chore: trigger CI build_mem0 / build_embedchain on cli-only PR

- Python: 0.2.4 → 0.2.5 in cli/python/pyproject.toml
- Node:   0.2.4 → 0.2.5 in cli/node/package.json
- docs/changelog/sdk.mdx: new CLI v0.2.5 entry (Agent Mode bootstrap,
  --agent-caller, mem0 identify, plugin sync, claim flow, pingKey fix,
  rate-limit clarity, JSON envelope command field, envelope validation)
- CI fix: ruff format applied to config.py + test_init_internals.py

Release flow: merge → tag cli-v0.2.5 (Python CD) and cli-node-v0.2.5
(Node CD) on GitHub Releases. OIDC trusted publishing — no tokens needed.

.claude-plugin/marketplace.json

@@ -12,7 +12,7 @@
       "name": "mem0",
       "source": "./mem0-plugin",
       "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
-      "version": "0.2.6"
+      "version": "0.1.2"
     }
   ]
 }

.cursor-plugin/marketplace.json

@@ -12,7 +12,7 @@
       "name": "mem0",
       "source": "./mem0-plugin",
       "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search.",
-      "version": "0.2.6"
+      "version": "0.1.1"
     }
   ]
 }

.github/workflows/ci.yml

@@ -3,7 +3,18 @@ name: ci
 on:
   push:
     branches: [main]
+    paths:
+      - 'mem0/**'
+      - 'tests/**'
+      - 'embedchain/**'
+      - '.github/workflows/**'
+      - 'pyproject.toml'
   pull_request:
+    paths:
+      - 'mem0/**'
+      - 'tests/**'
+      - 'embedchain/**'
+      - 'pyproject.toml'
 
 jobs:
   changelog_check:
@@ -49,8 +60,9 @@ jobs:
     runs-on: ubuntu-latest
     outputs:
       mem0_changed: ${{ steps.filter.outputs.mem0 }}
+      embedchain_changed: ${{ steps.filter.outputs.embedchain }}
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v3
     - uses: dorny/paths-filter@v2
       id: filter
       with:
@@ -58,28 +70,25 @@ jobs:
           mem0:
             - 'mem0/**'
             - 'tests/**'
-            - '.github/workflows/ci.yml'
+            - '.github/workflows/**'
             - 'pyproject.toml'
+          embedchain:
+            - 'embedchain/**'
 
