
||||
memory_count: SCALE_MEMORY_COUNT_COPY,
|
||||
},
|
||||
...overrides,
|
||||
},
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
function createFetchMock(options: {
|
||||
variant?: string;
|
||||
payload?: unknown;
|
||||
failFlags?: boolean;
|
||||
flagEnabled?: boolean;
|
||||
}) {
|
||||
const calls: any[] = [];
|
||||
const fetchMock = jest.fn(async (url: string | URL, init?: RequestInit) => {
|
||||
const target = String(url);
|
||||
|
||||
if (target.includes("/flags")) {
|
||||
if (options.failFlags) {
|
||||
throw new Error("flag evaluation failed");
|
||||
}
|
||||
const payload =
|
||||
options.payload === undefined
|
||||
? JSON.stringify(scalePayload())
|
||||
: options.payload;
|
||||
return {
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({
|
||||
flags: {
|
||||
"mem0-oss-notices": {
|
||||
key: "mem0-oss-notices",
|
||||
enabled: options.flagEnabled ?? true,
|
||||
variant: options.variant ?? "displayed",
|
||||
metadata: { payload },
|
||||
},
|
||||
},
|
||||
}),
|
||||
};
|
||||
}
|
||||
|
||||
if (target.includes("/i/v0/e/")) {
|
||||
calls.push(JSON.parse(String(init?.body)));
|
||||
return {
|
||||
ok: true,
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({}),
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
};
|
||||
});
|
||||
|
||||
return { fetchMock, calls };
|
||||
}
|
||||
|
||||
function noticeEvents(calls: any[]) {
|
||||
return calls.filter((call) => call.event === "mem0.notice_displayed");
|
||||
}
|
||||
|
||||
function scaleEvents(calls: any[]) {
|
||||
return noticeEvents(calls).filter(
|
||||
(call) => call.properties.notice_id === "scale_threshold",
|
||||
);
|
||||
}
|
||||
|
||||
function flagRequestCount(fetchMock: jest.Mock) {
|
||||
return fetchMock.mock.calls.filter(([url]) => String(url).includes("/flags"))
|
||||
.length;
|
||||
}
|
||||
|
||||
async function createMemory() {
|
||||
const { Memory } = await import("../src/memory");
|
||||
return new Memory({
|
||||
version: "v1.1",
|
||||
embedder: {
|
||||
provider: "openai",
|
||||
config: { apiKey: "test-key", model: "text-embedding-3-small" },
|
||||
},
|
||||
vectorStore: {
|
||||
provider: "memory",
|
||||
config: {
|
||||
collectionName: `test-scale-${Date.now()}-${Math.random()}`,
|
||||
dimension: 1536,
|
||||
dbPath: ":memory:",
|
||||
},
|
||||
},
|
||||
llm: {
|
||||
provider: "openai",
|
||||
config: { apiKey: "test-key", model: "gpt-5-mini" },
|
||||
},
|
||||
historyDbPath: ":memory:",
|
||||
});
|
||||
}
|
||||
|
||||
async function addSeed(memory: any, userId = "scale-user") {
|
||||
await memory.add("The user's favorite drink is green tea.", {
|
||||
userId,
|
||||
infer: false,
|
||||
});
|
||||
}
|
||||
|
||||
describe("Node OSS scale threshold notice", () => {
|
||||
let originalMem0Dir: string | undefined;
|
||||
let originalTelemetry: string | undefined;
|
||||
let originalSampleRate: string | undefined;
|
||||
let originalFetch: typeof global.fetch;
|
||||
let stderrSpy: jest.SpyInstance;
|
||||
|
||||
beforeEach(() => {
|
||||
originalMem0Dir = process.env.MEM0_DIR;
|
||||
originalTelemetry = process.env.MEM0_TELEMETRY;
|
||||
originalSampleRate = process.env.MEM0_TELEMETRY_SAMPLE_RATE;
|
||||
originalFetch = global.fetch;
|
||||
|
||||
process.env.MEM0_DIR = makeTempMem0Dir();
|
||||
process.env.MEM0_TELEMETRY = "true";
|
||||
process.env.MEM0_TELEMETRY_SAMPLE_RATE = "1";
|
||||
stderrSpy = jest
|
||||
.spyOn(process.stderr, "write")
|
||||
.mockImplementation(() => true);
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
afterEach(() => {
|
||||
if (originalMem0Dir === undefined) delete process.env.MEM0_DIR;
|
||||
else process.env.MEM0_DIR = originalMem0Dir;
|
||||
|
||||
if (originalTelemetry === undefined) delete process.env.MEM0_TELEMETRY;
|
||||
else process.env.MEM0_TELEMETRY = originalTelemetry;
|
||||
|
||||
if (originalSampleRate === undefined) {
|
||||
delete process.env.MEM0_TELEMETRY_SAMPLE_RATE;
|
||||
} else {
|
||||
process.env.MEM0_TELEMETRY_SAMPLE_RATE = originalSampleRate;
|
||||
}
|
||||
|
||||
global.fetch = originalFetch;
|
||||
jest.restoreAllMocks();
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
it("detects high topK after successful search", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await addSeed(memory);
|
||||
stderrSpy.mockClear();
|
||||
const result = await memory.search("favorite drink private text", {
|
||||
filters: { user_id: "scale-user" },
|
||||
topK: 50,
|
||||
});
|
||||
|
||||
expect(result.results.length).toBeGreaterThan(0);
|
||||
expect(stderrSpy).toHaveBeenCalledWith("Scale top 50\n");
|
||||
const event = scaleEvents(calls)[0];
|
||||
expect(event.properties).toMatchObject({
|
||||
notice_id: "scale_threshold",
|
||||
displayed: true,
|
||||
trigger_function: "search",
|
||||
trigger_source: "top_k",
|
||||
trigger_reason: "high_top_k",
|
||||
top_k: 50,
|
||||
threshold: 50,
|
||||
sample_rate: 1,
|
||||
});
|
||||
expect(JSON.stringify(event.properties)).not.toContain("private text");
|
||||
});
|
||||
|
||||
it("detects high topK after successful getAll", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await addSeed(memory);
|
||||
stderrSpy.mockClear();
|
||||
const result = await memory.getAll({
|
||||
filters: { user_id: "scale-user" },
|
||||
topK: 50,
|
||||
});
|
||||
|
||||
expect(result.results.length).toBeGreaterThan(0);
|
||||
expect(stderrSpy).toHaveBeenCalledWith("Scale top 50\n");
|
||||
const event = scaleEvents(calls)[0];
|
||||
expect(event.properties).toMatchObject({
|
||||
notice_id: "scale_threshold",
|
||||
displayed: true,
|
||||
trigger_function: "get_all",
|
||||
trigger_source: "top_k",
|
||||
trigger_reason: "high_top_k",
|
||||
top_k: 50,
|
||||
threshold: 50,
|
||||
});
|
||||
});
|
||||
|
||||
it("does not evaluate topK below the threshold", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await addSeed(memory);
|
||||
await memory.search("favorite drink", {
|
||||
filters: { user_id: "scale-user" },
|
||||
topK: 49,
|
||||
});
|
||||
|
||||
expect(flagRequestCount(fetchMock)).toBe(0);
|
||||
expect(scaleEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.scale_threshold).toBeUndefined();
|
||||
});
|
||||
|
||||
it("marks memory-count threshold evaluated before PostHog display", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ failFlags: true });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
(memory as any).vectorStore.count = jest
|
||||
.fn()
|
||||
.mockResolvedValue({ count: 2000 });
|
||||
|
||||
await memory.add("Scale count threshold fixture.", {
|
||||
userId: "scale-user",
|
||||
infer: false,
|
||||
});
|
||||
|
||||
expect((memory as any).vectorStore.count).toHaveBeenCalledTimes(1);
|
||||
expect(
|
||||
readConfig().notice_state.scale_threshold
|
||||
.memory_count_threshold_evaluated,
|
||||
).toBe(true);
|
||||
expect(scaleEvents(calls)).toHaveLength(0);
|
||||
});
|
||||
|
||||
it("does not count provider memories once threshold was evaluated", async () => {
|
||||
consumeFirstRun({ memory_count_threshold_evaluated: true });
|
||||
const { fetchMock } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
(memory as any).vectorStore.count = jest
|
||||
.fn()
|
||||
.mockResolvedValue({ count: 2000 });
|
||||
|
||||
await memory.add("Scale count already evaluated fixture.", {
|
||||
userId: "scale-user",
|
||||
infer: false,
|
||||
});
|
||||
|
||||
expect((memory as any).vectorStore.count).not.toHaveBeenCalled();
|
||||
expect(flagRequestCount(fetchMock)).toBe(0);
|
||||
});
|
||||
|
||||
it("throttles under-threshold provider counts", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
(memory as any).vectorStore.count = jest
|
||||
.fn()
|
||||
.mockResolvedValue({ count: 1999 });
|
||||
|
||||
await memory.add("Scale count under threshold one.", {
|
||||
userId: "scale-user",
|
||||
infer: false,
|
||||
});
|
||||
await memory.add("Scale count under threshold two.", {
|
||||
userId: "scale-user",
|
||||
infer: false,
|
||||
});
|
||||
|
||||
expect((memory as any).vectorStore.count).toHaveBeenCalledTimes(1);
|
||||
expect(flagRequestCount(fetchMock)).toBe(0);
|
||||
});
|
||||
|
||||
it("records holdout and disabled variants silently", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "holdout",
|
||||
payload: JSON.stringify(scalePayload({ enabled: false })),
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await addSeed(memory);
|
||||
stderrSpy.mockClear();
|
||||
await memory.search("favorite drink", {
|
||||
filters: { user_id: "scale-user" },
|
||||
topK: 50,
|
||||
});
|
||||
|
||||
expect(stderrSpy).not.toHaveBeenCalled();
|
||||
const event = scaleEvents(calls)[0];
|
||||
expect(event.properties).toMatchObject({
|
||||
displayed: false,
|
||||
bypass_reason: "payload_disabled",
|
||||
disabled_reason: "payload_disabled",
|
||||
variant: "holdout",
|
||||
});
|
||||
});
|
||||
|
||||
it("treats missing enabled as enabled for scale payloads", async () => {
|
||||
consumeFirstRun();
|
||||
const payload = scalePayload();
|
||||
delete (payload.notices.scale_threshold as Record<string, any>).enabled;
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
payload: JSON.stringify(payload),
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await addSeed(memory);
|
||||
stderrSpy.mockClear();
|
||||
await memory.search("favorite drink", {
|
||||
filters: { user_id: "scale-user" },
|
||||
topK: 50,
|
||||
});
|
||||
|
||||
expect(stderrSpy.mock.calls.flat().join("")).toContain("Scale top 50");
|
||||
expect(scaleEvents(calls)[0].properties).toMatchObject({
|
||||
notice_id: "scale_threshold",
|
||||
displayed: true,
|
||||
payload: "Scale top 50",
|
||||
});
|
||||
});
|
||||
|
||||
it("caps evaluated scale opportunities at 10 per week", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await addSeed(memory);
|
||||
stderrSpy.mockClear();
|
||||
for (let index = 0; index < 11; index++) {
|
||||
await memory.search(`favorite drink ${index}`, {
|
||||
filters: { user_id: "scale-user" },
|
||||
topK: 50,
|
||||
});
|
||||
}
|
||||
|
||||
expect(scaleEvents(calls)).toHaveLength(10);
|
||||
expect(flagRequestCount(fetchMock)).toBe(10);
|
||||
expect(stderrSpy).toHaveBeenCalledTimes(10);
|
||||
expect(readConfig().notice_state.scale_threshold.events).toHaveLength(10);
|
||||
});
|
||||
|
||||
it("does not consume first-run on the same qualifying scale call", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.getAll({
|
||||
filters: { user_id: "scale-user" },
|
||||
topK: 50,
|
||||
});
|
||||
|
||||
expect(scaleEvents(calls)).toHaveLength(1);
|
||||
expect(readConfig().notice_state?.first_run).toBeUndefined();
|
||||
});
|
||||
|
||||
it("telemetry off skips flag evaluation, event emission, and state writes", async () => {
|
||||
process.env.MEM0_TELEMETRY = "false";
|
||||
jest.resetModules();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.getAll({
|
||||
filters: { user_id: "scale-user" },
|
||||
topK: 50,
|
||||
});
|
||||
|
||||
expect(flagRequestCount(fetchMock)).toBe(0);
|
||||
expect(scaleEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state).toBeUndefined();
|
||||
});
|
||||
});
|
||||
@@ -1,516 +0,0 @@
|
||||
/// <reference types="jest" />
|
||||
import * as fs from "fs";
|
||||
import * as os from "os";
|
||||
import * as path from "path";
|
||||
|
||||
jest.setTimeout(15000);
|
||||
|
||||
jest.mock("../src/embeddings/google", () => ({
|
||||
GoogleEmbedder: jest.fn(),
|
||||
}));
|
||||
jest.mock("../src/llms/google", () => ({
|
||||
GoogleLLM: jest.fn(),
|
||||
}));
|
||||
|
||||
const mockEmbedding = new Array(1536).fill(0.1);
|
||||
const mockEmbed = jest.fn().mockResolvedValue(mockEmbedding);
|
||||
const mockEmbedBatch = jest
|
||||
.fn()
|
||||
.mockImplementation((texts: string[]) =>
|
||||
Promise.resolve(texts.map(() => mockEmbedding)),
|
||||
);
|
||||
|
||||
jest.mock("../src/embeddings/openai", () => ({
|
||||
OpenAIEmbedder: jest.fn().mockImplementation(() => ({
|
||||
embed: mockEmbed,
|
||||
embedBatch: mockEmbedBatch,
|
||||
embeddingDims: 1536,
|
||||
})),
|
||||
}));
|
||||
|
||||
const mockGenerateResponse = jest.fn();
|
||||
jest.mock("../src/llms/openai", () => ({
|
||||
OpenAILLM: jest.fn().mockImplementation(() => ({
|
||||
generateResponse: mockGenerateResponse,
|
||||
})),
|
||||
}));
|
||||
|
||||
const TEMPORAL_COPY =
|
||||
"Temporal reasoning requires a Mem0 API key. Get one for free at https://app.mem0.ai";
|
||||
const PLAIN_TIMESTAMP_ERROR =
|
||||
"The timestamp parameter is not supported by the OSS Memory SDK.";
|
||||
const PLAIN_REFERENCE_DATE_ERROR =
|
||||
"The referenceDate parameter is not supported by the OSS Memory SDK.";
|
||||
|
||||
function makeTempMem0Dir(): string {
|
||||
return fs.mkdtempSync(path.join(os.tmpdir(), "mem0-node-temporal-feature-"));
|
||||
}
|
||||
|
||||
function temporalPayload(overrides: Record<string, any> = {}) {
|
||||
return {
|
||||
notices: {
|
||||
temporal_stub: {
|
||||
enabled: true,
|
||||
notice_type: "error",
|
||||
copy: TEMPORAL_COPY,
|
||||
...overrides,
|
||||
},
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
function createFetchMock(options: {
|
||||
variant?: string;
|
||||
payload?: unknown;
|
||||
failFlags?: boolean;
|
||||
flagEnabled?: boolean;
|
||||
}) {
|
||||
const calls: any[] = [];
|
||||
const fetchMock = jest.fn(async (url: string | URL, init?: RequestInit) => {
|
||||
const target = String(url);
|
||||
|
||||
if (target.includes("/flags")) {
|
||||
if (options.failFlags) {
|
||||
throw new Error("flag evaluation failed");
|
||||
}
|
||||
return {
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({
|
||||
flags: {
|
||||
"mem0-oss-notices": {
|
||||
key: "mem0-oss-notices",
|
||||
enabled: options.flagEnabled ?? true,
|
||||
variant: options.variant ?? "displayed",
|
||||
metadata: {
|
||||
payload:
|
||||
options.payload === undefined
|
||||
? JSON.stringify(temporalPayload())
|
||||
: options.payload,
|
||||
},
|
||||
},
|
||||
},
|
||||
}),
|
||||
};
|
||||
}
|
||||
|
||||
if (target.includes("/i/v0/e/")) {
|
||||
calls.push(JSON.parse(String(init?.body)));
|
||||
return {
|
||||
ok: true,
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({}),
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
};
|
||||
});
|
||||
|
||||
return { fetchMock, calls };
|
||||
}
|
||||
|
||||
function noticeEvents(calls: any[]) {
|
||||
return calls.filter((call) => call.event === "mem0.notice_displayed");
|
||||
}
|
||||
|
||||
async function createMemory() {
|
||||
const { Memory } = await import("../src/memory");
|
||||
return new Memory({
|
||||
version: "v1.1",
|
||||
embedder: {
|
||||
provider: "openai",
|
||||
config: { apiKey: "test-key", model: "text-embedding-3-small" },
|
||||
},
|
||||
vectorStore: {
|
||||
provider: "memory",
|
||||
config: {
|
||||
collectionName: `test-temporal-feature-${Date.now()}-${Math.random()}`,
|
||||
dimension: 1536,
|
||||
dbPath: ":memory:",
|
||||
},
|
||||
},
|
||||
llm: {
|
||||
provider: "openai",
|
||||
config: { apiKey: "test-key", model: "gpt-5-mini" },
|
||||
},
|
||||
historyDbPath: ":memory:",
|
||||
});
|
||||
}
|
||||
|
||||
describe("Node OSS temporal feature error notice", () => {
|
||||
let originalMem0Dir: string | undefined;
|
||||
let originalTelemetry: string | undefined;
|
||||
let originalSampleRate: string | undefined;
|
||||
let originalFetch: typeof global.fetch;
|
||||
|
||||
beforeEach(() => {
|
||||
originalMem0Dir = process.env.MEM0_DIR;
|
||||
originalTelemetry = process.env.MEM0_TELEMETRY;
|
||||
originalSampleRate = process.env.MEM0_TELEMETRY_SAMPLE_RATE;
|
||||
originalFetch = global.fetch;
|
||||
|
||||
process.env.MEM0_DIR = makeTempMem0Dir();
|
||||
process.env.MEM0_TELEMETRY = "true";
|
||||
process.env.MEM0_TELEMETRY_SAMPLE_RATE = "1";
|
||||
mockEmbed.mockClear();
|
||||
mockEmbedBatch.mockClear();
|
||||
mockGenerateResponse.mockClear();
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
afterEach(() => {
|
||||
if (originalMem0Dir === undefined) delete process.env.MEM0_DIR;
|
||||
else process.env.MEM0_DIR = originalMem0Dir;
|
||||
|
||||
if (originalTelemetry === undefined) delete process.env.MEM0_TELEMETRY;
|
||||
else process.env.MEM0_TELEMETRY = originalTelemetry;
|
||||
|
||||
if (originalSampleRate === undefined) {
|
||||
delete process.env.MEM0_TELEMETRY_SAMPLE_RATE;
|
||||
} else {
|
||||
process.env.MEM0_TELEMETRY_SAMPLE_RATE = originalSampleRate;
|
||||
}
|
||||
|
||||
global.fetch = originalFetch;
|
||||
jest.restoreAllMocks();
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
it.each([
|
||||
["add", "timestamp"],
|
||||
["search", "referenceDate"],
|
||||
])(
|
||||
"raises CTA copy for displayed %s(%s) and emits displayed=true",
|
||||
async (triggerFunction, triggerParameter) => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
if (triggerFunction === "add") {
|
||||
await expect(
|
||||
memory.add("Temporal add", {
|
||||
userId: "temporal-user",
|
||||
timestamp: 1778112000,
|
||||
}),
|
||||
).rejects.toThrow(TEMPORAL_COPY);
|
||||
} else {
|
||||
await expect(
|
||||
memory.search("Temporal search", {
|
||||
filters: { user_id: "temporal-user" },
|
||||
referenceDate: "2026-05-06",
|
||||
}),
|
||||
).rejects.toThrow(TEMPORAL_COPY);
|
||||
}
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "temporal_stub",
|
||||
notice_type: "error",
|
||||
flag_key: "mem0-oss-notices",
|
||||
variant: "displayed",
|
||||
displayed: true,
|
||||
payload: TEMPORAL_COPY,
|
||||
notice_config_found: true,
|
||||
sync_type: "async",
|
||||
trigger_function: triggerFunction,
|
||||
trigger_parameter: triggerParameter,
|
||||
sample_rate: 1,
|
||||
}),
|
||||
);
|
||||
},
|
||||
);
|
||||
|
||||
it.each([
|
||||
["add", "timestamp"],
|
||||
["search", "referenceDate"],
|
||||
])(
|
||||
"raises CTA copy for holdout %s(%s) and emits displayed=true",
|
||||
async (triggerFunction, triggerParameter) => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "holdout" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
if (triggerFunction === "add") {
|
||||
await expect(
|
||||
memory.add("Temporal add", {
|
||||
userId: "temporal-user",
|
||||
timestamp: null,
|
||||
}),
|
||||
).rejects.toThrow(TEMPORAL_COPY);
|
||||
} else {
|
||||
await expect(
|
||||
memory.search("Temporal search", {
|
||||
filters: { user_id: "temporal-user" },
|
||||
referenceDate: null,
|
||||
}),
|
||||
).rejects.toThrow(TEMPORAL_COPY);
|
||||
}
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "temporal_stub",
|
||||
variant: "holdout",
|
||||
displayed: true,
|
||||
trigger_function: triggerFunction,
|
||||
trigger_parameter: triggerParameter,
|
||||
}),
|
||||
);
|
||||
expect(notices[0].properties.bypass_reason).toBeUndefined();
|
||||
},
|
||||
);
|
||||
|
||||
it("uses plain error for unknown future variants and emits not_displayed", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "silent" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(
|
||||
memory.add("Temporal add", {
|
||||