   build_mem0:
     needs: check_changes
+    if: needs.check_changes.outputs.mem0_changed == 'true'
     runs-on: ubuntu-latest
     strategy:
       matrix:
         python-version: ["3.10", "3.11", "3.12"]
     steps:
-      - name: Skip — no relevant changes
-        if: needs.check_changes.outputs.mem0_changed != 'true'
-        run: echo "No changes in mem0/, tests/, pyproject.toml, or ci.yml — skipping"
-      - uses: actions/checkout@v4
-        if: needs.check_changes.outputs.mem0_changed == 'true'
+      - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python-version }}
-        if: needs.check_changes.outputs.mem0_changed == 'true'
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
       - name: Clean up disk space
-        if: needs.check_changes.outputs.mem0_changed == 'true'
         run: |
           df -h
           sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc /opt/hostedtoolcache/CodeQL
@@ -87,27 +96,61 @@ jobs:
           sudo docker builder prune -a
           df -h
       - name: Install Hatch
-        if: needs.check_changes.outputs.mem0_changed == 'true'
         run: pip install hatch
       - name: Load cached venv
-        if: needs.check_changes.outputs.mem0_changed == 'true'
         id: cached-hatch-dependencies
         uses: actions/cache@v3
         with:
           path: .venv
           key: venv-mem0-${{ runner.os }}-${{ hashFiles('**/pyproject.toml') }}
       - name: Install GEOS Libraries
-        if: needs.check_changes.outputs.mem0_changed == 'true'
         run: sudo apt-get update && sudo apt-get install -y libgeos-dev
       - name: Install dependencies
-        if: needs.check_changes.outputs.mem0_changed == 'true' && steps.cached-hatch-dependencies.outputs.cache-hit != 'true'
         run: |
           pip install --upgrade pip
           pip install -e ".[test,graph,vector_stores,llms,extras]"
           pip install ruff
+        if: steps.cached-hatch-dependencies.outputs.cache-hit != 'true'
       - name: Run Linting
-        if: needs.check_changes.outputs.mem0_changed == 'true'
         run: make lint
       - name: Run tests and generate coverage report
-        if: needs.check_changes.outputs.mem0_changed == 'true'
         run: make test
+
+  build_embedchain:
+    needs: check_changes
+    if: needs.check_changes.outputs.embedchain_changed == 'true'
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install Hatch
+        run: pip install hatch
+      - name: Load cached venv
+        id: cached-hatch-dependencies
+        uses: actions/cache@v3
+        with:
+          path: .venv
+          key: venv-embedchain-${{ runner.os }}-${{ hashFiles('**/pyproject.toml') }}
+      - name: Install dependencies
+        run: cd embedchain && make install_all
+        if: steps.cached-hatch-dependencies.outputs.cache-hit != 'true'
+      - name: Run Formatting
+        run: |
+          mkdir -p embedchain/.ruff_cache && chmod -R 777 embedchain/.ruff_cache
+          cd embedchain && hatch run format
+      - name: Lint with ruff
+        run: cd embedchain && make lint
+      - name: Run tests and generate coverage report
+        run: cd embedchain && make coverage
+      - name: Upload coverage reports to Codecov
+        uses: codecov/codecov-action@v3
+        with:
+          file: coverage.xml
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}

.gitignore

@@ -170,6 +170,7 @@ cython_debug/
 # Database
 db
 test-db
+!embedchain/embedchain/core/db/
 
 .vscode
 .idea/

AGENTS.md

@@ -33,6 +33,7 @@ This is a **polyglot monorepo** containing Python and TypeScript packages, CLIs,
 | `evaluation/` | Benchmarking framework — LOCOMO evals, experiment runner, score generation |
 | `examples/` | Sample projects — demo apps, Chrome extension, multi-agent patterns |
 | `cookbooks/` | Jupyter notebooks — customer support chatbot, AutoGen integration |
+| `embedchain/` | Legacy Embedchain RAG framework (maintained separately, Poetry-based) |
 | `pr-reviews/` | Pull request review materials |
 | `scripts/` | Repo-wide utility scripts (e.g., `check-llms-txt-coverage.py` for docs/llms.txt sync) |
 
@@ -329,7 +330,7 @@ make run-openai                    # OpenAI comparison
   - Root SDK: line length **120**
   - Python CLI: line length **100** with extended rule set (UP, B, SIM, RUF)
 - **isort** with `profile = "black"` for import sorting.
-- Ruff excludes `openmemory/` from root config.
+- Ruff excludes `embedchain/` and `openmemory/` from root config.
 
 ### TypeScript Conventions
 
@@ -413,6 +414,7 @@ To add a new LLM, embedding, vector store, or reranker provider:
 | Python CLI | `cli-python-ci.yml` | Push to `cli/python/`, PRs, manual | Ruff lint + pytest + hatch build on Python 3.10, 3.11, 3.12 |
 | Node CLI | `cli-node-ci.yml` | Push to `cli/node/`, PRs, manual | Biome lint + tsc + vitest + tsup build on Node 20, 22 |
 | OpenClaw | `openclaw-checks.yml` | Push to `openclaw/`, PRs, manual | tsc + vitest (with Codecov) + tsup build on Node 20, 22 |
+| Embedchain | `ci.yml` (shared) | PRs on `embedchain/` | Ruff + pytest + coverage on Python 3.9–3.12 |
 