userId: "temporal-user",
|
||||
timestamp: 1778112000,
|
||||
}),
|
||||
).rejects.toThrow(PLAIN_TIMESTAMP_ERROR);
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "temporal_stub",
|
||||
variant: "silent",
|
||||
displayed: false,
|
||||
bypass_reason: "not_displayed",
|
||||
payload: TEMPORAL_COPY,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("treats missing enabled as enabled for feature-error payloads", async () => {
|
||||
const payload = temporalPayload();
|
||||
delete (payload.notices.temporal_stub as Record<string, any>).enabled;
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
payload: JSON.stringify(payload),
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(
|
||||
memory.add("Temporal add", {
|
||||
userId: "temporal-user",
|
||||
timestamp: 1778112000,
|
||||
}),
|
||||
).rejects.toThrow(TEMPORAL_COPY);
|
||||
|
||||
expect(noticeEvents(calls)[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "temporal_stub",
|
||||
displayed: true,
|
||||
payload: TEMPORAL_COPY,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("uses timestamp plain error for disabled payload and emits payload_disabled", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
payload: JSON.stringify(temporalPayload({ enabled: false })),
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(
|
||||
memory.add("Temporal add", {
|
||||
userId: "temporal-user",
|
||||
timestamp: 1778112000,
|
||||
}),
|
||||
).rejects.toThrow(PLAIN_TIMESTAMP_ERROR);
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "temporal_stub",
|
||||
displayed: false,
|
||||
bypass_reason: "payload_disabled",
|
||||
disabled_reason: "payload_disabled",
|
||||
notice_config_found: true,
|
||||
trigger_function: "add",
|
||||
trigger_parameter: "timestamp",
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it.each([
|
||||
[
|
||||
"missing config",
|
||||
JSON.stringify({ notices: {} }),
|
||||
"missing_notice_config",
|
||||
],
|
||||
[
|
||||
"missing copy",
|
||||
JSON.stringify(temporalPayload({ copy: "" })),
|
||||
"missing_copy",
|
||||
],
|
||||
["malformed payload", "{not-json", "missing_notice_config"],
|
||||
])(
|
||||
"uses referenceDate plain error for %s",
|
||||
async (_label, payload, bypassReason) => {
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
payload,
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(
|
||||
memory.search("Temporal search", {
|
||||
filters: { user_id: "temporal-user" },
|
||||
referenceDate: "2026-05-06",
|
||||
}),
|
||||
).rejects.toThrow(PLAIN_REFERENCE_DATE_ERROR);
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "temporal_stub",
|
||||
displayed: false,
|
||||
bypass_reason: bypassReason,
|
||||
trigger_function: "search",
|
||||
trigger_parameter: "referenceDate",
|
||||
}),
|
||||
);
|
||||
},
|
||||
);
|
||||
|
||||
it("uses plain error and emits no event when the blunt flag is disabled", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
flagEnabled: false,
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(
|
||||
memory.add("Temporal add", {
|
||||
userId: "temporal-user",
|
||||
timestamp: 1778112000,
|
||||
}),
|
||||
).rejects.toThrow(PLAIN_TIMESTAMP_ERROR);
|
||||
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
});
|
||||
|
||||
it("uses plain error and emits no event when PostHog fails", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ failFlags: true });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(
|
||||
memory.search("Temporal search", {
|
||||
filters: { user_id: "temporal-user" },
|
||||
referenceDate: "2026-05-06",
|
||||
}),
|
||||
).rejects.toThrow(PLAIN_REFERENCE_DATE_ERROR);
|
||||
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
});
|
||||
|
||||
it("uses plain error and skips flag evaluation when telemetry is off", async () => {
|
||||
process.env.MEM0_TELEMETRY = "False";
|
||||
jest.resetModules();
|
||||
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(
|
||||
memory.add("Temporal add", {
|
||||
userId: "temporal-user",
|
||||
timestamp: 1778112000,
|
||||
}),
|
||||
).rejects.toThrow(PLAIN_TIMESTAMP_ERROR);
|
||||
|
||||
expect(fetchMock).not.toHaveBeenCalled();
|
||||
expect(noticeEvents(calls)).toHaveLength(0);
|
||||
});
|
||||
|
||||
it("throws before add validation, normal telemetry, embeddings, and first-run", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
const addToVectorStoreSpy = jest.spyOn(memory as any, "addToVectorStore");
|
||||
|
||||
await expect(
|
||||
memory.add(
|
||||
undefined as any,
|
||||
{
|
||||
timestamp: 1778112000,
|
||||
} as any,
|
||||
),
|
||||
).rejects.toThrow(TEMPORAL_COPY);
|
||||
|
||||
expect(mockEmbed).not.toHaveBeenCalled();
|
||||
expect(addToVectorStoreSpy).not.toHaveBeenCalled();
|
||||
expect(calls.map((call) => call.event)).not.toContain("mem0.add");
|
||||
expect(
|
||||
noticeEvents(calls).some(
|
||||
(call) => call.properties.notice_id === "first_run",
|
||||
),
|
||||
).toBe(false);
|
||||
});
|
||||
|
||||
it("throws before search validation, normal telemetry, embeddings, and first-run", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(
|
||||
memory.search("Temporal search", {
|
||||
userId: "invalid-top-level-user",
|
||||
referenceDate: "2026-05-06",
|
||||
} as any),
|
||||
).rejects.toThrow(TEMPORAL_COPY);
|
||||
|
||||
expect(mockEmbed).not.toHaveBeenCalled();
|
||||
expect(calls.map((call) => call.event)).not.toContain("mem0.search");
|
||||
expect(
|
||||
noticeEvents(calls).some(
|
||||
(call) => call.properties.notice_id === "first_run",
|
||||
),
|
||||
).toBe(false);
|
||||
});
|
||||
|
||||
it("leaves normal add/search calls unchanged when temporal options are omitted", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "holdout" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
const addResult = await memory.add("Normal add", {
|
||||
userId: "normal-user",
|
||||
infer: false,
|
||||
});
|
||||
expect(addResult.results).toHaveLength(1);
|
||||
|
||||
const searchResult = await memory.search("Normal add", {
|
||||
filters: { user_id: "normal-user" },
|
||||
topK: 3,
|
||||
});
|
||||
expect(searchResult.results.length).toBeGreaterThanOrEqual(0);
|
||||
|
||||
const eventNames = calls.map((call) => call.event);
|
||||
expect(eventNames).toContain("mem0.add");
|
||||
expect(eventNames).toContain("mem0.search");
|
||||
expect(
|
||||
noticeEvents(calls).some(
|
||||
(call) => call.properties.notice_id === "temporal_stub",
|
||||
),
|
||||
).toBe(false);
|
||||
});
|
||||
});
|
||||
@@ -1,594 +0,0 @@
|
||||
/// <reference types="jest" />
|
||||
import * as fs from "fs";
|
||||
import * as os from "os";
|
||||
import * as path from "path";
|
||||
|
||||
jest.setTimeout(15000);
|
||||
|
||||
jest.mock("../src/embeddings/google", () => ({
|
||||
GoogleEmbedder: jest.fn(),
|
||||
}));
|
||||
jest.mock("../src/llms/google", () => ({
|
||||
GoogleLLM: jest.fn(),
|
||||
}));
|
||||
|
||||
const mockEmbedding = new Array(1536).fill(0.1);
|
||||
jest.mock("../src/embeddings/openai", () => ({
|
||||
OpenAIEmbedder: jest.fn().mockImplementation(() => ({
|
||||
embed: jest.fn().mockResolvedValue(mockEmbedding),
|
||||
embedBatch: jest
|
||||
.fn()
|
||||
.mockImplementation((texts: string[]) =>
|
||||
Promise.resolve(texts.map(() => mockEmbedding)),
|
||||
),
|
||||
embeddingDims: 1536,
|
||||
})),
|
||||
}));
|
||||
|
||||
jest.mock("../src/llms/openai", () => ({
|
||||
OpenAILLM: jest.fn().mockImplementation(() => ({
|
||||
generateResponse: jest.fn(),
|
||||
})),
|
||||
}));
|
||||
|
||||
const TEMPORAL_USAGE_COPY =
|
||||
"This looks like a time-aware memory workflow. Mem0 Platform has temporal reasoning built in. Use `timestamp` when adding memories and `reference_date` when searching.";
|
||||
|
||||
function makeTempMem0Dir(): string {
|
||||
return fs.mkdtempSync(path.join(os.tmpdir(), "mem0-node-temporal-usage-"));
|
||||
}
|
||||
|
||||
function configPath(): string {
|
||||
return path.join(process.env.MEM0_DIR as string, "config.json");
|
||||
}
|
||||
|
||||
function readConfig(): Record<string, any> {
|
||||
const file = configPath();
|
||||
if (!fs.existsSync(file)) return {};
|
||||
return JSON.parse(fs.readFileSync(file, "utf8"));
|
||||
}
|
||||
|
||||
function writeConfig(config: Record<string, any>) {
|
||||
fs.mkdirSync(path.dirname(configPath()), { recursive: true });
|
||||
fs.writeFileSync(configPath(), JSON.stringify(config, null, 4));
|
||||
}
|
||||
|
||||
function consumeFirstRun() {
|
||||
writeConfig({
|
||||
user_id: "node-temporal-usage-test-user",
|
||||
notice_state: {
|
||||
first_run: {
|
||||
consumed: true,
|
||||
trigger_function: "test_setup",
|
||||
variant: "test",
|
||||
},
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
function temporalUsagePayload(overrides: Record<string, any> = {}) {
|
||||
return {
|
||||
notices: {
|
||||
temporal_usage: {
|
||||
enabled: true,
|
||||
notice_type: "log_line",
|
||||
copy: TEMPORAL_USAGE_COPY,
|
||||
...overrides,
|
||||
},
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
function createFetchMock(options: {
|
||||
variant?: string;
|
||||
payload?: unknown;
|
||||
failFlags?: boolean;
|
||||
flagEnabled?: boolean;
|
||||
}) {
|
||||
const calls: any[] = [];
|
||||
const fetchMock = jest.fn(async (url: string | URL, init?: RequestInit) => {
|
||||
const target = String(url);
|
||||
|
||||
if (target.includes("/flags")) {
|
||||
if (options.failFlags) {
|
||||
throw new Error("flag evaluation failed");
|
||||
}
|
||||
const payload =
|
||||
options.payload === undefined
|
||||
? JSON.stringify(temporalUsagePayload())
|
||||
: options.payload;
|
||||
return {
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({
|
||||
flags: {
|
||||
"mem0-oss-notices": {
|
||||
key: "mem0-oss-notices",
|
||||
enabled: options.flagEnabled ?? true,
|
||||
variant: options.variant ?? "displayed",
|
||||
metadata: { payload },
|
||||
},
|
||||
},
|
||||
}),
|
||||
};
|
||||
}
|
||||
|
||||
if (target.includes("/i/v0/e/")) {
|
||||
calls.push(JSON.parse(String(init?.body)));
|
||||
return {
|
||||
ok: true,
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
ok: true,
|
||||
json: jest.fn().mockResolvedValue({}),
|
||||
text: jest.fn().mockResolvedValue(""),
|
||||
};
|
||||
});
|
||||
|
||||
return { fetchMock, calls };
|
||||
}
|
||||
|
||||
function noticeEvents(calls: any[]) {
|
||||
return calls.filter((call) => call.event === "mem0.notice_displayed");
|
||||
}
|
||||
|
||||
function temporalUsageEvents(calls: any[]) {
|
||||
return noticeEvents(calls).filter(
|
||||
(call) => call.properties.notice_id === "temporal_usage",
|
||||
);
|
||||
}
|
||||
|
||||
function flagRequestCount(fetchMock: jest.Mock) {
|
||||
return fetchMock.mock.calls.filter(([url]) => String(url).includes("/flags"))
|
||||
.length;
|
||||
}
|
||||
|
||||
async function createMemory() {
|
||||
const { Memory } = await import("../src/memory");
|
||||
return new Memory({
|
||||
version: "v1.1",
|
||||
embedder: {
|
||||
provider: "openai",
|
||||
config: { apiKey: "test-key", model: "text-embedding-3-small" },
|
||||
},
|
||||
vectorStore: {
|
||||
provider: "memory",
|
||||
config: {
|
||||
collectionName: `test-temporal-usage-${Date.now()}-${Math.random()}`,
|
||||
dimension: 1536,
|
||||
dbPath: ":memory:",
|
||||
},
|
||||
},
|
||||
llm: {
|
||||
provider: "openai",
|
||||
config: { apiKey: "test-key", model: "gpt-5-mini" },
|
||||
},
|
||||
historyDbPath: ":memory:",
|
||||
});
|
||||
}
|
||||
|
||||
async function addSearchSeed(memory: any, userId = "temporal-user") {
|
||||
await memory.add("The user's favorite drink is green tea.", {
|
||||
userId,
|
||||
infer: false,
|
||||
});
|
||||
}
|
||||
|
||||
describe("Node OSS temporal usage notice", () => {
|
||||
let originalMem0Dir: string | undefined;
|
||||
let originalTelemetry: string | undefined;
|
||||
let originalSampleRate: string | undefined;
|
||||
let originalFetch: typeof global.fetch;
|
||||
let stderrSpy: jest.SpyInstance;
|
||||
|
||||
beforeEach(() => {
|
||||
originalMem0Dir = process.env.MEM0_DIR;
|
||||
originalTelemetry = process.env.MEM0_TELEMETRY;
|
||||
originalSampleRate = process.env.MEM0_TELEMETRY_SAMPLE_RATE;
|
||||
originalFetch = global.fetch;
|
||||
|
||||
process.env.MEM0_DIR = makeTempMem0Dir();
|
||||
process.env.MEM0_TELEMETRY = "true";
|
||||
process.env.MEM0_TELEMETRY_SAMPLE_RATE = "1";
|
||||
stderrSpy = jest
|
||||
.spyOn(process.stderr, "write")
|
||||
.mockImplementation(() => true);
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
afterEach(() => {
|
||||
if (originalMem0Dir === undefined) delete process.env.MEM0_DIR;
|
||||
else process.env.MEM0_DIR = originalMem0Dir;
|
||||
|
||||
if (originalTelemetry === undefined) delete process.env.MEM0_TELEMETRY;
|
||||
else process.env.MEM0_TELEMETRY = originalTelemetry;
|
||||
|
||||
if (originalSampleRate === undefined) {
|
||||
delete process.env.MEM0_TELEMETRY_SAMPLE_RATE;
|
||||
} else {
|
||||
process.env.MEM0_TELEMETRY_SAMPLE_RATE = originalSampleRate;
|
||||
}
|
||||
|
||||
global.fetch = originalFetch;
|
||||
jest.restoreAllMocks();
|
||||
jest.resetModules();
|
||||
});
|
||||
|
||||
it("detects timestamp-like metadata after successful add", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
const result = await memory.add("Temporal metadata memory", {
|
||||
userId: "temporal-user",
|
||||
infer: false,
|
||||
metadata: { event_date: "2025-04-09" },
|
||||
});
|
||||
|
||||
expect(result.results).toHaveLength(1);
|
||||
expect(stderrSpy).toHaveBeenCalledWith(`${TEMPORAL_USAGE_COPY}\n`);
|
||||
const notices = temporalUsageEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "temporal_usage",
|
||||
notice_type: "log_line",
|
||||
flag_key: "mem0-oss-notices",
|
||||
variant: "displayed",
|
||||
displayed: true,
|
||||
payload: TEMPORAL_USAGE_COPY,
|
||||
notice_config_found: true,
|
||||
sync_type: "async",
|
||||
trigger_function: "add",
|
||||
trigger_source: "metadata",
|
||||
trigger_reason: "date_like_metadata",
|
||||
sample_rate: 1,
|
||||
}),
|
||||
);
|
||||
expect(readConfig().notice_state.temporal_usage.events).toHaveLength(1);
|
||||
});
|
||||
|
||||
it("emits query trigger fields after successful search", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addSearchSeed(memory);
|
||||
|
||||
await memory.search("what happened last week?", {
|
||||
filters: { user_id: "temporal-user" },
|
||||
topK: 3,
|
||||
});
|
||||
|
||||
const notices = temporalUsageEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "temporal_usage",
|
||||
displayed: true,
|
||||
trigger_function: "search",
|
||||
trigger_source: "query",
|
||||
trigger_reason: "relative_phrase",
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("detects temporal range filters after successful search", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addSearchSeed(memory);
|
||||
|
||||
await memory.search("favorite drink", {
|
||||
filters: {
|
||||
user_id: "temporal-user",
|
||||
AND: [{ created_at: { gte: "2025-04-01" } }],
|
||||
},
|
||||
topK: 3,
|
||||
});
|
||||
|
||||
const notices = temporalUsageEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "temporal_usage",
|
||||
displayed: true,
|
||||
trigger_function: "search",
|
||||
trigger_source: "filter",
|
||||
trigger_reason: "date_range_filter",
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("does not evaluate for normal non-temporal add/search calls", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.add("Normal memory", {
|
||||
userId: "normal-user",
|
||||
infer: false,
|
||||
metadata: { category: "planning" },
|
||||
});
|
||||
await memory.search("favorite drink", {
|
||||
filters: { user_id: "normal-user" },
|
||||
topK: 3,
|
||||
});
|
||||
|
||||
expect(flagRequestCount(fetchMock)).toBe(0);
|
||||
expect(temporalUsageEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.temporal_usage).toBeUndefined();
|
||||
});
|
||||
|
||||
it("does not evaluate when add/search fail", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await expect(
|
||||
memory.add("Invalid temporal metadata add", {
|
||||
infer: false,
|
||||
metadata: { event_date: "2025-04-09" },
|
||||
} as any),
|
||||
).rejects.toThrow("One of the filters");
|
||||
await expect(
|
||||
memory.search("what happened last week?", {
|
||||
filters: {},
|
||||
}),
|
||||
).rejects.toThrow("filters must contain");
|
||||
|
||||
expect(temporalUsageEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.temporal_usage).toBeUndefined();
|
||||
});
|
||||
|
||||
it("is silent for holdout and emits displayed=false", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "holdout" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.add("Temporal metadata memory", {
|
||||
userId: "temporal-user",
|
||||
infer: false,
|
||||
metadata: { event_date: "2025-04-09" },
|
||||
});
|
||||
|
||||
expect(stderrSpy).not.toHaveBeenCalled();
|
||||
const notices = temporalUsageEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "temporal_usage",
|
||||
variant: "holdout",
|
||||
displayed: false,
|
||||
bypass_reason: "holdout",
|
||||
trigger_source: "metadata",
|
||||
trigger_reason: "date_like_metadata",
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it.each([
|
||||
[
|
||||
"disabled payload",
|
||||
JSON.stringify(temporalUsagePayload({ enabled: false })),
|
||||
"payload_disabled",
|
||||
],
|
||||
[
|
||||
"missing config",
|
||||
JSON.stringify({ notices: {} }),
|
||||
"missing_notice_config",
|
||||
],
|
||||
[
|
||||
"missing copy",
|
||||
JSON.stringify(temporalUsagePayload({ copy: "" })),
|
||||
"missing_copy",
|
||||
],
|
||||
["malformed payload", "{not-json", "missing_notice_config"],
|
||||
])("is silent and safe for %s", async (_label, payload, bypassReason) => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
payload,
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.add("Temporal metadata memory", {
|
||||
userId: "temporal-user",
|
||||
infer: false,
|
||||
metadata: { event_date: "2025-04-09" },
|
||||
});
|
||||
|
||||
expect(stderrSpy).not.toHaveBeenCalled();
|
||||
const notices = temporalUsageEvents(calls);
|
||||
expect(notices).toHaveLength(1);
|
||||
expect(notices[0].properties).toEqual(
|
||||
expect.objectContaining({
|
||||
notice_id: "temporal_usage",
|
||||
displayed: false,
|
||||
bypass_reason: bypassReason,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it("does not consume cap or emit when the blunt flag is disabled", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({
|
||||
variant: "displayed",
|
||||
flagEnabled: false,
|
||||
});
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.add("Temporal metadata memory", {
|
||||
userId: "temporal-user",
|
||||
infer: false,
|
||||
metadata: { event_date: "2025-04-09" },
|
||||
});
|
||||
|
||||
expect(temporalUsageEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.temporal_usage).toBeUndefined();
|
||||
});
|
||||
|
||||
it("does not consume cap or emit when PostHog fails", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ failFlags: true });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.add("Temporal metadata memory", {
|
||||
userId: "temporal-user",
|
||||
infer: false,
|
||||
metadata: { event_date: "2025-04-09" },
|
||||
});
|
||||
|
||||
expect(temporalUsageEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state?.temporal_usage).toBeUndefined();
|
||||
});
|
||||
|
||||
it("does nothing when telemetry is off", async () => {
|
||||
process.env.MEM0_TELEMETRY = "False";
|
||||
jest.resetModules();
|
||||
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.add("Temporal metadata memory", {
|
||||
userId: "temporal-user",
|
||||
infer: false,
|
||||
metadata: { event_date: "2025-04-09" },
|
||||
});
|
||||
|
||||
expect(fetchMock).not.toHaveBeenCalled();
|
||||
expect(temporalUsageEvents(calls)).toHaveLength(0);
|
||||
expect(readConfig().notice_state).toBeUndefined();
|
||||
});
|
||||
|
||||
it("caps evaluated opportunities at 10 per rolling week", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addSearchSeed(memory);
|
||||
|
||||
for (let index = 1; index <= 11; index++) {
|
||||
await memory.search(`what happened last week ${index}?`, {
|
||||
filters: { user_id: "temporal-user" },
|
||||
topK: 3,
|
||||
});
|
||||
}
|
||||
|
||||
expect(temporalUsageEvents(calls)).toHaveLength(10);
|
||||
expect(flagRequestCount(fetchMock)).toBe(10);
|
||||
expect(readConfig().notice_state.temporal_usage.events).toHaveLength(10);
|
||||
expect(stderrSpy).toHaveBeenCalledTimes(10);
|
||||
});
|
||||
|
||||
it("does not consume first-run on the same qualifying temporal usage call", async () => {
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
|
||||
await memory.add("Temporal metadata memory", {
|
||||
userId: "temporal-user",
|
||||
infer: false,
|
||||
metadata: { event_date: "2025-04-09" },
|
||||
});
|
||||
|
||||
expect(readConfig().notice_state.first_run).toBeUndefined();
|
||||
await memory.add("Normal follow-up memory", {
|
||||
userId: "temporal-user",
|
||||
infer: false,
|
||||
});
|
||||
|
||||
const notices = noticeEvents(calls);
|
||||
expect(notices.map((call) => call.properties.notice_id)).toEqual([
|
||||
"temporal_usage",
|
||||
"first_run",
|
||||
]);
|
||||
expect(readConfig().notice_state.first_run.consumed).toBe(true);
|
||||
});
|
||||
|
||||
it("does not include raw query, metadata, or filter values in telemetry props", async () => {
|
||||
consumeFirstRun();
|
||||
const { fetchMock, calls } = createFetchMock({ variant: "displayed" });
|
||||
global.fetch = fetchMock as any;
|
||||
const memory = await createMemory();
|
||||
await addSearchSeed(memory);
|
||||
|
||||
await memory.search("private trip since 2025-04-09", {
|
||||
filters: {
|
||||
user_id: "temporal-user",
|
||||
created_at: { gte: "2025-04-09" },
|
||||
},
|
||||
topK: 3,
|
||||
});
|
||||
|
||||
const props = temporalUsageEvents(calls)[0].properties;
|
||||
const serialized = JSON.stringify(props);
|
||||
expect(serialized).not.toContain("private trip");
|
||||
expect(serialized).not.toContain("2025-04-09");
|
||||
expect(serialized).not.toContain("temporal-user");
|
||||
});
|
||||
|
||||
it("detects expected query, metadata, and filter cases conservatively", async () => {
|
||||
const { __noticeTestHooks } = await import("../src/utils/notices");
|
||||
|
||||
expect(
|
||||
__noticeTestHooks.detectTemporalUsageFromSearch("notes from today", null),
|
||||
).toEqual({ triggerSource: "query", triggerReason: "relative_phrase" });
|
||||
expect(
|
||||
__noticeTestHooks.detectTemporalUsageFromSearch(
|
||||
"notes from 2025-04-09",
|
||||
null,
|
||||
),
|
||||
).toEqual({ triggerSource: "query", triggerReason: "date_like_query" });
|
||||
expect(
|
||||
__noticeTestHooks.detectTemporalUsageFromMetadata({
|
||||
event_date: "2025-04-09",
|
||||
}),
|
||||
).toEqual({
|
||||
triggerSource: "metadata",
|
||||
triggerReason: "date_like_metadata",
|
||||
});
|
||||
expect(
|
||||
__noticeTestHooks.detectTemporalUsageFromSearch("favorite drink", {
|
||||
AND: [{ created_at: { gte: "2025-04-01" } }],
|
||||
}),
|
||||
).toEqual({ triggerSource: "filter", triggerReason: "date_range_filter" });
|
||||
expect(
|
||||
__noticeTestHooks.detectTemporalUsageFromSearch("favorite drink", null),
|
||||
).toBeNull();
|
||||
expect(
|
||||
__noticeTestHooks.detectTemporalUsageFromMetadata({
|
||||
category: "planning",
|
||||
}),
|
||||
).toBeNull();
|
||||
});
|
||||
|
||||
it("detectors never raise for cyclic metadata or filters", async () => {
|
||||
const { __noticeTestHooks } = await import("../src/utils/notices");
|
||||
const metadata: Record<string, any> = {};
|
||||
metadata.self = metadata;
|
||||
const filters: Record<string, any> = {};
|
||||
filters.AND = [filters];
|
||||
|
||||
expect(
|
||||
__noticeTestHooks.detectTemporalUsageFromMetadata(metadata),
|
||||
).toBeNull();
|
||||
expect(
|
||||
__noticeTestHooks.detectTemporalUsageFromSearch(
|
||||
"favorite drink",
|
||||
filters,
|
||||
),
|
||||
).toBeNull();
|
||||
});
|
||||
});
|
||||
@@ -61,13 +61,6 @@ describe("telemetry sampling", () => {
|
||||
expect(fetchMock).toHaveBeenCalledTimes(1);
|
||||
});
|
||||
|
||||
it("notice_displayed event fires even at the highest random value", async () => {
|
||||
randomSpy.mockReturnValue(0.999);
|
||||
const { captureClientEvent } = await import("../src/utils/telemetry");
|
||||
await captureClientEvent("notice_displayed", makeInstance());
|
||||
expect(fetchMock).toHaveBeenCalledTimes(1);
|
||||
});
|
||||
|
||||
it("init event payload has sample_rate: 1.0", async () => {
|
||||
randomSpy.mockReturnValue(0.999);
|
||||
const { captureClientEvent } = await import("../src/utils/telemetry");
|
||||
|
||||
@@ -923,7 +923,6 @@ describe("Memory class – backward compat with all providers", () => {
|
||||
|
||||
jest.doMock("../src/utils/telemetry", () => ({
|
||||
captureClientEvent: jest.fn().mockResolvedValue(undefined),
|
||||
isTelemetryEnabled: jest.fn(() => false),
|
||||
}));
|
||||
|
||||
MemoryClass = require("../src/memory").Memory;
|
||||
|
||||
@@ -16,10 +16,6 @@ class QdrantConfig(BaseModel):
|
||||
path: Optional[str] = Field("/tmp/qdrant", description="Path for local Qdrant database")
|
||||
url: Optional[str] = Field(None, description="Full URL for Qdrant server")
|
||||
api_key: Optional[str] = Field(None, description="API key for Qdrant server")
|
||||
https: Optional[bool] = Field(
|
||||
None,
|
||||
description="Whether to force HTTPS on or off. Explicit schemes in url take precedence.",
|
||||
)
|
||||
on_disk: Optional[bool] = Field(False,description="Enables persistent storage. Vectors are kept on disk (True) or in memory (False). Does not delete the local database path.")
|
||||
|
||||
@model_validator(mode="before")
|
||||
|
||||
+2
-195
@@ -5,7 +5,6 @@ import hashlib
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
import uuid
|
||||
import warnings
|
||||
from copy import deepcopy
|
||||
@@ -27,29 +26,6 @@ from mem0.memory.base import MemoryBase
|
||||
from mem0.memory.setup import mem0_dir, setup_config
|
||||
from mem0.memory.storage import SQLiteManager
|
||||
from mem0.memory.telemetry import MEM0_TELEMETRY, capture_event
|
||||
from mem0.memory.notices import (
|
||||
PERFORMANCE_SLOW_QUERY_THRESHOLD_SECONDS,
|
||||
detect_scale_threshold_from_add_result,
|
||||
detect_scale_threshold_from_top_k,
|
||||
detect_decay_usage_from_delete,
|
||||
detect_decay_usage_from_delete_all,
|
||||
detect_temporal_usage_from_metadata,
|
||||
detect_temporal_usage_from_search,
|
||||
display_decay_usage_notice,
|
||||
display_decay_usage_notice_async,
|
||||
display_first_run_notice,
|
||||
display_first_run_notice_async,
|
||||
display_performance_slow_query_notice,
|
||||
display_performance_slow_query_notice_async,
|
||||
display_scale_threshold_notice,
|
||||
display_scale_threshold_notice_async,
|
||||
display_temporal_usage_notice,
|
||||
display_temporal_usage_notice_async,
|
||||
get_decay_feature_error_message,
|
||||
get_decay_feature_error_message_async,
|
||||
get_temporal_feature_error_message,
|
||||
get_temporal_feature_error_message_async,
|
||||
)
|
||||
from mem0.memory.utils import (
|
||||
extract_json,
|
||||
parse_messages,
|
||||
@@ -373,36 +349,6 @@ def _entity_collection_name(provider: str, collection_name: str) -> str:
|
||||
setup_config()
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_PROJECT_UPDATE_UNSUPPORTED_ERROR = "Project updates are not supported by the OSS Memory SDK."
|
||||
|
||||
|
||||
class _OSSProject:
|
||||
def update(
|
||||
self,
|
||||
custom_instructions: Optional[str] = None,
|
||||
custom_categories: Optional[list] = None,
|
||||
retrieval_criteria: Optional[list] = None,
|
||||
multilingual: Optional[bool] = None,
|
||||
decay: Optional[bool] = None,
|
||||
):
|
||||
if decay is True:
|
||||
raise ValueError(get_decay_feature_error_message("sync", "project.update", "decay"))
|
||||
raise ValueError(_PROJECT_UPDATE_UNSUPPORTED_ERROR)
|
||||
|
||||
|
||||
class _AsyncOSSProject:
|
||||
async def update(
|
||||
self,
|
||||
custom_instructions: Optional[str] = None,
|
||||
custom_categories: Optional[list] = None,
|
||||
retrieval_criteria: Optional[list] = None,
|
||||
multilingual: Optional[bool] = None,
|
||||
decay: Optional[bool] = None,
|
||||
):
|
||||
if decay is True:
|
||||
raise ValueError(await get_decay_feature_error_message_async("async", "project.update", "decay"))
|
||||
raise ValueError(_PROJECT_UPDATE_UNSUPPORTED_ERROR)
|
||||
|
||||
|
||||
class Memory(MemoryBase):
|
||||
def __init__(self, config: MemoryConfig = MemoryConfig()):
|
||||
@@ -471,10 +417,6 @@ class Memory(MemoryBase):
|
||||
|
||||
capture_event("mem0.init", self, {"sync_type": "sync"})
|
||||
|
||||
@property
|
||||
def project(self):
|
||||
return _OSSProject()
|
||||
|
||||
@property
|
||||
def entity_store(self):
|
||||
"""Lazily initialize entity store on first use."""
|
||||
@@ -658,7 +600,6 @@ class Memory(MemoryBase):
|
||||
agent_id: Optional[str] = None,
|
||||
run_id: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None,
|
||||
timestamp: Optional[Any] = None,
|
||||
infer: bool = True,
|
||||
memory_type: Optional[str] = None,
|
||||
prompt: Optional[str] = None,
|
||||
@@ -676,7 +617,6 @@ class Memory(MemoryBase):
|
||||
agent_id (str, optional): ID of the agent creating the memory. Defaults to None.
|
||||
run_id (str, optional): ID of the run creating the memory. Defaults to None.
|
||||
metadata (dict, optional): Metadata to store with the memory. Defaults to None.
|
||||
timestamp (Any, optional): Platform-only temporal parameter. Not supported in OSS.
|
||||
infer (bool, optional): If True (default), an LLM is used to extract key facts from
|
||||
'messages' and decide whether to add, update, or delete related memories.
|
||||
If False, 'messages' are added as raw memories directly.
|
||||
@@ -699,10 +639,7 @@ class Memory(MemoryBase):
|
||||
LLMError: If LLM operations fail.
|
||||
DatabaseError: If database operations fail.
|
||||
"""
|
||||
if timestamp is not None:
|
||||
raise ValueError(get_temporal_feature_error_message("sync", "add", "timestamp"))
|
||||
|
||||
temporal_usage_notice = detect_temporal_usage_from_metadata(metadata)
|
||||
processed_metadata, effective_filters = _build_filters_and_metadata(
|
||||
user_id=user_id,
|
||||
agent_id=agent_id,
|
||||
@@ -734,13 +671,6 @@ class Memory(MemoryBase):
|
||||
|
||||
if agent_id is not None and memory_type == MemoryType.PROCEDURAL.value:
|
||||
results = self._create_procedural_memory(messages, metadata=processed_metadata, prompt=prompt)
|
||||
scale_threshold_notice = detect_scale_threshold_from_add_result(self, results)
|
||||
if temporal_usage_notice:
|
||||
display_temporal_usage_notice(self, "sync", "add", *temporal_usage_notice)
|
||||
elif scale_threshold_notice:
|
||||
display_scale_threshold_notice(self, "sync", "add", *scale_threshold_notice)
|
||||
else:
|
||||
display_first_run_notice(self, "sync", "add")
|
||||
return results
|
||||
|
||||
if self.config.llm.config.get("enable_vision"):
|
||||
@@ -749,13 +679,6 @@ class Memory(MemoryBase):
|
||||
messages = parse_vision_messages(messages)
|
||||
|
||||
vector_store_result = self._add_to_vector_store(messages, processed_metadata, effective_filters, infer, prompt=prompt)
|
||||
scale_threshold_notice = detect_scale_threshold_from_add_result(self, vector_store_result)
|
||||
if temporal_usage_notice:
|
||||
display_temporal_usage_notice(self, "sync", "add", *temporal_usage_notice)
|
||||
elif scale_threshold_notice:
|
||||
display_scale_threshold_notice(self, "sync", "add", *scale_threshold_notice)
|
||||
else:
|
||||
display_first_run_notice(self, "sync", "add")
|
||||
return {"results": vector_store_result}
|
||||
|
||||
def _add_to_vector_store(self, messages, metadata, filters, infer, prompt=None):
|
||||
@@ -1082,7 +1005,6 @@ class Memory(MemoryBase):
|
||||
capture_event("mem0.get", self, {"memory_id": memory_id, "sync_type": "sync"})
|
||||
memory = self.vector_store.get(vector_id=memory_id)
|
||||
if not memory:
|
||||
display_first_run_notice(self, "sync", "get")
|
||||
return None
|
||||
|
||||
promoted_payload_keys = [
|
||||
@@ -1111,7 +1033,6 @@ class Memory(MemoryBase):
|
||||
if additional_metadata:
|
||||
result_item["metadata"] = additional_metadata
|
||||
|
||||
display_first_run_notice(self, "sync", "get")
|
||||
return result_item
|
||||
|
||||
def get_all(
|
||||
@@ -1167,7 +1088,6 @@ class Memory(MemoryBase):
|
||||
)
|
||||
|
||||
limit = top_k
|
||||
scale_threshold_notice = detect_scale_threshold_from_top_k(top_k)
|
||||
|
||||
keys, encoded_ids = process_telemetry_filters(effective_filters)
|
||||
capture_event(
|
||||
@@ -1176,10 +1096,6 @@ class Memory(MemoryBase):
|
||||
|
||||
all_memories_result = self._get_all_from_vector_store(effective_filters, limit)
|
||||
|
||||
if scale_threshold_notice:
|
||||
display_scale_threshold_notice(self, "sync", "get_all", *scale_threshold_notice)
|
||||
else:
|
||||
display_first_run_notice(self, "sync", "get_all")
|
||||
return {"results": all_memories_result}
|
||||
|
||||
def _get_all_from_vector_store(self, filters, limit):
|
||||
@@ -1238,7 +1154,6 @@ class Memory(MemoryBase):
|
||||
threshold: float = 0.1,
|
||||
rerank: bool = False,
|
||||
explain: bool = False,
|
||||
reference_date: Optional[Any] = None,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
@@ -1270,7 +1185,6 @@ class Memory(MemoryBase):
|
||||
threshold (float, optional): Minimum score for a memory to be included. Defaults to 0.1.
|
||||
rerank (bool, optional): Whether to rerank results. Defaults to False.
|
||||
explain (bool, optional): Whether to include score_details for each result. Defaults to False.
|
||||
reference_date (Any, optional): Platform-only temporal parameter. Not supported in OSS.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the search results under a "results" key.
|
||||
@@ -1280,16 +1194,12 @@ class Memory(MemoryBase):
|
||||
ValueError: If filters doesn't contain at least one of user_id, agent_id, run_id,
|
||||
or if threshold/top_k values are invalid.
|
||||
"""
|
||||
if reference_date is not None:
|
||||
raise ValueError(get_temporal_feature_error_message("sync", "search", "reference_date"))
|
||||
|
||||
# Reject top-level entity params - must use filters instead
|
||||
_reject_top_level_entity_params(kwargs, "search")
|
||||
|
||||
# Validate search parameters (before applying defaults)
|
||||
_validate_search_params(threshold=threshold, top_k=top_k)
|
||||
query = _validate_and_trim_search_query(query)
|
||||
temporal_usage_notice = detect_temporal_usage_from_search(query, filters)
|
||||
|
||||
# Validate and trim entity IDs in filters
|
||||
effective_filters = filters.copy() if filters else {}
|
||||
@@ -1312,7 +1222,6 @@ class Memory(MemoryBase):
|
||||
)
|
||||
|
||||
limit = top_k
|
||||
scale_threshold_notice = detect_scale_threshold_from_top_k(top_k)
|
||||
|
||||
# Apply enhanced metadata filtering if advanced operators are detected
|
||||
if self._has_advanced_operators(effective_filters):
|
||||
@@ -1341,9 +1250,7 @@ class Memory(MemoryBase):
|
||||
},
|
||||
)
|
||||
|
||||
search_start = time.perf_counter()
|
||||
original_memories = self._search_vector_store(query, effective_filters, limit, threshold, explain=explain)
|
||||
search_elapsed_seconds = time.perf_counter() - search_start
|
||||
|
||||
# Apply reranking if enabled and reranker is available
|
||||
if rerank and self.reranker and original_memories:
|
||||
@@ -1353,21 +1260,6 @@ class Memory(MemoryBase):
|
||||
except Exception as e:
|
||||
logger.warning(f"Reranking failed, using original results: {e}")
|
||||
|
||||
if temporal_usage_notice:
|
||||
display_temporal_usage_notice(self, "sync", "search", *temporal_usage_notice)
|
||||
elif scale_threshold_notice:
|
||||
display_scale_threshold_notice(self, "sync", "search", *scale_threshold_notice)
|
||||
elif search_elapsed_seconds > PERFORMANCE_SLOW_QUERY_THRESHOLD_SECONDS:
|
||||
display_performance_slow_query_notice(
|
||||
self,
|
||||
"sync",
|
||||
"search",
|
||||
search_elapsed_seconds,
|
||||
top_k,
|
||||
len(original_memories),
|
||||
)
|
||||
else:
|
||||
display_first_run_notice(self, "sync", "search")
|
||||
return {"results": original_memories}
|
||||
|
||||
def _process_metadata_filters(self, metadata_filters: Dict[str, Any]) -> Dict[str, Any]:
|
||||
@@ -1677,7 +1569,6 @@ class Memory(MemoryBase):
|
||||
existing_embeddings = {data: self.embedding_model.embed(data, "update")}
|
||||
|
||||
self._update_memory(memory_id, data, existing_embeddings, metadata)
|
||||
display_first_run_notice(self, "sync", "update")
|
||||
return {"message": "Memory updated successfully!"}
|
||||
|
||||
def delete(self, memory_id):
|
||||
@@ -1694,11 +1585,6 @@ class Memory(MemoryBase):
|
||||
raise ValueError(f"Memory with id {memory_id} not found")
|
||||
|
||||
self._delete_memory(memory_id, existing_memory)
|
||||
decay_usage_notice = detect_decay_usage_from_delete()
|
||||
if decay_usage_notice:
|
||||
display_decay_usage_notice(self, "sync", "delete", *decay_usage_notice)
|
||||
else:
|
||||
display_first_run_notice(self, "sync", "delete")
|
||||
return {"message": "Memory deleted successfully!"}
|
||||
|
||||
def delete_all(self, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None):
|
||||
@@ -1732,11 +1618,6 @@ class Memory(MemoryBase):
|
||||
|
||||
logger.info(f"Deleted {len(memories)} memories")
|
||||
|
||||
decay_usage_notice = detect_decay_usage_from_delete_all(len(memories))
|
||||
if decay_usage_notice:
|
||||
display_decay_usage_notice(self, "sync", "delete_all", *decay_usage_notice)
|
||||
else:
|
||||
display_first_run_notice(self, "sync", "delete_all")
|
||||
return {"message": "Memories deleted successfully!"}
|
||||
|
||||
def history(self, memory_id):
|
||||
@@ -1750,9 +1631,7 @@ class Memory(MemoryBase):
|
||||
list: List of changes for the memory.