 ### CD Workflows (automated publishing)
 
@@ -574,6 +576,7 @@ N/A
 - Modify CI/CD workflows without explicit approval.
 - Add new Python dependencies to the core `dependencies` list in `pyproject.toml` without discussion — use optional dependency groups instead.
 - Commit `.env` files, API keys, or credentials.
+- Modify `embedchain/` unless specifically working on that package — it has its own build system (Poetry).
 - Skip pre-commit hooks.
 - Use npm or yarn in TypeScript packages — this repo uses pnpm exclusively.
 - Use `require()` for imports in TypeScript — use ES module `import` syntax.

MIGRATION_GUIDE_v1.0.md

@@ -0,0 +1,221 @@
+# Migration Guide: Upgrading to mem0 1.0.0
+
+## TL;DR
+
+**What changed?** We simplified the API by removing confusing version parameters. Now everything returns a consistent format: `{"results": [...]}`.
+
+**What you need to do:**
+1. Upgrade: `pip install mem0ai==1.0.0`
+2. Remove `version` and `output_format` parameters from your code
+3. Update response handling to use `result["results"]` instead of treating responses as lists
+
+**Time needed:** ~5-10 minutes for most projects
+
+---
+
+## Quick Migration Guide
+
+### 1. Install the Update
+
+```bash
+pip install mem0ai==1.0.0
+```
+
+### 2. Update Your Code
+
+**If you're using the Memory API:**
+
+```python
+# Before
+memory = Memory(config=MemoryConfig(version="v1.1"))
+result = memory.add("I like pizza")
+
+# After
+memory = Memory()  # That's it - version is automatic now
+result = memory.add("I like pizza")
+```
+
+**If you're using the Client API:**
+
+```python
+# Before
+client.add(messages, output_format="v1.1")
+client.search(query, version="v2", output_format="v1.1")
+
+# After
+client.add(messages)  # Just remove those extra parameters
+client.search(query)
+```
+
+### 3. Update How You Handle Responses
+
+All responses now use the same format: a dictionary with `"results"` key.
+
+```python
+# Before - you might have done this
+result = memory.add("I like pizza")
+for item in result:  # Treating it as a list
+    print(item)
+
+# After - do this instead
+result = memory.add("I like pizza")
+for item in result["results"]:  # Access the results key
+    print(item)
+
+# Graph relations (if you use them)
+if "relations" in result:
+    for relation in result["relations"]:
+        print(relation)
+```
+
+---
+
+## Enhanced Message Handling
+
+The platform client (MemoryClient) now supports the same flexible message formats as the OSS version:
+
+```python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-key")
+
+# All three formats now work:
+
+# 1. Single string (automatically converted to user message)
+client.add("I like pizza", user_id="alice")
+
+# 2. Single message dictionary
+client.add({"role": "user", "content": "I like pizza"}, user_id="alice")
+
+# 3. List of messages (conversation)
+client.add([
+    {"role": "user", "content": "I like pizza"},
+    {"role": "assistant", "content": "I'll remember that!"}
+], user_id="alice")
+```
+
+### Async Mode Configuration
+
+The `async_mode` parameter now defaults to `True` but can be configured:
+
+```python
+# Default behavior (async_mode=True)
+client.add(messages, user_id="alice")
+
+# Explicitly set async mode
+client.add(messages, user_id="alice", async_mode=True)
+
+# Disable async mode if needed
+client.add(messages, user_id="alice", async_mode=False)
+```
+
+**Note:** `async_mode=True` provides better performance for most use cases. Only set it to `False` if you have specific synchronous processing requirements.
+
+---
+
+## That's It!
+
+For most users, that's all you need to know. The changes are:
+- ✅ No more `version` or `output_format` parameters
+- ✅ Consistent `{"results": [...]}` response format
+- ✅ Cleaner, simpler API
+
+---
+
+## Common Issues
+
+**Getting `KeyError: 'results'`?**
+
+Your code is still treating the response as a list. Update it:
+```python
+# Change this:
+for memory in response:
+
+# To this:
+for memory in response["results"]:
+```
+
+**Getting `TypeError: unexpected keyword argument`?**
+
+You're still passing old parameters. Remove them:
+```python
+# Change this:
+client.add(messages, output_format="v1.1")
+
+# To this:
+client.add(messages)
+```
+
+**Seeing deprecation warnings?**
+
+Remove any explicit `version="v1.0"` from your config:
+```python
+# Change this:
+memory = Memory(config=MemoryConfig(version="v1.0"))
+
+# To this:
+memory = Memory()
+```
+
+---
+
+## What's New in 1.0.0
+
+- **Better vector stores:** Fixed OpenSearch and improved reliability across all stores
+- **Cleaner API:** One way to do things, no more confusing options
+- **Enhanced GCP support:** Better Vertex AI configuration options
+- **Flexible message input:** Platform client now accepts strings, dicts, and lists (aligned with OSS)
+- **Configurable async_mode:** Now defaults to `True` but users can override if needed
+
+---
+
+## Need Help?
+
+- Check [GitHub Issues](https://github.com/mem0ai/mem0/issues)
+- Read the [documentation](https://docs.mem0.ai/)
+- Open a new issue if you're stuck
+
+---
+
+## Advanced: Configuration Changes
+
+**If you configured vector stores with version:**
+
+```python
+# Before
+config = MemoryConfig(
+    version="v1.1",
+    vector_store=VectorStoreConfig(...)
+)
+
+# After
+config = MemoryConfig(
+    vector_store=VectorStoreConfig(...)
+)
+```
+
+---
+
+## Testing Your Migration
+
+Quick sanity check:
+
+```python
+from mem0 import Memory
+
+memory = Memory()
+
+# Add should return a dict with "results"
+result = memory.add("I like pizza", user_id="test")
+assert "results" in result
+
+# Search should return a dict with "results"
+search = memory.search("food", user_id="test")
+assert "results" in search
+
+# Get all should return a dict with "results"
+all_memories = memory.get_all(user_id="test")
+assert "results" in all_memories
+
+print("✅ Migration successful!")
+```