|
||||
"""
|
||||
capture_event("mem0.history", self, {"memory_id": memory_id, "sync_type": "sync"})
|
||||
history = self.db.get_history(memory_id)
|
||||
display_first_run_notice(self, "sync", "history")
|
||||
return history
|
||||
return self.db.get_history(memory_id)
|
||||
|
||||
def _create_memory(self, data, existing_embeddings, metadata=None):
|
||||
logger.debug(f"Creating memory with {data=}")
|
||||
@@ -1952,7 +1831,6 @@ class Memory(MemoryBase):
|
||||
self._entity_store = None
|
||||
|
||||
capture_event("mem0.reset", self, {"sync_type": "sync"})
|
||||
display_first_run_notice(self, "sync", "reset")
|
||||
|
||||
def close(self):
|
||||
"""Release resources held by this Memory instance (SQLite connections, etc.)."""
|
||||
@@ -2011,10 +1889,6 @@ class AsyncMemory(MemoryBase):
|
||||
|
||||
capture_event("mem0.init", self, {"sync_type": "async"})
|
||||
|
||||
@property
|
||||
def project(self):
|
||||
return _AsyncOSSProject()
|
||||
|
||||
@property
|
||||
def entity_store(self):
|
||||
"""Lazily initialize entity store on first use."""
|
||||
@@ -2184,7 +2058,6 @@ class AsyncMemory(MemoryBase):
|
||||
agent_id: Optional[str] = None,
|
||||
run_id: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None,
|
||||
timestamp: Optional[Any] = None,
|
||||
infer: bool = True,
|
||||
memory_type: Optional[str] = None,
|
||||
prompt: Optional[str] = None,
|
||||
@@ -2199,7 +2072,6 @@ class AsyncMemory(MemoryBase):
|
||||
agent_id (str, optional): ID of the agent creating the memory. Defaults to None.
|
||||
run_id (str, optional): ID of the run creating the memory. Defaults to None.
|
||||
metadata (dict, optional): Metadata to store with the memory. Defaults to None.
|
||||
timestamp (Any, optional): Platform-only temporal parameter. Not supported in OSS.
|
||||
infer (bool, optional): Whether to infer the memories. Defaults to True.
|
||||
memory_type (str, optional): Type of memory to create. Defaults to None.
|
||||
Pass "procedural_memory" to create procedural memories.
|
||||
@@ -2208,10 +2080,6 @@ class AsyncMemory(MemoryBase):
|
||||
Returns:
|
||||
dict: A dictionary containing the result of the memory addition operation.
|
||||
"""
|
||||
if timestamp is not None:
|
||||
raise ValueError(await get_temporal_feature_error_message_async("async", "add", "timestamp"))
|
||||
|
||||
temporal_usage_notice = detect_temporal_usage_from_metadata(metadata)
|
||||
processed_metadata, effective_filters = _build_filters_and_metadata(
|
||||
user_id=user_id, agent_id=agent_id, run_id=run_id, input_metadata=metadata
|
||||
)
|
||||
@@ -2239,13 +2107,6 @@ class AsyncMemory(MemoryBase):
|
||||
results = await self._create_procedural_memory(
|
||||
messages, metadata=processed_metadata, prompt=prompt, llm=llm
|
||||
)
|
||||
scale_threshold_notice = await asyncio.to_thread(detect_scale_threshold_from_add_result, self, results)
|
||||
if temporal_usage_notice:
|
||||
await display_temporal_usage_notice_async(self, "async", "add", *temporal_usage_notice)
|
||||
elif scale_threshold_notice:
|
||||
await display_scale_threshold_notice_async(self, "async", "add", *scale_threshold_notice)
|
||||
else:
|
||||
await display_first_run_notice_async(self, "async", "add")
|
||||
return results
|
||||
|
||||
if self.config.llm.config.get("enable_vision"):
|
||||
@@ -2254,13 +2115,6 @@ class AsyncMemory(MemoryBase):
|
||||
messages = parse_vision_messages(messages)
|
||||
|
||||
vector_store_result = await self._add_to_vector_store(messages, processed_metadata, effective_filters, infer, prompt=prompt)
|
||||
scale_threshold_notice = await asyncio.to_thread(detect_scale_threshold_from_add_result, self, vector_store_result)
|
||||
if temporal_usage_notice:
|
||||
await display_temporal_usage_notice_async(self, "async", "add", *temporal_usage_notice)
|
||||
elif scale_threshold_notice:
|
||||
await display_scale_threshold_notice_async(self, "async", "add", *scale_threshold_notice)
|
||||
else:
|
||||
await display_first_run_notice_async(self, "async", "add")
|
||||
return {"results": vector_store_result}
|
||||
|
||||
async def _add_to_vector_store(
|
||||
@@ -2594,7 +2448,6 @@ class AsyncMemory(MemoryBase):
|
||||
capture_event("mem0.get", self, {"memory_id": memory_id, "sync_type": "async"})
|
||||
memory = await asyncio.to_thread(self.vector_store.get, vector_id=memory_id)
|
||||
if not memory:
|
||||
await display_first_run_notice_async(self, "async", "get")
|
||||
return None
|
||||
|
||||
promoted_payload_keys = [
|
||||
@@ -2623,7 +2476,6 @@ class AsyncMemory(MemoryBase):
|
||||
if additional_metadata:
|
||||
result_item["metadata"] = additional_metadata
|
||||
|
||||
await display_first_run_notice_async(self, "async", "get")
|
||||
return result_item
|
||||
|
||||
async def get_all(
|
||||
@@ -2679,7 +2531,6 @@ class AsyncMemory(MemoryBase):
|
||||
)
|
||||
|
||||
limit = top_k
|
||||
scale_threshold_notice = detect_scale_threshold_from_top_k(top_k)
|
||||
|
||||
keys, encoded_ids = process_telemetry_filters(effective_filters)
|
||||
capture_event(
|
||||
@@ -2688,10 +2539,6 @@ class AsyncMemory(MemoryBase):
|
||||
|
||||
all_memories_result = await self._get_all_from_vector_store(effective_filters, limit)
|
||||
|
||||
if scale_threshold_notice:
|
||||
await display_scale_threshold_notice_async(self, "async", "get_all", *scale_threshold_notice)
|
||||
else:
|
||||
await display_first_run_notice_async(self, "async", "get_all")
|
||||
return {"results": all_memories_result}
|
||||
|
||||
async def _get_all_from_vector_store(self, filters, limit):
|
||||
@@ -2750,7 +2597,6 @@ class AsyncMemory(MemoryBase):
|
||||
threshold: float = 0.1,
|
||||
rerank: bool = False,
|
||||
explain: bool = False,
|
||||
reference_date: Optional[Any] = None,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
@@ -2782,7 +2628,6 @@ class AsyncMemory(MemoryBase):
|
||||
threshold (float, optional): Minimum score for a memory to be included. Defaults to 0.1.
|
||||
rerank (bool, optional): Whether to rerank results. Defaults to False.
|
||||
explain (bool, optional): Whether to include score_details for each result. Defaults to False.
|
||||
reference_date (Any, optional): Platform-only temporal parameter. Not supported in OSS.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the search results under a "results" key.
|
||||
@@ -2792,18 +2637,12 @@ class AsyncMemory(MemoryBase):
|
||||
ValueError: If filters doesn't contain at least one of user_id, agent_id, run_id,
|
||||
or if threshold/top_k values are invalid.
|
||||
"""
|
||||
if reference_date is not None:
|
||||
raise ValueError(
|
||||
await get_temporal_feature_error_message_async("async", "search", "reference_date")
|
||||
)
|
||||
|
||||
# Reject top-level entity params - must use filters instead
|
||||
_reject_top_level_entity_params(kwargs, "search")
|
||||
|
||||
# Validate search parameters (before applying defaults)
|
||||
_validate_search_params(threshold=threshold, top_k=top_k)
|
||||
query = _validate_and_trim_search_query(query)
|
||||
temporal_usage_notice = detect_temporal_usage_from_search(query, filters)
|
||||
|
||||
# Validate and trim entity IDs in filters
|
||||
effective_filters = filters.copy() if filters else {}
|
||||
@@ -2828,7 +2667,6 @@ class AsyncMemory(MemoryBase):
|
||||
)
|
||||
|
||||
limit = top_k
|
||||
scale_threshold_notice = detect_scale_threshold_from_top_k(top_k)
|
||||
|
||||
# Apply enhanced metadata filtering if advanced operators are detected
|
||||
if self._has_advanced_operators(effective_filters):
|
||||
@@ -2857,9 +2695,7 @@ class AsyncMemory(MemoryBase):
|
||||
},
|
||||
)
|
||||
|
||||
search_start = time.perf_counter()
|
||||
original_memories = await self._search_vector_store(query, effective_filters, limit, threshold, explain=explain)
|
||||
search_elapsed_seconds = time.perf_counter() - search_start
|
||||
|
||||
# Apply reranking if enabled and reranker is available
|
||||
if rerank and self.reranker and original_memories:
|
||||
@@ -2872,21 +2708,6 @@ class AsyncMemory(MemoryBase):
|
||||
except Exception as e:
|
||||
logger.warning(f"Reranking failed, using original results: {e}")
|
||||
|
||||
if temporal_usage_notice:
|
||||
await display_temporal_usage_notice_async(self, "async", "search", *temporal_usage_notice)
|
||||
elif scale_threshold_notice:
|
||||
await display_scale_threshold_notice_async(self, "async", "search", *scale_threshold_notice)
|
||||
elif search_elapsed_seconds > PERFORMANCE_SLOW_QUERY_THRESHOLD_SECONDS:
|
||||
await display_performance_slow_query_notice_async(
|
||||
self,
|
||||
"async",
|
||||
"search",
|
||||
search_elapsed_seconds,
|
||||
top_k,
|
||||
len(original_memories),
|
||||
)
|
||||
else:
|
||||
await display_first_run_notice_async(self, "async", "search")
|
||||
return {"results": original_memories}
|
||||
|
||||
def _process_metadata_filters(self, metadata_filters: Dict[str, Any]) -> Dict[str, Any]:
|
||||
@@ -3187,7 +3008,6 @@ class AsyncMemory(MemoryBase):
|
||||
existing_embeddings = {data: embeddings}
|
||||
|
||||
await self._update_memory(memory_id, data, existing_embeddings, metadata)
|
||||
await display_first_run_notice_async(self, "async", "update")
|
||||
return {"message": "Memory updated successfully!"}
|
||||
|
||||
async def delete(self, memory_id):
|
||||
@@ -3204,11 +3024,6 @@ class AsyncMemory(MemoryBase):
|
||||
raise ValueError(f"Memory with id {memory_id} not found")
|
||||
|
||||
await self._delete_memory(memory_id, existing_memory)
|
||||
decay_usage_notice = detect_decay_usage_from_delete()
|
||||
if decay_usage_notice:
|
||||
await display_decay_usage_notice_async(self, "async", "delete", *decay_usage_notice)
|
||||
else:
|
||||
await display_first_run_notice_async(self, "async", "delete")
|
||||
return {"message": "Memory deleted successfully!"}
|
||||
|
||||
async def delete_all(self, user_id=None, agent_id=None, run_id=None):
|
||||
@@ -3245,11 +3060,6 @@ class AsyncMemory(MemoryBase):
|
||||
|
||||
logger.info(f"Deleted {len(memories[0])} memories")
|
||||
|
||||
decay_usage_notice = detect_decay_usage_from_delete_all(len(memories[0]))
|
||||
if decay_usage_notice:
|
||||
await display_decay_usage_notice_async(self, "async", "delete_all", *decay_usage_notice)
|
||||
else:
|
||||
await display_first_run_notice_async(self, "async", "delete_all")
|
||||
return {"message": "Memories deleted successfully!"}
|
||||
|
||||
async def history(self, memory_id):
|
||||
@@ -3263,9 +3073,7 @@ class AsyncMemory(MemoryBase):
|
||||
list: List of changes for the memory.
|
||||
"""
|
||||
capture_event("mem0.history", self, {"memory_id": memory_id, "sync_type": "async"})
|
||||
history = await asyncio.to_thread(self.db.get_history, memory_id)
|
||||
await display_first_run_notice_async(self, "async", "history")
|
||||
return history
|
||||
return await asyncio.to_thread(self.db.get_history, memory_id)
|
||||
|
||||
async def _create_memory(self, data, existing_embeddings, metadata=None):
|
||||
logger.debug(f"Creating memory with {data=}")
|
||||
@@ -3483,7 +3291,6 @@ class AsyncMemory(MemoryBase):
|
||||
)
|
||||
|
||||
capture_event("mem0.reset", self, {"sync_type": "async"})
|
||||
await display_first_run_notice_async(self, "async", "reset")
|
||||
|
||||
def close(self):
|
||||
"""Release resources held by this AsyncMemory instance."""
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
+1
-13
@@ -1,7 +1,6 @@
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import tempfile
|
||||
import uuid
|
||||
from hashlib import sha256
|
||||
|
||||
@@ -35,21 +34,10 @@ def _load_config():
|
||||
def _write_config(config):
|
||||
"""Best-effort write of ~/.mem0/config.json. Never raises."""
|
||||
path = _config_path()
|
||||
temp_path = None
|
||||
try:
|
||||
os.makedirs(os.path.dirname(path), exist_ok=True)
|
||||
with tempfile.NamedTemporaryFile("w", dir=os.path.dirname(path), delete=False) as f:
|
||||
temp_path = f.name
|
||||
with open(path, "w") as f:
|
||||
json.dump(config, f, indent=4)
|
||||
f.flush()
|
||||
os.fsync(f.fileno())
|
||||
os.replace(temp_path, path)
|
||||
except Exception as e:
|
||||
if temp_path:
|
||||
try:
|
||||
os.unlink(temp_path)
|
||||
except OSError:
|
||||
pass
|
||||
_logger.debug("Failed to write mem0 config %s: %s", path, e)
|
||||
|
||||
|
||||
|
||||
@@ -14,7 +14,6 @@ from mem0.memory.setup import get_or_create_user_id
|
||||
MEM0_TELEMETRY = os.environ.get("MEM0_TELEMETRY", "True")
|
||||
PROJECT_API_KEY = "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX"
|
||||
HOST = "https://us.i.posthog.com"
|
||||
FEATURE_FLAGS_REQUEST_TIMEOUT_SECONDS = 0.5
|
||||
|
||||
if isinstance(MEM0_TELEMETRY, str):
|
||||
MEM0_TELEMETRY = MEM0_TELEMETRY.lower() in ("true", "1", "yes")
|
||||
@@ -50,9 +49,7 @@ MEM0_TELEMETRY_SAMPLE_RATE = _parse_sample_rate(os.environ.get("MEM0_TELEMETRY_S
|
||||
# Events that bypass sampling and always fire. Keep this set in sync with the
|
||||
# event names passed to capture_event() in mem0/memory/main.py.
|
||||
# $identify is included so PostHog person-merging is never lost to sampling.
|
||||
_LIFECYCLE_EVENTS = frozenset(
|
||||
{"mem0.init", "mem0.reset", "mem0._create_procedural_memory", "mem0.notice_displayed", "$identify"}
|
||||
)
|
||||
_LIFECYCLE_EVENTS = frozenset({"mem0.init", "mem0.reset", "mem0._create_procedural_memory", "$identify"})
|
||||
|
||||
|
||||
def _sampling_before_send(msg):
|
||||
@@ -80,15 +77,15 @@ class AnonymousTelemetry:
|
||||
self.user_id = None
|
||||
return
|
||||
|
||||
self.posthog = Posthog(
|
||||
project_api_key=PROJECT_API_KEY,
|
||||
host=HOST,
|
||||
before_send=before_send,
|
||||
feature_flags_request_timeout_seconds=FEATURE_FLAGS_REQUEST_TIMEOUT_SECONDS,
|
||||
)
|
||||
try:
|
||||
self.posthog = Posthog(project_api_key=PROJECT_API_KEY, host=HOST, before_send=before_send)
|
||||
except TypeError:
|
||||
# posthog <4.5.0 does not accept before_send; fall back without sampling.
|
||||
_logger.debug("posthog.Posthog does not accept before_send; upgrade to >=4.5.0 for sampling")
|
||||
self.posthog = Posthog(project_api_key=PROJECT_API_KEY, host=HOST)
|
||||
self.user_id = get_or_create_user_id(vector_store)
|
||||
|
||||
def capture_event(self, event_name, properties=None, user_email=None, flags=None):
|
||||
def capture_event(self, event_name, properties=None, user_email=None):
|
||||
if self.posthog is None:
|
||||
return
|
||||
|
||||
@@ -112,10 +109,7 @@ class AnonymousTelemetry:
|
||||
**properties,
|
||||
}
|
||||
try:
|
||||
capture_kwargs = {"distinct_id": distinct_id, "properties": properties}
|
||||
if flags is not None:
|
||||
capture_kwargs["flags"] = flags
|
||||
self.posthog.capture(event_name, **capture_kwargs)
|
||||
self.posthog.capture(distinct_id=distinct_id, event=event_name, properties=properties)
|
||||
except Exception as e:
|
||||
_logger.debug("Failed to capture telemetry event %r: %s", event_name, e)
|
||||
|
||||
|
||||
+4
-13
@@ -179,20 +179,11 @@ def parse_vision_messages(messages, llm=None, vision_details="auto"):
|
||||
|
||||
# Handle message content
|
||||
if isinstance(msg["content"], list):
|
||||
if llm is None:
|
||||
text_parts = [
|
||||
part["text"] for part in msg["content"]
|
||||
if isinstance(part, dict) and part.get("type") == "text"
|
||||
]
|
||||
if not text_parts:
|
||||
continue
|
||||
returned_messages.append({"role": msg["role"], "content": " ".join(text_parts)})
|
||||
else:
|
||||
description = get_image_description(msg, llm, vision_details)
|
||||
returned_messages.append({"role": msg["role"], "content": description})
|
||||
# Multiple image URLs in content
|
||||
description = get_image_description(msg, llm, vision_details)
|
||||
returned_messages.append({"role": msg["role"], "content": description})
|
||||
elif isinstance(msg["content"], dict) and msg["content"].get("type") == "image_url":
|
||||
if llm is None:
|
||||
continue
|
||||
# Single image content
|
||||
image_url = msg["content"]["image_url"]["url"]
|
||||
try:
|
||||
description = get_image_description(image_url, llm, vision_details)
|
||||
|
||||
@@ -37,7 +37,6 @@ class Qdrant(VectorStoreBase):
|
||||
path: str = None,
|
||||
url: str = None,
|
||||
api_key: str = None,
|
||||
https: bool | None = None,
|
||||
on_disk: bool = False,
|
||||
):
|
||||
"""
|
||||
@@ -52,8 +51,6 @@ class Qdrant(VectorStoreBase):
|
||||
path (str, optional): Path for local Qdrant database. Defaults to None.
|
||||
url (str, optional): Full URL for Qdrant server. Defaults to None.
|
||||
api_key (str, optional): API key for Qdrant server. Defaults to None.
|
||||
https (bool, optional): Whether to force HTTPS on or off. Explicit schemes in url take precedence.
|
||||
Defaults to None.
|
||||
on_disk (bool, optional): Enables persistent storage. Vectors are stored on disk (True) or in memory (False).
|
||||
Does not delete the local database path. Defaults to False.
|
||||
"""
|
||||
@@ -69,8 +66,6 @@ class Qdrant(VectorStoreBase):
|
||||
if host and port:
|
||||
params["host"] = host
|
||||
params["port"] = port
|
||||
if https is not None:
|
||||
params["https"] = https
|
||||
|
||||
if not params:
|
||||
params["path"] = path
|
||||
|
||||
Generated
+9
-9
@@ -3494,15 +3494,15 @@ pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4,<14.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "litellm"
|
||||
version = "1.83.7"
|
||||
version = "1.88.1"
|
||||
description = "Library to easily interface with LLM API providers"
|
||||
optional = true
|
||||
python-versions = "<4.0,>=3.9"
|
||||
python-versions = "<3.14,>=3.10"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"llms\""
|
||||
files = [
|
||||
{file = "litellm-1.83.7-py3-none-any.whl", hash = "sha256:5784a1d9a9a4a8acd6ca1e347003a5e2e1b3c749b4d41e7da4904577adade111"},
|
||||
{file = "litellm-1.83.7.tar.gz", hash = "sha256:e2f2cb99df2e2b2eab63f1354faa45c88dd7c8d40c18eb648afb1b349c689633"},
|
||||
{file = "litellm-1.88.1-py3-none-any.whl", hash = "sha256:369b84e57d9426582ddc35e731956ddb6618cda97cc44e4e4d2dfa75982a6e3a"},
|
||||
{file = "litellm-1.88.1.tar.gz", hash = "sha256:89c6b74cc7912d6365793006ff951c0450fe847625008dfe49de8a7dc4529aa5"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -3515,7 +3515,7 @@ jinja2 = "3.1.6"
|
||||
jsonschema = "4.23.0"
|
||||
openai = "2.30.0"
|
||||
pydantic = "2.12.5"
|
||||
python-dotenv = "1.0.1"
|
||||
python-dotenv = "1.2.2"
|
||||
tiktoken = "0.12.0"
|
||||
tokenizers = "0.22.2"
|
||||
|
||||
@@ -6866,15 +6866,15 @@ six = ">=1.5"
|
||||
|
||||
[[package]]
|
||||
name = "python-dotenv"
|
||||
version = "1.0.1"
|
||||
version = "1.2.2"
|
||||
description = "Read key-value pairs from a .env file and set them as environment variables"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
python-versions = ">=3.10"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"vector-stores\" or extra == \"llms\" or extra == \"extras\""
|
||||
files = [
|
||||
{file = "python-dotenv-1.0.1.tar.gz", hash = "sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca"},
|
||||
{file = "python_dotenv-1.0.1-py3-none-any.whl", hash = "sha256:f7b63ef50f1b690dddf550d03497b66d609393b40b564ed0d674909a68ebf16a"},
|
||||
{file = "python_dotenv-1.2.2-py3-none-any.whl", hash = "sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a"},
|
||||
{file = "python_dotenv-1.2.2.tar.gz", hash = "sha256:2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
||||
+3
-3
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
|
||||
|
||||
[project]
|
||||
name = "mem0ai"
|
||||
version = "2.0.7"
|
||||
version = "2.0.5"
|
||||
description = "Long-term memory for AI Agents"
|
||||
authors = [
|
||||
{ name = "Mem0", email = "support@mem0.ai" }
|
||||
@@ -17,7 +17,7 @@ dependencies = [
|
||||
"qdrant-client>=1.12.0",
|
||||
"pydantic>=2.7.3",
|
||||
"openai>=1.90.0",
|
||||
"posthog>=7.14.0",
|
||||
"posthog>=4.5.0",
|
||||
"pytz>=2024.1",
|
||||
"sqlalchemy>=2.0.31",
|
||||
"protobuf>=5.29.6,<7.0.0",
|
||||
@@ -55,7 +55,7 @@ vector_stores = [
|
||||
llms = [
|
||||
"groq>=0.3.0",
|
||||
"together>=0.2.10",
|
||||
"litellm>=1.83.7",
|
||||
"litellm>=1.88.1",
|
||||
"openai>=1.90.0",
|
||||
"ollama>=0.3.0",
|
||||
"vertexai>=0.1.0",
|
||||
|
||||
+1
-3
@@ -22,9 +22,8 @@ Invoked as a slash command to execute a specific end-to-end workflow. These do r
|
||||
|-------|---------|---------|
|
||||
| [`mem0-integrate`](./mem0-integrate/) | `/mem0-integrate` — wire Mem0 into an existing repo via TDD | `npx skills add https://github.com/mem0ai/mem0 --skill mem0-integrate` |
|
||||
| [`mem0-test-integration`](./mem0-test-integration/) | `/mem0-test-integration` — verify what `/mem0-integrate` produced | `npx skills add https://github.com/mem0ai/mem0 --skill mem0-test-integration` |
|
||||
| [`mem0-oss-to-platform`](./mem0-oss-to-platform/) | `/mem0-oss-to-platform` — migrate a project from Mem0 OSS to the hosted Platform SDK | `npx skills add https://github.com/mem0ai/mem0 --skill mem0-oss-to-platform` |
|
||||
|
||||
The `mem0-integrate` and `mem0-test-integration` skills are designed to run in sequence on the same workspace:
|
||||
The two pipeline skills are designed to run in sequence on the same workspace:
|
||||
|
||||
```
|
||||
/mem0-integrate → mem0-integrate/<slug> branch + .mem0-integration/ artifacts
|
||||
@@ -37,7 +36,6 @@ The `mem0-integrate` and `mem0-test-integration` skills are designed to run in s
|
||||
- **Using the terminal CLI?** → `mem0-cli`
|
||||
- **Building with `@ai-sdk/*`?** → `mem0-vercel-ai-sdk`
|
||||
- **Want the assistant to wire Mem0 into an existing repo for you?** → `mem0-integrate`, then `mem0-test-integration`
|
||||
- **Already using Mem0 OSS and want to move to the hosted Platform?** → `mem0-oss-to-platform`
|
||||
|
||||
## Links
|
||||
|
||||
|
||||
@@ -1,189 +0,0 @@
|
||||
Apache License
|
||||
Version 2.0, January 2004
|
||||
http://www.apache.org/licenses/
|
||||
|
||||
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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||||
|
||||
1. Definitions.
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||||
|
||||
"License" shall mean the terms and conditions for use, reproduction,
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END OF TERMS AND CONDITIONS
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Copyright 2024 Mem0.ai
|
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|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
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you may not use this file except in compliance with the License.
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||||
You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software
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|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
@@ -1,79 +0,0 @@
|
||||
# mem0-oss-to-platform — Pipeline Skill
|
||||
|
||||
Migrate a project from the Mem0 Open Source (self-hosted) SDK to the Mem0 Platform (hosted) SDK, end to end. The skill audits where Mem0 is used, writes a reviewable migration plan, and executes it after you approve.
|
||||
|
||||
> **This is a pipeline skill, not a reference skill.** Invoke it when you want your agent to migrate an existing project's Mem0 integration from OSS to the Platform. For day-to-day SDK coding help, install [`mem0`](../mem0/SKILL.md) instead.
|
||||
>
|
||||
> **Part of the Mem0 Skill Graph:**
|
||||
> - Reference: [mem0](../mem0/SKILL.md) · [mem0-cli](../mem0-cli/SKILL.md) · [mem0-vercel-ai-sdk](../mem0-vercel-ai-sdk/SKILL.md)
|
||||
> - Pipeline: [mem0-integrate](../mem0-integrate/SKILL.md) → [mem0-test-integration](../mem0-test-integration/SKILL.md) · **mem0-oss-to-platform** (this skill)
|
||||
|
||||
## What This Skill Does
|
||||
|
||||
When invoked, your assistant will:
|
||||
|
||||
- **Discover** every place Mem0 is used in the project — imports, client init, config blocks, call sites, dependencies, env, and local infra
|
||||
- **Verify** the exact API against the installed SDK rather than guessing
|
||||
- **Map** each OSS `Memory` usage to its hosted `MemoryClient` equivalent (Python and TypeScript)
|
||||
- **Flag** everything that isn't a clean 1:1 and needs a human decision
|
||||
- **Write** a reviewable `MEM0_MIGRATION_PLAN.md`, then **execute it after you approve** — strictly scoped to the Mem0 integration, with no unrelated refactors
|
||||
|
||||
## When to Use
|
||||
|
||||
Trigger phrases:
|
||||
|
||||
- "Migrate my Mem0 setup to the Platform"
|
||||
- "Switch from self-hosted Mem0 to MemoryClient"
|
||||
- "Use my Mem0 API key instead of a local Qdrant"
|
||||
- "Move Mem0 to the hosted/managed service"
|
||||
|
||||
Do **not** use this skill for general SDK usage (install [`mem0`](../mem0/SKILL.md)), or to add Mem0 to a repo that doesn't use it yet (use [`mem0-integrate`](../mem0-integrate/SKILL.md)).
|
||||
|
||||
## Installation
|
||||
|
||||
### CLI (Claude Code, Codex, OpenCode, OpenClaw, or any tool that supports skills)
|
||||
|
||||
```bash
|
||||
npx skills add https://github.com/mem0ai/mem0 --skill mem0-oss-to-platform
|
||||
```
|
||||
|
||||
### Claude.ai
|
||||
|
||||
1. Download this `skills/mem0-oss-to-platform` folder as a ZIP
|
||||
2. Go to **Settings > Capabilities > Skills**
|
||||
3. Click **Upload skill** and select the ZIP
|
||||
|
||||
### Claude API (Skills API)
|
||||
|
||||
```bash
|
||||
curl -X POST https://api.anthropic.com/v1/skills \
|
||||
-H "x-api-key: $ANTHROPIC_API_KEY" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"name": "mem0-oss-to-platform", "source": "https://github.com/mem0ai/mem0/tree/main/skills/mem0-oss-to-platform"}'
|
||||
```
|
||||
|
||||
### Prerequisites
|
||||
|
||||
- A Mem0 Platform API key ([get one](https://app.mem0.ai/dashboard/api-keys))
|
||||
- An existing project that uses the Mem0 OSS SDK
|
||||
|
||||
## Workflow
|
||||
|
||||
```
|
||||
(invoke skill) → audits the repo's Mem0 usage,
|
||||
writes MEM0_MIGRATION_PLAN.md,
|
||||
stops for your review
|
||||
(approve) → executes the plan and verifies
|
||||
(compile/import, real-API smoke test)
|
||||
```
|
||||
|
||||
## Links
|
||||
|
||||
- [Mem0 Platform Dashboard](https://app.mem0.ai)
|
||||
- [Mem0 Documentation](https://docs.mem0.ai)
|
||||
- [OSS → Platform migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3)
|
||||
- [Platform vs OSS comparison](https://docs.mem0.ai/platform/platform-vs-oss)
|
||||
|
||||
## License
|
||||
|
||||
Apache-2.0
|
||||
@@ -1,120 +0,0 @@
|
||||
---
|
||||
name: mem0-oss-to-platform
|
||||
description: >-
|
||||
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK
|
||||
(the local `Memory` class) to the mem0 Platform / hosted / managed SDK (the `MemoryClient`
|
||||
class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off
|
||||
OSS/self-hosted to the hosted API — e.g. "migrate my mem0 setup to the platform", "switch from
|
||||
self-hosted mem0 to MemoryClient", "use my mem0 API key instead of a local Qdrant", "move mem0
|
||||
to the cloud/hosted/managed service", or "replace my local mem0 vector store + embedder config
|
||||
with the platform". Applies to Python (`from mem0 import Memory` → `from mem0 import MemoryClient`)
|
||||
and TypeScript/JavaScript (`import { Memory } from "mem0ai/oss"` → `import MemoryClient from "mem0ai"`).
|
||||
Trigger even when the user doesn't say the word "migrate" but clearly wants their existing mem0
|
||||
integration to run against the hosted platform. It first produces a reviewable migration plan,
|
||||
then executes it after the developer approves. Strictly scoped to the mem0 integration — it does
|
||||
not refactor, restructure, or "improve" any unrelated code.
|
||||
---
|
||||
|
||||
# Migrate mem0 OSS → mem0 Platform (hosted)
|
||||
|
||||
This skill migrates a project's memory layer from the **self-hosted mem0 OSS SDK** to the
|
||||
**hosted mem0 Platform SDK**, working for any project shape — an agent, a RAG pipeline, an API
|
||||
service, a chatbot, a background worker. You discover where mem0 is actually used, write a plan the
|
||||
developer reviews, and then execute it on approval.
|
||||
|
||||
## The mental model (read this first — it's why the migration is shaped the way it is)
|
||||
|
||||
OSS mem0 means **the developer runs the whole memory stack themselves**: a vector store
|
||||
(Qdrant/pgvector/Chroma/…), an embedder, an LLM for fact extraction, and a local history DB. All of
|
||||
that is wired up in a config object passed to `Memory`.
|
||||
|
||||
The Platform means **mem0 runs that stack for them**. The developer just holds an **API key**. So
|
||||
the migration is mostly *subtraction*: the local infrastructure config collapses into a single
|
||||
`MemoryClient(api_key=...)`. The method calls stay recognizable (`add`/`search`/`get_all`/…), but a
|
||||
few parameter conventions tighten up and the return values are server responses.
|
||||
|
||||
So the core of every migration is:
|
||||
1. `Memory` / `Memory.from_config({...})` → `MemoryClient()` (reads the API key from the env).
|
||||
2. Delete the local `vector_store` / `llm` / `embedder` / `graph_store` / `history_db_path` config.
|
||||
3. Fix up each call site to the hosted call convention (entity IDs into `filters`, pagination, etc.).
|
||||
4. Flag everything that *isn't* a clean 1:1 so the developer can decide (see `references/gotchas.md`).
|
||||
|
||||
**Scope discipline:** touch only mem0-related code, config, dependencies, and env. Preserve the
|
||||
project's existing behavior, structure, and style. Do not rename things, "tidy" nearby code, or
|
||||
change the app's logic. The developer asked to swap a backend, not to refactor their project.
|
||||
|
||||
## Workflow
|
||||
|
||||
Work through these phases in order. Phases 1–4 produce the plan; phase 5 runs only after approval.
|
||||
|
||||
### Phase 0 — Prerequisite check
|
||||
The hosted SDK needs a mem0 API key (`MEM0_API_KEY`, obtainable at https://app.mem0.ai). Confirm
|
||||
the developer has one. You don't need the key value to write the plan, but flag in the plan that it
|
||||
must be set (in `.env` / secrets manager, never hardcoded) before execution and verification.
|
||||
|
||||
### Phase 1 — Discover the mem0 footprint
|
||||
Do not assume the layout. Find every place mem0 appears. Detect the language and the **installed**
|
||||
version first, then sweep for usage. Concretely, search for:
|
||||
- **Imports / instantiation:** `from mem0 import Memory`, `Memory.from_config`, `Memory(`,
|
||||
`import ... from "mem0ai"`, `from "mem0ai/oss"`, `new Memory(`.
|
||||
- **Config blocks:** keys like `vector_store`/`vectorStore`, `embedder`, `llm`, `graph_store`/
|
||||
`graphStore`, `history_db_path`, `historyStore`, `custom_fact_extraction_prompt`,
|
||||
`custom_update_memory_prompt`, `enable_graph`.
|
||||
- **Every call site:** `.add(`, `.search(`, `.get_all(`/`.getAll(`, `.delete_all(`/`.deleteAll(`,
|
||||
`.get(`, `.update(`, `.delete(`, `.reset(`, `.history(`.
|
||||
- **Dependencies & env:** `requirements.txt`/`pyproject.toml`/`package.json` for `mem0ai` and any
|
||||
local-infra deps that exist *only* for mem0 (e.g. `qdrant-client`, `chromadb`); `.env`/config for
|
||||
things like `OPENAI_API_KEY` used by the local embedder/LLM; any docker-compose service (e.g. a
|
||||
Qdrant container) that exists only to back mem0.
|
||||
|
||||
Use Grep/Glob broadly; a single missed call site is a runtime break later. Record `file:line` for
|
||||
each finding — the plan's inventory is built from this.
|
||||
|
||||
### Phase 2 — Verify the API against the installed SDK (don't guess)
|
||||
Versions drift, and the OSS and hosted classes have subtly different signatures. Before mapping,
|
||||
confirm the **real** signatures of the installed package rather than trusting memory:
|
||||
- **Python:** `python -c "import inspect; from mem0 import MemoryClient; print(inspect.signature(MemoryClient.search))"`
|
||||
for each method you'll touch, and read the installed source under
|
||||
`site-packages/mem0/client/main.py` if anything is ambiguous (e.g. whether a method *rejects*
|
||||
top-level entity params). Also check the OSS side the project currently uses.
|
||||
- **TypeScript:** read the installed types/dist under `node_modules/mem0ai/` to confirm option names
|
||||
(`limit` vs `topK`, `userId` vs a nested `filters`) and the default vs `mem0ai/oss` export.
|
||||
|
||||
This verification step is the single most important habit — it's what keeps the plan correct across
|
||||
mem0 versions. Then consult `references/api-mapping.md` for the OSS→hosted translation of each
|
||||
method (Python and TypeScript), and the official guide at https://docs.mem0.ai/migration/oss-v2-to-v3.
|
||||
|
||||
### Phase 3 — Map each site and flag the gaps
|
||||
For every call site and config block from Phase 1, determine the hosted equivalent using the
|
||||
mapping. Most calls map cleanly. Some don't — and those matter more than the mechanical edits.
|
||||
Read `references/gotchas.md` and flag anything that needs a human decision: self-hosted/data-
|
||||
residency setups, local model choices moving server-side, graph-memory usage, custom prompts, hot-
|
||||
path calls that now make network round-trips, and **existing locally-stored memories not carrying
|
||||
over** (data migration is out of scope unless the developer asks — note it, don't silently attempt it).
|
||||
|
||||
### Phase 4 — Write the plan and stop
|
||||
Write the full plan to `MEM0_MIGRATION_PLAN.md` at the repo root, following the structure in
|
||||
`references/plan-template.md`. It must be concrete enough to execute from and honest about the gaps.
|
||||
Then **stop and present it for review.** Do not start editing code in the same turn — the whole
|
||||
point is that the developer reads and approves the plan first.
|
||||
|
||||
### Phase 5 — Execute on approval (guided)
|
||||
Once the developer approves (they may ask for changes first — incorporate them), execute the plan:
|
||||
- Make the edits file by file, staying strictly within mem0 scope.
|
||||
- Update dependencies and env (`MEM0_API_KEY`; remove now-dead local-infra deps/services only if
|
||||
they exist solely for mem0 and you're confident).
|
||||
- **Verify**, mirroring how you'd confirm any backend swap:
|
||||
- It imports / type-checks / byte-compiles.
|
||||
- A smoke test exercises `add` → `search`/`get_all` → `delete_all` against the hosted API with a
|
||||
real `MEM0_API_KEY`, and the app's own entry point still runs.
|
||||
- No local mem0 storage directory gets created anymore (e.g. a `.mem0/`, local Qdrant path) —
|
||||
proof the memory really lives on the platform now.
|
||||
- Report what changed, what was verified, and any flagged concerns the developer still needs to act
|
||||
on (e.g. configuring custom instructions in the dashboard, migrating old data).
|
||||
|
||||
## Reference files
|
||||
- `references/api-mapping.md` — exact OSS→hosted method/param/return mapping for Python and
|
||||
TypeScript, plus dependency and env changes. Read during Phase 2–3.
|
||||
- `references/gotchas.md` — the things that aren't a clean 1:1 and need a human decision. Read
|
||||
during Phase 3 so the plan's "Concerns" section is complete.
|
||||
- `references/plan-template.md` — the exact structure for `MEM0_MIGRATION_PLAN.md`. Use in Phase 4.