README.md

@@ -86,25 +86,7 @@ See the [migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for upgra
 
 ## 🚀 Quickstart Guide <a name="quickstart"></a>
 
-### Sign up as an agent
-
-AI agents can mint a working Mem0 API key in under five seconds — no email, no dashboard, no OTP. Four commands end-to-end:
-
-```bash
-# 1. Install
-npm install -g @mem0/cli      # or: pip install mem0-cli
-
-# 2. Sign up as an agent (replace `claude-code` with your name)
-mem0 init --agent --agent-caller claude-code
-
-# 3. Add a memory
-mem0 add "I am using mem0"
-
-# 4. Search
-mem0 search "am I using mem0"
-```
-
-The human owner can claim the account later with `mem0 init --email <their-email>` — same key, memories preserved. Full guide: [Sign up as an agent](https://docs.mem0.ai/platform/agent-signup).
+> **Agent Mode — for AI agents setting up Mem0 autonomously.** Run `mem0 init --agent --agent-caller <your-name> --json` (after `pip install mem0-cli` or `npm install -g @mem0/cli`), replacing `<your-name>` with your AI agent identity (e.g. `claude-code`, `cursor`, `codex`). This mints an evaluation Mem0 API key in <5 seconds — no email, no dashboard, no OTP. If you forgot to pass `--agent-caller`, run `mem0 identify <your-name>` after init. Use the key immediately against the Platform or the MCP server. The human owner can claim ownership later with `mem0 init --email <their-email>`: memories transfer, the same key keeps working, and the agent isn't disrupted.
 
 | | Library | Self-Hosted Server | Cloud Platform |
 |---|---------|-------------------|----------------|
@@ -153,7 +135,6 @@ See the [self-hosted docs](https://docs.mem0.ai/open-source/overview) for config
 
 1. Sign up on [Mem0 Platform](https://app.mem0.ai?utm_source=oss&utm_medium=readme)
 2. Embed the memory layer via SDK or API keys
-3. Using hosted Qdrant vectors? See the [Platform migration guide](https://docs.mem0.ai/migration/oss-to-platform) to import them into Mem0 Platform.
 