|
||||
@@ -1,117 +0,0 @@
|
||||
# OSS → Platform API mapping
|
||||
|
||||
Exact translation of the mem0 OSS (self-hosted `Memory`) API to the hosted `MemoryClient` API.
|
||||
**Always confirm against the installed package** (see SKILL.md Phase 2) — versions drift. The facts
|
||||
below match `mem0ai` 2.0.x (the v3 platform API) and the official guide:
|
||||
https://docs.mem0.ai/migration/oss-v2-to-v3
|
||||
|
||||
## Contents
|
||||
- [Python](#python)
|
||||
- [TypeScript / JavaScript](#typescript--javascript)
|
||||
- [Return shapes](#return-shapes)
|
||||
- [Dependencies & environment](#dependencies--environment)
|
||||
- [v2→v3 default/behavior changes](#v2v3-defaultbehavior-changes)
|
||||
|
||||
---
|
||||
|
||||
## Python
|
||||
|
||||
### Import & client construction
|
||||
```python
|
||||
# OSS (self-hosted)
|
||||
from mem0 import Memory
|
||||
memory = Memory() # or:
|
||||
memory = Memory.from_config({ # all of this local config disappears
|
||||
"vector_store": {...},
|
||||
"llm": {...},
|
||||
"embedder": {...},
|
||||
"history_db_path": "...",
|
||||
})
|
||||
|
||||
# Platform (hosted)
|
||||
from mem0 import MemoryClient
|
||||
memory = MemoryClient() # reads MEM0_API_KEY from the env
|
||||
# or: MemoryClient(api_key="...")
|
||||
```
|
||||
Notes:
|
||||
- The client reads `MEM0_API_KEY` from the environment when `api_key` is omitted.
|
||||
- **Drop** `vector_store`, `llm`, `embedder`, `graph_store`, `history_db_path` — these are managed
|
||||
server-side now.
|
||||
- **Drop** `org_id` / `project_id` constructor args if present — they're resolved from the API key
|
||||
in v3.
|
||||
- For async codebases, use `AsyncMemoryClient` (same methods, `await`-ed).
|
||||
|
||||
### Method calls
|
||||
| Operation | OSS `Memory` | Hosted `MemoryClient` |
|
||||
|---|---|---|
|
||||
| add | `memory.add(messages, user_id="u")` | `memory.add(messages, user_id="u")` — unchanged (top-level entity IDs accepted) |
|
||||
| search | `memory.search(q, user_id="u", limit=N)` *(older)* or `…, filters={"user_id":"u"}, top_k=N` *(newer)* | `memory.search(q, filters={"user_id": "u"}, top_k=N)` — entity IDs **must** be inside `filters`; top-level `user_id`/`agent_id`/`app_id`/`run_id` raise `ValueError` |
|
||||
| get_all | `memory.get_all(user_id="u")` or `…, filters={"user_id":"u"}` | `memory.get_all(filters={"user_id": "u"}, page=1, page_size=N)` — entity IDs in `filters`; paginated with `page`/`page_size` (**not** `top_k`) |
|
||||
| delete_all | `memory.delete_all(user_id="u")` | `memory.delete_all(user_id="u")` — unchanged |
|
||||
| get | `memory.get(memory_id)` | `memory.get(memory_id)` |
|
||||
| update | `memory.update(memory_id, data=...)` | `memory.update(memory_id, text=...)` — confirm param name against installed sig |
|
||||
| delete | `memory.delete(memory_id)` | `memory.delete(memory_id)` |
|
||||
| reset | `memory.reset()` (wipes the local store) | **No global reset.** Use `memory.delete_all(filters=...)` scoped to the relevant entity. Flag this. |
|
||||
|
||||
Key rule: for **search** and **get_all**, the hosted client requires entity IDs (`user_id`,
|
||||
`agent_id`, `app_id`, `run_id`) inside a `filters` dict and will raise if you pass them top-level.
|
||||
For **add** and **delete_all**, top-level entity IDs are accepted.
|
||||
|
||||
---
|
||||
|
||||
## TypeScript / JavaScript
|
||||
|
||||
The hosted and OSS SDKs ship in the same `mem0ai` npm package, distinguished by import path.
|
||||
Confirm option names against `node_modules/mem0ai/` types.
|
||||
|
||||
### Import & client construction
|
||||
```typescript
|
||||
// OSS (self-hosted) — note the "/oss" subpath
|
||||
import { Memory } from "mem0ai/oss";
|
||||
const memory = new Memory({ /* vectorStore, embedder, llm, historyStore … */ });
|
||||
|
||||
// Platform (hosted) — default export from the package root
|
||||
import MemoryClient from "mem0ai";
|
||||
const memory = new MemoryClient({ apiKey: process.env.MEM0_API_KEY });
|
||||
// Drop organizationId / projectId — resolved from the API key in v3.
|
||||
```
|
||||
|
||||
### Method calls (option-object differences)
|
||||
| Operation | OSS / old client | Hosted client (v3) |
|
||||
|---|---|---|
|
||||
| add | `memory.add(messages, { userId: "u" })` | `memory.add(messages, { userId: "u" })` — unchanged |
|
||||
| search | `memory.search(q, { userId: "u", limit: 20 })` | `memory.search(q, { filters: { userId: "u" }, topK: 20 })` — entity IDs into `filters`; `limit` → `topK` |
|
||||
| getAll | `memory.getAll({ userId: "u" })` | `memory.getAll({ filters: { userId: "u" } })` — entity IDs into `filters` |
|
||||
| deleteAll | `memory.deleteAll({ userId: "u" })` | `memory.deleteAll({ userId: "u" })` |
|
||||
| get / update / delete | `memory.get(id)` etc. | same, by memory id |
|
||||
|
||||
Also drop legacy options that no longer apply on v3: `async_mode`, `output_format`, `enable_graph`.
|
||||
|
||||
---
|
||||
|
||||
## Return shapes
|
||||
- `search(...)` and `get_all(...)` return `{"results": [...]}`; each item has at least a `memory`
|
||||
(text) field, plus `id` and (for search) `score`. Code that reads `result["results"]` and pulls
|
||||
`item["memory"]` keeps working.
|
||||
- `get_all(...)` on the hosted client is paginated: `{"count", "next", "previous", "results": [...]}`.
|
||||
- `add(...)` returns the created memories. On v3 it returns **only ADD events** — if the old code
|
||||
branched on `event == "UPDATE"` / `"DELETE"` from `add()` results, that branch is now dead.
|
||||
|
||||
---
|
||||
|
||||
## Dependencies & environment
|
||||
- **Keep** the `mem0ai` dependency — `MemoryClient` ships in the same package. No version bump is
|
||||
required just to use the hosted client (confirm the installed version supports it).
|
||||
- **Remove** dependencies that existed *only* to back the local mem0 store/embedder/LLM and are now
|
||||
unused (e.g. `qdrant-client`, `chromadb`, a local embedding lib). Only remove what you can confirm
|
||||
is unused elsewhere.
|
||||
- **Add** `MEM0_API_KEY` to the environment / `.env.example` / secrets manager / deployment config.
|
||||
- Local-infra services (e.g. a Qdrant docker-compose service) that existed only for mem0 can be
|
||||
retired — flag this rather than deleting infrastructure unilaterally.
|
||||
|
||||
## v2→v3 default/behavior changes
|
||||
From the official migration guide — surface any that affect the project:
|
||||
- Python `top_k` default changed 100 → 20; TS `limit` renamed to `topK`.
|
||||
- New `threshold` default `0.1` (was none); new `rerank` default `false` (was true).
|
||||
- `custom_fact_extraction_prompt` → `custom_instructions`; `custom_update_memory_prompt` deprecated.
|
||||
- Graph memory (`enable_graph`, `graph_store`) removed from the OSS v3 surface — see gotchas.
|
||||
@@ -1,63 +0,0 @@
|
||||
# Gotchas — the things that aren't a clean 1:1
|
||||
|
||||
Swapping `Memory` for `MemoryClient` is mostly mechanical. These items are *not* mechanical: they
|
||||
change behavior, move responsibility off the developer's machine, or have no direct equivalent.
|
||||
Every one that applies to the project belongs in the plan's **"Concerns & decisions needed"**
|
||||
section, phrased as a decision for the developer — never silently resolved.
|
||||
|
||||
## 1. Data does not migrate with the code
|
||||
Migrating the *code* does not move the *memories*. Anything stored in the local vector store /
|
||||
history DB stays there; the hosted account starts empty. This is the most surprising gap, so call
|
||||
it out prominently. Data migration is **out of scope** unless the developer explicitly asks. If they
|
||||
do, the rough path is: read everything from the OSS store (`get_all` per user/entity) and re-`add`
|
||||
it to the hosted client — but treat that as a separate, opt-in task.
|
||||
|
||||
## 2. Self-hosting / data residency
|
||||
A local or self-hosted vector store sometimes exists *on purpose* — compliance, data residency, air-
|
||||
gapped deployment, cost. Moving to the managed platform sends memory content to mem0's servers.
|
||||
Don't assume that's acceptable; flag it as an explicit decision, especially for regulated domains.
|
||||
|
||||
## 3. Local models move server-side
|
||||
If the OSS config used specific local/self-chosen models (e.g. Ollama, a particular embedder, a
|
||||
non-OpenAI LLM for fact extraction), those choices disappear — extraction and embedding now run on
|
||||
the platform with the platform's configuration. Memory *content and quality may shift* as a result.
|
||||
Flag where the project depended on a specific model.
|
||||
|
||||
## 4. Graph memory
|
||||
If the project uses graph memory (`enable_graph`, `graph_store`), this changed in v3 and is handled
|
||||
differently on the platform. Don't assume a drop-in mapping — verify current platform graph support
|
||||
in the docs and flag the usage for the developer.
|
||||
|
||||
## 5. Custom prompts / extraction config
|
||||
`custom_fact_extraction_prompt` → `custom_instructions`, and `custom_update_memory_prompt` is
|
||||
deprecated. On the platform these tend to be **project-level settings configured in the dashboard**
|
||||
rather than passed in code. Flag any custom prompt the project relied on so the developer can re-
|
||||
apply it in the dashboard.
|
||||
|
||||
## 6. Every call is now a network request
|
||||
Local calls become remote API calls. That introduces latency, network failures, timeouts, rate
|
||||
limits, and per-call cost. Flag mem0 calls on hot paths or in tight loops, and recommend adding
|
||||
error handling / retries / timeouts where the old local calls were effectively infallible. For async
|
||||
apps, use `AsyncMemoryClient` (Python) so calls don't block the event loop.
|
||||
|
||||
## 7. API key & secrets
|
||||
The hosted client needs `MEM0_API_KEY`. It must come from the environment / a secrets manager — never
|
||||
hardcoded. Ensure it's added to `.env.example`, local `.env`, CI, and deployment config. Without it
|
||||
the client fails to initialize.
|
||||
|
||||
## 8. Dropped constructor args & legacy options
|
||||
`org_id` / `project_id` (Python) and `organizationId` / `projectId` (TS) are no longer passed to the
|
||||
constructor in v3 — they're resolved from the API key. Per-call legacy options like `async_mode`,
|
||||
`output_format`, and `enable_graph` are gone. Remove them rather than leaving dead args.
|
||||
|
||||
## 9. Return-shape drift
|
||||
- `add()` returns **only ADD events** on v3. Code that inspected `add()` results for `UPDATE` /
|
||||
`DELETE` events has dead branches now.
|
||||
- `search` / `get_all` return `{"results": [...]}`; `get_all` is paginated (`count`/`next`/
|
||||
`previous`/`results`). Code that limited via `top_k` on `get_all` should move to `page`/`page_size`.
|
||||
- Default `top_k` dropped 100 → 20, `threshold` now `0.1`, `rerank` now `false` — result counts and
|
||||
ordering can change even when the call looks equivalent.
|
||||
|
||||
## 10. No global `reset()`
|
||||
The OSS `reset()` wipes the whole local store. There's no hosted equivalent that nukes everything;
|
||||
use `delete_all` scoped by `filters`. Flag any `reset()` call.
|
||||
@@ -1,72 +0,0 @@
|
||||
# Plan template — `MEM0_MIGRATION_PLAN.md`
|
||||
|
||||
Write the plan to `MEM0_MIGRATION_PLAN.md` at the repo root using the structure below. Keep it
|
||||
concrete enough to execute from and honest about the gaps. Fill every section from the actual
|
||||
findings — don't leave placeholders. Drop a section only if it genuinely doesn't apply (and say so).
|
||||
|
||||
```markdown
|
||||
# mem0 OSS → Platform Migration Plan
|
||||
|
||||
## Summary
|
||||
- One paragraph: what's moving (self-hosted `Memory` → hosted `MemoryClient`) and why.
|
||||
- Detected language(s) and the installed `mem0ai` version.
|
||||
- Counts: N files touched, M call sites, plus config/deps/env changes.
|
||||
|
||||
## Prerequisites
|
||||
- `MEM0_API_KEY` must be set in the environment before execution/verification (https://app.mem0.ai).
|
||||
- Note where it should live (`.env`, secrets manager, CI, deploy config).
|
||||
|
||||
## Inventory
|
||||
A table of every mem0 touchpoint found, so the developer can see the full footprint:
|
||||
|
||||
| File:line | Current (OSS) usage | Category | Maps to |
|
||||
|-----------|--------------------|----------|---------|
|
||||
| path:LN | `Memory.from_config({...})` | client init | `MemoryClient()` |
|
||||
| path:LN | `memory.search(q, user_id=...)` | call site | `search(q, filters={...}, top_k=...)` |
|
||||
| ... | ... | config / dep / env / infra | ... |
|
||||
|
||||
## Change set
|
||||
Grouped by file. For each, a short before → after with the actual surrounding code, so the edits are
|
||||
unambiguous. Example:
|
||||
|
||||
### `path/to/file.py`
|
||||
- Replace import `from mem0 import Memory` → `from mem0 import MemoryClient`.
|
||||
- Replace the `Memory.from_config({...})` block with `MemoryClient()` (drops local vector_store/
|
||||
llm/embedder/history config).
|
||||
- `search(...)`: move `user_id` into `filters={"user_id": ...}`; keep `top_k`.
|
||||
```
|
||||
# before
|
||||
...
|
||||
# after
|
||||
...
|
||||
```
|
||||
|
||||
## Dependencies & config
|
||||
- `requirements.txt` / `pyproject.toml` / `package.json`: keep `mem0ai`; remove now-unused local-
|
||||
infra deps (list them, with the reason each is safe to remove).
|
||||
- Env: add `MEM0_API_KEY`; remove env vars only used by the old local embedder/LLM if now unused.
|
||||
- Infrastructure: local services that existed only for mem0 (e.g. a Qdrant docker-compose service)
|
||||
can be retired — listed for the developer's confirmation, not auto-deleted.
|
||||
|
||||
## Concerns & decisions needed
|
||||
The non-1:1 items from the gotchas that apply here, each phrased as a decision for the developer.
|
||||
Cover, where relevant: data not migrating, self-hosting/data-residency, local models moving server-
|
||||
side, graph memory, custom prompts (now dashboard settings), network/latency/cost on hot paths,
|
||||
return-shape changes (`add` ADD-only, `get_all` pagination, default `top_k`/threshold/rerank), and
|
||||
any `reset()` usage. Be specific about which file/line each concern affects.
|
||||
|
||||
## Out of scope
|
||||
- Existing memory **data** is not migrated (code only). If wanted, it's a separate opt-in task
|
||||
(export from the OSS store, re-add to the hosted client).
|
||||
- No unrelated refactors, renames, or behavior changes.
|
||||
|
||||
## Verification plan
|
||||
How execution will be confirmed end-to-end:
|
||||
- Imports / type-checks / byte-compiles cleanly.
|
||||
- Smoke test against the hosted API with a real `MEM0_API_KEY`: `add` a fact → `search`/`get_all`
|
||||
returns it → `delete_all` clears it. Plus: the app's own entry point still runs.
|
||||
- Confirm no local mem0 storage dir is created anymore (e.g. `.mem0/`) — proof memory is hosted.
|
||||
|
||||
## Rollback
|
||||
- All changes are in version control; revert with git if needed. Note the branch/commit strategy.
|
||||
```
|
||||
@@ -1,60 +0,0 @@
|
||||
import pytest
|
||||
|
||||
from mem0.memory import main as memory_main
|
||||
from mem0.memory.main import AsyncMemory, Memory
|
||||
|
||||
|
||||
def test_sync_project_update_decay_true_raises_with_notice_message(monkeypatch):
|
||||
calls = []
|
||||
|
||||
def get_error(sync_type, trigger_function, trigger_parameter):
|
||||
calls.append((sync_type, trigger_function, trigger_parameter))
|
||||
return "blocked decay"
|
||||
|
||||
monkeypatch.setattr(memory_main, "get_decay_feature_error_message", get_error)
|
||||
|
||||
with pytest.raises(ValueError, match="blocked decay"):
|
||||
Memory.__new__(Memory).project.update(decay=True)
|
||||
|
||||
assert calls == [("sync", "project.update", "decay")]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_project_update_decay_true_raises_with_notice_message(monkeypatch):
|
||||
calls = []
|
||||
|
||||
async def get_error(sync_type, trigger_function, trigger_parameter):
|
||||
calls.append((sync_type, trigger_function, trigger_parameter))
|
||||
return "blocked async decay"
|
||||
|
||||
monkeypatch.setattr(memory_main, "get_decay_feature_error_message_async", get_error)
|
||||
|
||||
with pytest.raises(ValueError, match="blocked async decay"):
|
||||
await AsyncMemory.__new__(AsyncMemory).project.update(decay=True)
|
||||
|
||||
assert calls == [("async", "project.update", "decay")]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("kwargs", [{}, {"decay": False}])
|
||||
def test_sync_project_update_non_trigger_raises_plain_error_without_notice(monkeypatch, kwargs):
|
||||
monkeypatch.setattr(
|
||||
memory_main,
|
||||
"get_decay_feature_error_message",
|
||||
lambda *args: pytest.fail("decay feature notice should not run"),
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="Project updates are not supported by the OSS Memory SDK."):
|
||||
Memory.__new__(Memory).project.update(**kwargs)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("kwargs", [{}, {"decay": False}])
|
||||
async def test_async_project_update_non_trigger_raises_plain_error_without_notice(monkeypatch, kwargs):
|
||||
monkeypatch.setattr(
|
||||
memory_main,
|
||||
"get_decay_feature_error_message_async",
|
||||
lambda *args: pytest.fail("decay feature notice should not run"),
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="Project updates are not supported by the OSS Memory SDK."):
|
||||
await AsyncMemory.__new__(AsyncMemory).project.update(**kwargs)
|
||||
@@ -1,214 +0,0 @@
|
||||
from types import SimpleNamespace
|
||||
from unittest.mock import AsyncMock, MagicMock
|
||||
|
||||
import pytest
|
||||
|
||||
from mem0.memory import main as memory_main
|
||||
from mem0.memory.main import AsyncMemory, Memory
|
||||
|
||||
|
||||
def make_sync_memory():
|
||||
memory = Memory.__new__(Memory)
|
||||
memory.vector_store = MagicMock()
|
||||
memory._delete_memory = MagicMock()
|
||||
return memory
|
||||
|
||||
|
||||
def make_async_memory():
|
||||
memory = AsyncMemory.__new__(AsyncMemory)
|
||||
memory.vector_store = MagicMock()
|
||||
memory._delete_memory = AsyncMock()
|
||||
return memory
|
||||
|
||||
|
||||
def test_sync_delete_decay_usage_runs_after_success(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
existing_memory = SimpleNamespace(id="memory-1")
|
||||
memory.vector_store.get.return_value = existing_memory
|
||||
decay_notice = MagicMock()
|
||||
first_run_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(
|
||||
memory_main,
|
||||
"detect_decay_usage_from_delete",
|
||||
MagicMock(return_value=("delete_count", "repeated_deletes", 5, None)),
|
||||
)
|
||||
monkeypatch.setattr(memory_main, "display_decay_usage_notice", decay_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", first_run_notice)
|
||||
|
||||
result = Memory.delete(memory, "memory-1")
|
||||
|
||||
assert result == {"message": "Memory deleted successfully!"}
|
||||
memory._delete_memory.assert_called_once_with("memory-1", existing_memory)
|
||||
decay_notice.assert_called_once_with(
|
||||
memory,
|
||||
"sync",
|
||||
"delete",
|
||||
"delete_count",
|
||||
"repeated_deletes",
|
||||
5,
|
||||
None,
|
||||
)
|
||||
first_run_notice.assert_not_called()
|
||||
|
||||
|
||||
def test_sync_delete_below_threshold_uses_first_run_notice(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
memory.vector_store.get.return_value = SimpleNamespace(id="memory-1")
|
||||
decay_notice = MagicMock()
|
||||
first_run_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main, "detect_decay_usage_from_delete", MagicMock(return_value=None))
|
||||
monkeypatch.setattr(memory_main, "display_decay_usage_notice", decay_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", first_run_notice)
|
||||
|
||||
Memory.delete(memory, "memory-1")
|
||||
|
||||
decay_notice.assert_not_called()
|
||||
first_run_notice.assert_called_once_with(memory, "sync", "delete")
|
||||
|
||||
|
||||
def test_sync_delete_failure_does_not_trigger_decay_usage_notice(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
memory.vector_store.get.return_value = None
|
||||
detect_decay = MagicMock()
|
||||
decay_notice = MagicMock()
|
||||
first_run_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main, "detect_decay_usage_from_delete", detect_decay)
|
||||
monkeypatch.setattr(memory_main, "display_decay_usage_notice", decay_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", first_run_notice)
|
||||
|
||||
with pytest.raises(ValueError, match="Memory with id memory-1 not found"):
|
||||
Memory.delete(memory, "memory-1")
|
||||
|
||||
detect_decay.assert_not_called()
|
||||
decay_notice.assert_not_called()
|
||||
first_run_notice.assert_not_called()
|
||||
|
||||
|
||||
def test_sync_delete_all_decay_usage_runs_after_success(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
memories = [SimpleNamespace(id="memory-1"), SimpleNamespace(id="memory-2")]
|
||||
memory.vector_store.list.return_value = (memories, None)
|
||||
decay_notice = MagicMock()
|
||||
first_run_notice = MagicMock()
|
||||
detect_decay = MagicMock(return_value=("delete_all", "bulk_delete", None, 2))
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main, "detect_decay_usage_from_delete_all", detect_decay)
|
||||
monkeypatch.setattr(memory_main, "display_decay_usage_notice", decay_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", first_run_notice)
|
||||
|
||||
result = Memory.delete_all(memory, user_id="u1")
|
||||
|
||||
assert result == {"message": "Memories deleted successfully!"}
|
||||
assert memory._delete_memory.call_count == 2
|
||||
detect_decay.assert_called_once_with(2)
|
||||
decay_notice.assert_called_once_with(
|
||||
memory,
|
||||
"sync",
|
||||
"delete_all",
|
||||
"delete_all",
|
||||
"bulk_delete",
|
||||
None,
|
||||
2,
|
||||
)
|
||||
first_run_notice.assert_not_called()
|
||||
|
||||
|
||||
def test_sync_delete_all_zero_deletes_uses_first_run_notice(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
memory.vector_store.list.return_value = ([], None)
|
||||
decay_notice = MagicMock()
|
||||
first_run_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main, "detect_decay_usage_from_delete_all", MagicMock(return_value=None))
|
||||
monkeypatch.setattr(memory_main, "display_decay_usage_notice", decay_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", first_run_notice)
|
||||
|
||||
Memory.delete_all(memory, user_id="u1")
|
||||
|
||||
decay_notice.assert_not_called()
|
||||
first_run_notice.assert_called_once_with(memory, "sync", "delete_all")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_delete_decay_usage_runs_after_success(monkeypatch):
|
||||
memory = make_async_memory()
|
||||
existing_memory = SimpleNamespace(id="memory-1")
|
||||
memory.vector_store.get.return_value = existing_memory
|
||||
decay_notice = AsyncMock()
|
||||
first_run_notice = AsyncMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(
|
||||
memory_main,
|
||||
"detect_decay_usage_from_delete",
|
||||
MagicMock(return_value=("delete_count", "repeated_deletes", 5, None)),
|
||||
)
|
||||
monkeypatch.setattr(memory_main, "display_decay_usage_notice_async", decay_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice_async", first_run_notice)
|
||||
|
||||
result = await AsyncMemory.delete(memory, "memory-1")
|
||||
|
||||
assert result == {"message": "Memory deleted successfully!"}
|
||||
memory._delete_memory.assert_awaited_once_with("memory-1", existing_memory)
|
||||
decay_notice.assert_awaited_once_with(
|
||||
memory,
|
||||
"async",
|
||||
"delete",
|
||||
"delete_count",
|
||||
"repeated_deletes",
|
||||
5,
|
||||
None,
|
||||
)
|
||||
first_run_notice.assert_not_awaited()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_delete_failure_does_not_trigger_decay_usage_notice(monkeypatch):
|
||||
memory = make_async_memory()
|
||||
memory.vector_store.get.return_value = None
|
||||
detect_decay = MagicMock()
|
||||
decay_notice = AsyncMock()
|
||||
first_run_notice = AsyncMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main, "detect_decay_usage_from_delete", detect_decay)
|
||||
monkeypatch.setattr(memory_main, "display_decay_usage_notice_async", decay_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice_async", first_run_notice)
|
||||
|
||||
with pytest.raises(ValueError, match="Memory with id memory-1 not found"):
|
||||
await AsyncMemory.delete(memory, "memory-1")
|
||||
|
||||
detect_decay.assert_not_called()
|
||||
decay_notice.assert_not_awaited()
|
||||
first_run_notice.assert_not_awaited()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_delete_all_decay_usage_runs_after_success(monkeypatch):
|
||||
memory = make_async_memory()
|
||||
memories = [SimpleNamespace(id="memory-1"), SimpleNamespace(id="memory-2")]
|
||||
memory.vector_store.list.return_value = (memories, None)
|
||||
decay_notice = AsyncMock()
|
||||
first_run_notice = AsyncMock()
|
||||
detect_decay = MagicMock(return_value=("delete_all", "bulk_delete", None, 2))
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main, "detect_decay_usage_from_delete_all", detect_decay)
|
||||
monkeypatch.setattr(memory_main, "display_decay_usage_notice_async", decay_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice_async", first_run_notice)
|
||||
|
||||
result = await AsyncMemory.delete_all(memory, user_id="u1")
|
||||
|
||||
assert result == {"message": "Memories deleted successfully!"}
|
||||
assert memory._delete_memory.await_count == 2
|
||||
detect_decay.assert_called_once_with(2)
|
||||
decay_notice.assert_awaited_once_with(
|
||||
memory,
|
||||
"async",
|
||||
"delete_all",
|
||||
"delete_all",
|
||||
"bulk_delete",
|
||||
None,
|
||||
2,
|
||||
)
|
||||
first_run_notice.assert_not_awaited()
|
||||
@@ -828,7 +828,7 @@ class TestEntityBoostParallelism:
|
||||
boosts = mock_memory._compute_entity_boosts(entities, {"user_id": "u1"})
|
||||
elapsed = time.perf_counter() - start
|
||||
|
||||
assert elapsed < 0.75, f"searches did not run concurrently (took {elapsed:.2f}s)"
|
||||
assert elapsed < 0.6, f"searches did not run concurrently (took {elapsed:.2f}s)"
|
||||
assert concurrent_count["peak"] >= 2, "no overlap observed between entity searches"
|
||||
assert len(boosts) == 4
|
||||
|
||||
@@ -854,6 +854,6 @@ class TestEntityBoostParallelism:
|
||||
boosts = await mock_async_memory._compute_entity_boosts_async(entities, {"user_id": "u1"})
|
||||
elapsed = time.perf_counter() - start
|
||||
|
||||
assert elapsed < 0.75, f"searches did not run concurrently (took {elapsed:.2f}s)"
|
||||
assert elapsed < 0.6, f"searches did not run concurrently (took {elapsed:.2f}s)"
|
||||
assert concurrent_count["peak"] >= 2, "no overlap observed between entity searches"
|
||||
assert len(boosts) == 4
|
||||
|
||||
@@ -1,60 +1,5 @@
|
||||
import pytest
|
||||
from unittest.mock import Mock
|
||||
|
||||
from mem0.memory.utils import parse_vision_messages, remove_spaces_from_entities, sanitize_relationship_for_cypher
|
||||
|
||||
|
||||
class TestParseVisionMessages:
|
||||
def test_multimodal_list_without_llm_extracts_text(self):
|
||||
messages = [
|
||||
{"role": "user", "content": [
|
||||
{"type": "text", "text": "What is this?"},
|
||||
{"type": "image_url", "image_url": {"url": "https://example.com/img.png"}},
|
||||
]},
|
||||
]
|
||||
result = parse_vision_messages(messages, llm=None)
|
||||
assert len(result) == 1
|
||||
assert result[0]["role"] == "user"
|
||||
assert result[0]["content"] == "What is this?"
|
||||
|
||||
def test_image_dict_without_llm_is_skipped(self):
|
||||
messages = [
|
||||
{"role": "user", "content": {"type": "image_url", "image_url": {"url": "https://example.com/img.png"}}},
|
||||
{"role": "user", "content": "hello"},
|
||||
]
|
||||
result = parse_vision_messages(messages, llm=None)
|
||||
assert len(result) == 1
|
||||
assert result[0]["content"] == "hello"
|
||||
|
||||
def test_multimodal_with_llm_calls_generate_response(self):
|
||||
mock_llm = Mock()
|
||||
mock_llm.generate_response.return_value = "A photo of a cat"
|
||||
messages = [
|
||||
{"role": "user", "content": [
|
||||
{"type": "text", "text": "Describe this"},
|
||||
{"type": "image_url", "image_url": {"url": "https://example.com/cat.png"}},
|
||||
]},
|
||||
]
|
||||
result = parse_vision_messages(messages, llm=mock_llm, vision_details="auto")
|
||||
assert result[0]["content"] == "A photo of a cat"
|
||||
mock_llm.generate_response.assert_called_once()
|
||||
|
||||
def test_image_only_list_without_llm_is_skipped(self):
|
||||
messages = [
|
||||
{"role": "user", "content": [
|
||||
{"type": "image_url", "image_url": {"url": "https://example.com/img.png"}},
|
||||
]},
|
||||
]
|
||||
result = parse_vision_messages(messages, llm=None)
|
||||
assert result == []
|
||||
|
||||
def test_plain_text_messages_pass_through(self):
|
||||
messages = [
|
||||
{"role": "system", "content": "You are helpful."},
|
||||
{"role": "user", "content": "Hello"},
|
||||
]
|
||||
result = parse_vision_messages(messages, llm=None)
|
||||
assert result == messages
|
||||
from mem0.memory.utils import remove_spaces_from_entities, sanitize_relationship_for_cypher
|
||||
|
||||
|
||||
class TestRemoveSpacesFromEntities:
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,445 +0,0 @@
|
||||
from copy import deepcopy
|
||||
from unittest.mock import AsyncMock, MagicMock
|
||||
|
||||
import pytest
|
||||
|
||||
from mem0.memory import notices
|
||||
from mem0.memory import main as memory_main
|
||||
from mem0.memory.main import AsyncMemory, Memory
|
||||
|
||||
|
||||
class FakeFlags:
|
||||
def __init__(self, variant, payload):
|
||||
self.variant = variant
|
||||
self.payload = payload
|
||||
|
||||
def get_flag(self, key):
|
||||
assert key == notices.FLAG_KEY
|
||||
return self.variant
|
||||
|
||||
def get_flag_payload(self, key):
|
||||
assert key == notices.FLAG_KEY
|
||||
return self.payload
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def reset_notice_process_state():
|
||||
notices._performance_slow_query_capacity_reached_in_process = False
|
||||
yield
|
||||
notices._performance_slow_query_capacity_reached_in_process = False
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def notice_harness(monkeypatch):
|
||||
config = {}
|
||||
telemetry = MagicMock()
|
||||
telemetry.user_id = "oss-user"
|
||||
|
||||
def write_config(updated):
|
||||
saved = deepcopy(updated)
|
||||
config.clear()
|
||||
config.update(saved)
|
||||
|
||||
monkeypatch.setattr(notices, "_load_config", lambda: config)
|
||||
monkeypatch.setattr(notices, "_write_config", write_config)
|
||||
monkeypatch.setattr(notices.telemetry_module, "MEM0_TELEMETRY", True)
|
||||
monkeypatch.setattr(notices.telemetry_module, "_get_oss_telemetry", lambda: telemetry)
|
||||
return config, telemetry
|
||||
|
||||
|
||||
def configure_flag(telemetry, variant, payload):
|
||||
flags = FakeFlags(variant, payload)
|
||||
telemetry.posthog.evaluate_flags.return_value = flags
|
||||
return flags
|
||||
|
||||
|
||||
def performance_payload(copy="Performance CTA", enabled=True, notice_type="log_line"):
|
||||
payload = {
|
||||
"notices": {
|
||||
"performance_slow_query": {
|
||||
"enabled": enabled,
|
||||
"notice_type": notice_type,
|
||||
}
|
||||
}
|
||||
}
|
||||
if copy is not None:
|
||||
payload["notices"]["performance_slow_query"]["copy"] = copy
|
||||
return payload
|
||||
|
||||
|
||||
def make_sync_memory(search_results=None):
|
||||
memory = Memory.__new__(Memory)
|
||||
memory.api_version = "v1.1"
|
||||
memory.reranker = None
|
||||
memory._search_vector_store = MagicMock(return_value=search_results or [])
|
||||
return memory
|
||||
|
||||
|
||||
def make_async_memory(search_results=None):
|
||||
memory = AsyncMemory.__new__(AsyncMemory)
|
||||
memory.api_version = "v1.1"
|
||||
memory.reranker = None
|
||||
memory._search_vector_store = AsyncMock(return_value=search_results or [])
|
||||
return memory
|
||||
|
||||
|
||||
def test_sync_slow_search_triggers_performance_notice_after_success(monkeypatch):
|
||||
results = [{"id": "m1"}, {"id": "m2"}]
|
||||
memory = make_sync_memory(search_results=results)
|
||||
performance_notice = MagicMock()
|
||||
temporal_notice = MagicMock()
|
||||
first_run_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main.time, "perf_counter", MagicMock(side_effect=[100.0, 102.1]))
|
||||
monkeypatch.setattr(memory_main, "display_performance_slow_query_notice", performance_notice)
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice", temporal_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", first_run_notice)
|
||||
|
||||
result = Memory.search(memory, "favorite drink", filters={"user_id": "u1"}, top_k=3)
|
||||
|
||||
assert result == {"results": results}
|
||||
memory._search_vector_store.assert_called_once()
|
||||
performance_notice.assert_called_once_with(memory, "sync", "search", pytest.approx(2.1), 3, 2)
|
||||
temporal_notice.assert_not_called()
|
||||
first_run_notice.assert_not_called()
|
||||
|
||||
|
||||
def test_sync_fast_search_uses_first_run_notice(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
performance_notice = MagicMock()
|
||||
first_run_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main.time, "perf_counter", MagicMock(side_effect=[100.0, 101.0]))
|
||||
monkeypatch.setattr(memory_main, "display_performance_slow_query_notice", performance_notice)
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice", MagicMock())
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", first_run_notice)
|
||||
|
||||
Memory.search(memory, "favorite drink", filters={"user_id": "u1"})
|
||||
|
||||
performance_notice.assert_not_called()
|
||||
first_run_notice.assert_called_once_with(memory, "sync", "search")
|
||||
|
||||
|
||||
def test_sync_failed_search_does_not_trigger_performance_notice(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
memory._search_vector_store.side_effect = RuntimeError("search failure")
|
||||
performance_notice = MagicMock()
|
||||
first_run_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main.time, "perf_counter", MagicMock(return_value=100.0))
|
||||
monkeypatch.setattr(memory_main, "display_performance_slow_query_notice", performance_notice)
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice", MagicMock())
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", first_run_notice)
|
||||
|
||||
with pytest.raises(RuntimeError, match="search failure"):
|
||||
Memory.search(memory, "favorite drink", filters={"user_id": "u1"})
|
||||
|
||||
performance_notice.assert_not_called()
|
||||
first_run_notice.assert_not_called()
|
||||
|
||||
|
||||
def test_sync_temporal_usage_takes_precedence_over_slow_search(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
performance_notice = MagicMock()
|
||||
temporal_notice = MagicMock()
|
||||
first_run_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main.time, "perf_counter", MagicMock(side_effect=[100.0, 102.1]))
|
||||
monkeypatch.setattr(memory_main, "display_performance_slow_query_notice", performance_notice)
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice", temporal_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", first_run_notice)
|
||||
|
||||
Memory.search(memory, "what happened last week?", filters={"user_id": "u1"})
|
||||
|
||||
temporal_notice.assert_called_once_with(memory, "sync", "search", "query", "relative_phrase")
|
||||
performance_notice.assert_not_called()
|
||||
first_run_notice.assert_not_called()
|
||||
|
||||
|
||||
def test_sync_scale_takes_precedence_over_slow_search(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
performance_notice = MagicMock()
|
||||
scale_notice = MagicMock()
|
||||
first_run_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main.time, "perf_counter", MagicMock(side_effect=[100.0, 102.1]))
|
||||
monkeypatch.setattr(memory_main, "display_performance_slow_query_notice", performance_notice)
|
||||
monkeypatch.setattr(memory_main, "display_scale_threshold_notice", scale_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", first_run_notice)
|
||||
|
||||
Memory.search(memory, "favorite drink", filters={"user_id": "u1"}, top_k=50)
|
||||
|
||||
scale_notice.assert_called_once_with(
|
||||
memory,
|
||||
"sync",
|
||||
"search",
|
||||
"top_k",
|
||||
"high_top_k",
|
||||
50,
|
||||
None,
|
||||
notices.SCALE_TOP_K_THRESHOLD,
|
||||
)
|
||||
performance_notice.assert_not_called()
|
||||
first_run_notice.assert_not_called()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_slow_search_triggers_performance_notice_after_success(monkeypatch):
|
||||
results = [{"id": "m1"}]
|
||||
memory = make_async_memory(search_results=results)
|
||||
performance_notice = AsyncMock()
|
||||
temporal_notice = AsyncMock()
|
||||
first_run_notice = AsyncMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main.time, "perf_counter", MagicMock(side_effect=[100.0, 102.1]))
|
||||
monkeypatch.setattr(memory_main, "display_performance_slow_query_notice_async", performance_notice)
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice_async", temporal_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice_async", first_run_notice)
|
||||
|
||||
result = await AsyncMemory.search(memory, "favorite drink", filters={"user_id": "u1"}, top_k=4)
|
||||
|
||||
assert result == {"results": results}
|
||||
memory._search_vector_store.assert_awaited_once()
|
||||
performance_notice.assert_awaited_once_with(memory, "async", "search", pytest.approx(2.1), 4, 1)
|
||||
temporal_notice.assert_not_awaited()
|
||||
first_run_notice.assert_not_awaited()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_fast_search_uses_first_run_notice(monkeypatch):
|
||||
memory = make_async_memory()
|
||||
performance_notice = AsyncMock()
|
||||
first_run_notice = AsyncMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main.time, "perf_counter", MagicMock(side_effect=[100.0, 101.0]))
|
||||
monkeypatch.setattr(memory_main, "display_performance_slow_query_notice_async", performance_notice)
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice_async", AsyncMock())
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice_async", first_run_notice)
|
||||
|
||||
await AsyncMemory.search(memory, "favorite drink", filters={"user_id": "u1"})
|
||||
|
||||
performance_notice.assert_not_awaited()
|
||||
first_run_notice.assert_awaited_once_with(memory, "async", "search")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_failed_search_does_not_trigger_performance_notice(monkeypatch):
|
||||
memory = make_async_memory()
|
||||
memory._search_vector_store.side_effect = RuntimeError("search failure")
|
||||
performance_notice = AsyncMock()
|
||||
first_run_notice = AsyncMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main.time, "perf_counter", MagicMock(return_value=100.0))
|
||||
monkeypatch.setattr(memory_main, "display_performance_slow_query_notice_async", performance_notice)
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice_async", AsyncMock())
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice_async", first_run_notice)
|
||||
|
||||
with pytest.raises(RuntimeError, match="search failure"):
|
||||
await AsyncMemory.search(memory, "favorite drink", filters={"user_id": "u1"})
|
||||
|
||||
performance_notice.assert_not_awaited()
|
||||
first_run_notice.assert_not_awaited()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_temporal_usage_takes_precedence_over_slow_search(monkeypatch):
|
||||
memory = make_async_memory()
|
||||
performance_notice = AsyncMock()
|
||||
temporal_notice = AsyncMock()
|
||||
first_run_notice = AsyncMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main.time, "perf_counter", MagicMock(side_effect=[100.0, 102.1]))
|
||||
monkeypatch.setattr(memory_main, "display_performance_slow_query_notice_async", performance_notice)
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice_async", temporal_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice_async", first_run_notice)
|
||||
|
||||
await AsyncMemory.search(memory, "what happened last week?", filters={"user_id": "u1"})
|
||||
|
||||
temporal_notice.assert_awaited_once_with(memory, "async", "search", "query", "relative_phrase")
|
||||
performance_notice.assert_not_awaited()
|
||||
first_run_notice.assert_not_awaited()
|
||||
|
||||
|
||||
def test_performance_slow_query_displayed_logs_and_captures_event(notice_harness, capsys):
|
||||
config, telemetry = notice_harness
|
||||
flags = configure_flag(telemetry, "displayed", performance_payload())
|
||||
|
||||
notices.display_performance_slow_query_notice(
|
||||
MagicMock(),
|
||||
"sync",
|
||||
"search",
|
||||
elapsed_seconds=2.345,
|
||||
top_k=20,
|
||||
result_count=7,
|
||||
)
|
||||
|
||||
assert capsys.readouterr().err == "Performance CTA\n"
|
||||
telemetry.posthog.evaluate_flags.assert_called_once_with("oss-user", flag_keys=[notices.FLAG_KEY])
|
||||
telemetry.capture_event.assert_called_once()
|
||||
event_name, props = telemetry.capture_event.call_args.args
|
||||
assert event_name == notices.NOTICE_EVENT
|
||||
assert props["notice_id"] == "performance_slow_query"
|
||||
assert props["notice_type"] == "log_line"
|
||||
assert props["variant"] == "displayed"
|
||||
assert props["displayed"] is True
|
||||
assert props["payload"] == "Performance CTA"
|
||||
assert props["bypass_reason"] is None
|
||||
assert props["disabled_reason"] is None
|
||||
assert props["notice_config_found"] is True
|
||||
assert props["sync_type"] == "sync"
|
||||
assert props["trigger_function"] == "search"
|
||||
assert props["trigger_reason"] == "slow_query"
|
||||
assert props["elapsed_ms"] == 2345
|
||||
assert props["threshold_ms"] == 2000
|
||||
assert props["top_k"] == 20
|
||||
assert props["result_count"] == 7
|
||||
assert telemetry.capture_event.call_args.kwargs["flags"] is flags
|
||||
assert len(config["notice_state"]["performance_slow_query"]["events"]) == 1
|
||||
|
||||
|
||||
def test_performance_slow_query_holdout_is_silent_but_captures_event(notice_harness, capsys):
|
||||
_, telemetry = notice_harness
|
||||
configure_flag(telemetry, "holdout", performance_payload())
|
||||
|
||||
notices.display_performance_slow_query_notice(
|
||||
MagicMock(),
|
||||
"sync",
|
||||
"search",
|
||||
elapsed_seconds=2.1,
|
||||
top_k=10,
|
||||
result_count=2,
|
||||
)
|
||||
|
||||
assert capsys.readouterr().err == ""
|
||||
props = telemetry.capture_event.call_args.args[1]
|
||||
assert props["displayed"] is False
|
||||
assert props["bypass_reason"] == "holdout"
|
||||
assert props["trigger_reason"] == "slow_query"
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("payload", "expected_reason", "expected_found"),
|
||||
[
|
||||
({}, "missing_notice_config", False),
|
||||
({"notices": {}}, "missing_notice_config", False),
|
||||
({"notices": "not-an-object"}, "missing_notice_config", False),
|
||||
(performance_payload(copy=None), "missing_copy", True),
|
||||
(performance_payload(enabled=False, copy="hidden"), "payload_disabled", True),
|
||||
],
|
||||
)
|
||||
def test_performance_slow_query_bad_or_disabled_payload_is_silent_and_safe(
|
||||
notice_harness, payload, expected_reason, expected_found, capsys
|
||||
):
|
||||
_, telemetry = notice_harness
|
||||
configure_flag(telemetry, "displayed", payload)
|
||||
|
||||
notices.display_performance_slow_query_notice(
|
||||
MagicMock(),
|
||||
"sync",
|
||||
"search",
|
||||
elapsed_seconds=2.1,
|
||||
top_k=10,
|
||||
result_count=2,
|
||||
)
|
||||
|
||||
assert capsys.readouterr().err == ""
|
||||
props = telemetry.capture_event.call_args.args[1]
|
||||
assert props["displayed"] is False
|
||||
assert props["bypass_reason"] == expected_reason
|
||||
assert props["notice_config_found"] is expected_found
|
||||
|
||||
|
||||
@pytest.mark.parametrize("variant", [None, False])
|
||||
def test_performance_slow_query_blunt_flag_disable_does_not_capture_or_consume(
|
||||
notice_harness, variant, capsys
|
||||
):
|
||||
config, telemetry = notice_harness
|
||||
configure_flag(telemetry, variant, performance_payload())
|
||||
|
||||
notices.display_performance_slow_query_notice(
|
||||
MagicMock(),
|
||||
"sync",
|
||||
"search",
|
||||
elapsed_seconds=2.1,
|
||||
top_k=10,
|
||||
result_count=2,
|
||||
)
|
||||
|
||||
assert capsys.readouterr().err == ""
|
||||
telemetry.capture_event.assert_not_called()
|
||||
assert config.get("notice_state") is None
|
||||
|
||||
|
||||
def test_performance_slow_query_telemetry_disabled_does_not_touch_posthog_or_state(
|
||||
monkeypatch, capsys
|
||||
):
|
||||
load_config = MagicMock(return_value={})
|
||||
write_config = MagicMock()
|
||||
get_telemetry = MagicMock()
|
||||
|
||||
monkeypatch.setattr(notices, "_load_config", load_config)
|
||||
monkeypatch.setattr(notices, "_write_config", write_config)
|
||||
monkeypatch.setattr(notices.telemetry_module, "MEM0_TELEMETRY", False)
|
||||
monkeypatch.setattr(notices.telemetry_module, "_get_oss_telemetry", get_telemetry)
|
||||
|
||||
notices.display_performance_slow_query_notice(
|
||||
MagicMock(),
|
||||
"sync",
|
||||
"search",
|
||||
elapsed_seconds=2.1,
|
||||
top_k=10,
|
||||
result_count=2,
|
||||
)
|
||||
|
||||
load_config.assert_not_called()
|
||||
write_config.assert_not_called()
|
||||
get_telemetry.assert_not_called()
|
||||
assert capsys.readouterr().err == ""
|
||||
|
||||
|
||||
def test_performance_slow_query_cap_blocks_before_posthog_eval(notice_harness, capsys):
|
||||
config, telemetry = notice_harness
|
||||
configure_flag(telemetry, "displayed", performance_payload())
|
||||
|
||||
for _ in range(notices.PERFORMANCE_SLOW_QUERY_CAP):
|
||||
notices.display_performance_slow_query_notice(
|
||||
MagicMock(),
|
||||
"sync",
|
||||
"search",
|
||||
elapsed_seconds=2.1,
|
||||
top_k=10,
|
||||
result_count=2,
|
||||
)
|
||||
|
||||
notices.display_performance_slow_query_notice(
|
||||
MagicMock(),
|
||||
"sync",
|
||||
"search",
|
||||
elapsed_seconds=2.1,
|
||||
top_k=10,
|
||||
result_count=2,
|
||||
)
|
||||
|
||||
assert capsys.readouterr().err == "Performance CTA\n" * notices.PERFORMANCE_SLOW_QUERY_CAP
|
||||
assert telemetry.posthog.evaluate_flags.call_count == notices.PERFORMANCE_SLOW_QUERY_CAP
|
||||
assert telemetry.capture_event.call_count == notices.PERFORMANCE_SLOW_QUERY_CAP
|
||||
assert len(config["notice_state"]["performance_slow_query"]["events"]) == notices.PERFORMANCE_SLOW_QUERY_CAP
|
||||
|
||||
|
||||
def test_performance_slow_query_props_do_not_include_raw_user_inputs(notice_harness):
|
||||
_, telemetry = notice_harness
|
||||
configure_flag(telemetry, "displayed", performance_payload(copy="safe copy"))
|
||||
|
||||
notices.display_performance_slow_query_notice(
|
||||
MagicMock(),
|
||||
"sync",
|
||||
"search",
|
||||
elapsed_seconds=2.1,
|
||||
top_k=10,
|
||||
result_count=2,
|
||||
)
|
||||
|
||||
props = telemetry.capture_event.call_args.args[1]
|
||||
assert "favorite drink" not in str(props)
|
||||
assert "user_id" not in str(props)
|
||||
assert "green tea" not in str(props)
|
||||
@@ -1,96 +0,0 @@
|
||||
import pytest
|
||||
|
||||
from mem0.memory import main as memory_main
|
||||
from mem0.memory.main import AsyncMemory, Memory
|
||||
|
||||
|
||||
def test_sync_add_timestamp_raises_before_validation(monkeypatch):
|
||||
calls = []
|
||||
|
||||
def get_error(sync_type, trigger_function, trigger_parameter):
|
||||
calls.append((sync_type, trigger_function, trigger_parameter))
|
||||
return "blocked timestamp"
|
||||
|
||||
monkeypatch.setattr(memory_main, "get_temporal_feature_error_message", get_error)
|
||||
|
||||
with pytest.raises(ValueError, match="blocked timestamp"):
|
||||
Memory.add(Memory.__new__(Memory), "hello", timestamp=123)
|
||||
|
||||
assert calls == [("sync", "add", "timestamp")]
|
||||
|
||||
|
||||
def test_sync_search_reference_date_raises_before_validation(monkeypatch):
|
||||
calls = []
|
||||
|
||||
def get_error(sync_type, trigger_function, trigger_parameter):
|
||||
calls.append((sync_type, trigger_function, trigger_parameter))
|
||||
return "blocked reference date"
|
||||
|
||||
monkeypatch.setattr(memory_main, "get_temporal_feature_error_message", get_error)
|
||||
|
||||
with pytest.raises(ValueError, match="blocked reference date"):
|
||||
Memory.search(Memory.__new__(Memory), "what happened last week?", reference_date="2025-03-21")
|
||||
|
||||
assert calls == [("sync", "search", "reference_date")]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_add_timestamp_raises_before_validation(monkeypatch):
|
||||
calls = []
|
||||
|
||||
async def get_error(sync_type, trigger_function, trigger_parameter):
|
||||
calls.append((sync_type, trigger_function, trigger_parameter))
|
||||
return "blocked async timestamp"
|
||||
|
||||
monkeypatch.setattr(memory_main, "get_temporal_feature_error_message_async", get_error)
|
||||
|
||||
with pytest.raises(ValueError, match="blocked async timestamp"):
|
||||
await AsyncMemory.add(AsyncMemory.__new__(AsyncMemory), "hello", timestamp=123)
|
||||
|
||||
assert calls == [("async", "add", "timestamp")]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_search_reference_date_raises_before_validation(monkeypatch):
|
||||
calls = []
|
||||
|
||||
async def get_error(sync_type, trigger_function, trigger_parameter):
|
||||
calls.append((sync_type, trigger_function, trigger_parameter))
|
||||
return "blocked async reference date"
|
||||
|
||||
monkeypatch.setattr(memory_main, "get_temporal_feature_error_message_async", get_error)
|
||||
|
||||
with pytest.raises(ValueError, match="blocked async reference date"):
|
||||
await AsyncMemory.search(
|
||||
AsyncMemory.__new__(AsyncMemory),
|
||||
"what happened last week?",
|
||||
reference_date="2025-03-21",
|
||||
)
|
||||
|
||||
assert calls == [("async", "search", "reference_date")]
|
||||
|
||||
|
||||
def test_sync_add_without_timestamp_does_not_call_temporal_feature_notice(monkeypatch):
|
||||
get_error = monkeypatch.setattr(
|
||||
memory_main,
|
||||
"get_temporal_feature_error_message",
|
||||
lambda *args: pytest.fail("temporal feature notice should not run"),
|
||||
)
|
||||
|
||||
with pytest.raises(Exception, match="At least one of 'user_id', 'agent_id', or 'run_id'"):
|
||||
Memory.add(Memory.__new__(Memory), "hello")
|
||||
|
||||
assert get_error is None
|
||||
|
||||
|
||||
def test_sync_search_without_reference_date_does_not_call_temporal_feature_notice(monkeypatch):
|
||||
get_error = monkeypatch.setattr(
|
||||
memory_main,
|
||||
"get_temporal_feature_error_message",
|
||||
lambda *args: pytest.fail("temporal feature notice should not run"),
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="filters must contain"):
|
||||
Memory.search(Memory.__new__(Memory), "hello")
|
||||
|
||||
assert get_error is None
|
||||
@@ -1,202 +0,0 @@
|
||||
from types import SimpleNamespace
|
||||
from unittest.mock import AsyncMock, MagicMock
|
||||
|
||||
import pytest
|
||||
|
||||
from mem0.memory import main as memory_main
|
||||
from mem0.memory.main import AsyncMemory, Memory
|
||||
|
||||
|
||||
def make_sync_memory():
|
||||
memory = Memory.__new__(Memory)
|
||||
memory.config = SimpleNamespace(llm=SimpleNamespace(config={}))
|
||||
memory.api_version = "v1.1"
|
||||
memory.reranker = None
|
||||
memory._add_to_vector_store = MagicMock(return_value=[])
|
||||
memory._search_vector_store = MagicMock(return_value=[])
|
||||
return memory
|
||||
|
||||
|
||||
def make_async_memory():
|
||||
memory = AsyncMemory.__new__(AsyncMemory)
|
||||
memory.config = SimpleNamespace(llm=SimpleNamespace(config={}))
|
||||
memory.api_version = "v1.1"
|
||||
memory.reranker = None
|
||||
memory._add_to_vector_store = AsyncMock(return_value=[])
|
||||
memory._search_vector_store = AsyncMock(return_value=[])
|
||||
return memory
|
||||
|
||||
|
||||
def test_sync_add_temporal_metadata_triggers_notice_after_success(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
temporal_notice = MagicMock()
|
||||
first_run_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice", temporal_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", first_run_notice)
|
||||
|
||||
result = Memory.add(
|
||||
memory,
|
||||
"The user visited Paris.",
|
||||
user_id="u1",
|
||||
metadata={"event_date": "2025-04-09"},
|
||||
infer=False,
|
||||
)
|
||||
|
||||
assert result == {"results": []}
|
||||
memory._add_to_vector_store.assert_called_once()
|
||||
temporal_notice.assert_called_once_with(memory, "sync", "add", "metadata", "date_like_metadata")
|
||||
first_run_notice.assert_not_called()
|
||||
|
||||
|
||||
def test_sync_add_non_temporal_metadata_uses_first_run_notice(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
temporal_notice = MagicMock()
|
||||
first_run_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice", temporal_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", first_run_notice)
|
||||
|
||||
Memory.add(memory, "The user likes tea.", user_id="u1", metadata={"topic": "drink"}, infer=False)
|
||||
|
||||
temporal_notice.assert_not_called()
|
||||
first_run_notice.assert_called_once_with(memory, "sync", "add")
|
||||
|
||||
|
||||
def test_sync_add_failure_does_not_trigger_temporal_usage_notice(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
memory._add_to_vector_store.side_effect = RuntimeError("vector failure")
|
||||
temporal_notice = MagicMock()
|
||||
first_run_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice", temporal_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", first_run_notice)
|
||||
|
||||
with pytest.raises(RuntimeError, match="vector failure"):
|
||||
Memory.add(
|
||||
memory,
|
||||
"The user visited Paris.",
|
||||
user_id="u1",
|
||||
metadata={"event_date": "2025-04-09"},
|
||||
infer=False,
|
||||
)
|
||||
|
||||
temporal_notice.assert_not_called()
|
||||
first_run_notice.assert_not_called()
|
||||
|
||||
|
||||
def test_sync_search_temporal_query_triggers_notice_after_success(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
temporal_notice = MagicMock()
|
||||
first_run_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice", temporal_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", first_run_notice)
|
||||
|
||||
result = Memory.search(memory, "what happened last week?", filters={"user_id": "u1"})
|
||||
|
||||
assert result == {"results": []}
|
||||
memory._search_vector_store.assert_called_once()
|
||||
temporal_notice.assert_called_once_with(memory, "sync", "search", "query", "relative_phrase")
|
||||
first_run_notice.assert_not_called()
|
||||
|
||||
|
||||
def test_sync_search_temporal_filter_triggers_notice_after_success(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
temporal_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice", temporal_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", MagicMock())
|
||||
|
||||
Memory.search(
|
||||
memory,
|
||||
"favorite drink",
|
||||
filters={"user_id": "u1", "created_at": {"gte": "2025-04-01"}},
|
||||
)
|
||||
|
||||
temporal_notice.assert_called_once_with(memory, "sync", "search", "filter", "date_range_filter")
|
||||
|
||||
|
||||
def test_sync_search_failure_does_not_trigger_temporal_usage_notice(monkeypatch):
|
||||
memory = make_sync_memory()
|
||||
memory._search_vector_store.side_effect = RuntimeError("search failure")
|
||||
temporal_notice = MagicMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice", temporal_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice", MagicMock())
|
||||
|
||||
with pytest.raises(RuntimeError, match="search failure"):
|
||||
Memory.search(memory, "what happened last week?", filters={"user_id": "u1"})
|
||||
|
||||
temporal_notice.assert_not_called()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_add_temporal_metadata_triggers_notice_after_success(monkeypatch):
|
||||
memory = make_async_memory()
|
||||
temporal_notice = AsyncMock()
|
||||
first_run_notice = AsyncMock()
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice_async", temporal_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice_async", first_run_notice)
|
||||
|
||||
result = await AsyncMemory.add(
|
||||
memory,
|
||||
"The user visited Paris.",
|
||||
user_id="u1",
|
||||
metadata={"event_date": "2025-04-09"},
|
||||
infer=False,
|
||||
)
|
||||
|
||||
assert result == {"results": []}
|
||||
memory._add_to_vector_store.assert_awaited_once()
|
||||
temporal_notice.assert_awaited_once_with(memory, "async", "add", "metadata", "date_like_metadata")
|
||||
first_run_notice.assert_not_awaited()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_add_runs_scale_detection_in_thread(monkeypatch):
|
||||
memory = make_async_memory()
|
||||
scale_detector = MagicMock(return_value=("memory_count", "memory_count_threshold", None, 2000, 2000))
|
||||
scale_notice = AsyncMock()
|
||||
first_run_notice = AsyncMock()
|
||||
to_thread_calls = []
|
||||
|
||||
async def to_thread(fn, *args, **kwargs):
|
||||
to_thread_calls.append((fn, args, kwargs))
|
||||
return fn(*args, **kwargs)
|
||||
|
||||
monkeypatch.setattr(memory_main, "detect_scale_threshold_from_add_result", scale_detector)
|
||||
monkeypatch.setattr(memory_main.asyncio, "to_thread", to_thread)
|
||||
monkeypatch.setattr(memory_main, "display_scale_threshold_notice_async", scale_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice_async", first_run_notice)
|
||||
|
||||
result = await AsyncMemory.add(memory, "The user likes tea.", user_id="u1", infer=False)
|
||||
|
||||
assert result == {"results": []}
|
||||
assert to_thread_calls == [(scale_detector, (memory, []), {})]
|
||||
scale_detector.assert_called_once_with(memory, [])
|
||||
scale_notice.assert_awaited_once_with(
|
||||
memory,
|
||||
"async",
|
||||
"add",
|
||||
"memory_count",
|
||||
"memory_count_threshold",
|
||||
None,
|
||||
2000,
|
||||
2000,
|
||||
)
|
||||
first_run_notice.assert_not_awaited()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_search_temporal_query_triggers_notice_after_success(monkeypatch):
|
||||
memory = make_async_memory()
|
||||
temporal_notice = AsyncMock()
|
||||
first_run_notice = AsyncMock()
|
||||
monkeypatch.setattr(memory_main, "capture_event", MagicMock())
|
||||
monkeypatch.setattr(memory_main, "display_temporal_usage_notice_async", temporal_notice)
|
||||
monkeypatch.setattr(memory_main, "display_first_run_notice_async", first_run_notice)
|
||||
|
||||
result = await AsyncMemory.search(memory, "what happened last week?", filters={"user_id": "u1"})
|
||||
|
||||
assert result == {"results": []}
|
||||
memory._search_vector_store.assert_awaited_once()
|
||||
temporal_notice.assert_awaited_once_with(memory, "async", "search", "query", "relative_phrase")
|
||||
first_run_notice.assert_not_awaited()
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user