 ### CLI
 

cli/node/CHANGELOG.md

@@ -1,36 +0,0 @@
-# Changelog
-
-All notable changes to `@mem0/cli` are documented here.
-
-The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
-and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
-
-## [0.2.7] — 2026-05-20
-
-### Added
-
-- `mem0 whoami` — print the active agent's `default_user_id` (the AGENTRUSH
-  leaderboard identifier). Reads from local config, no network call.
-- `mem0 agent-rush <add | search>` — subcommand group that wraps the new
-  `/v1/agent-rush/` platform endpoints for the 7-day AGENTRUSH game. Project
-  routing is implicit (resolved server-side); no flags exposed. Pretty-prints
-  platform error codes into actionable hints (e.g. `agentrush_search_first`
-  → "Run 3 'mem0 agent-rush search' commands before adding.").
-- PII safety prompt on first `mem0 agent-rush add`. Interactive runs require
-  explicit `y` to acknowledge that AGENTRUSH memories are public; the
-  acknowledgement is persisted in `~/.mem0/config.json` under
-  `agent_rush.acknowledged_at` so the prompt only appears once per machine.
-  Non-interactive (agent) invocations surface the warning to stderr without
-  blocking.
-- New config schema field: `agent_rush.acknowledged_at` (ISO timestamp,
-  empty until first interactive acknowledgement).
-
-### Changed
-
-- HTTP requests from the new agent-rush commands send `X-Mem0-Mode: agent-rush`
-  in addition to the existing source headers, so platform telemetry can split
-  game traffic from regular CLI usage.
-
-## [0.2.6] and earlier
-
-Unlogged historical releases. See git history under `cli/node/`.

cli/node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@mem0/cli",
-  "version": "0.2.7",
+  "version": "0.2.5",
   "description": "The official CLI for mem0 — the memory layer for AI agents",
   "type": "module",
   "bin": {

cli/node/src/commands/agent-mode.ts

@@ -247,7 +247,7 @@ export async function claimViaOtp(
 		printError(`Claim failed: ${detail}`);
 		if (errCode === "email_already_claimed") {
 			console.log(
-				`  ${dim("Tip: this email already has a Mem0 account. Sign in at app.mem0.ai with your existing credentials.")}`,
+				`  ${dim("Tip: this email already has a Mem0 account. Sign in there and run `mem0 link <key>` to attach this agent.")}`,
 			);
 		}
 		process.exit(1);

cli/node/src/commands/agent-rush.ts

@@ -1,147 +0,0 @@
-/**
- * `mem0 agent-rush <add|search> "..."` — wraps the AGENTRUSH platform endpoints.
- * Project routing is implicit (server-side); zero flags needed.
- */
-
-import readline from "node:readline";
-import { colors, printError, printSuccess } from "../branding.js";
-import { loadConfig, saveConfig } from "../config.js";
-import { CLI_VERSION } from "../version.js";
-
-const PII_WARNING = [
-	"",
-	"⚠️  AGENTRUSH memories are PUBLIC — visible to any other player.",
-	"   Do not include real names, emails, secrets, work content, or PII.",
-	"",
-].join("\n");
-
-const ERROR_HINTS: Record<string, string> = {
-	agentrush_search_first:
-		"Run 3 'mem0 agent-rush search' commands before adding.",
-	agentrush_search_quota: "You've used your 3 lifetime searches.",
-	agentrush_add_quota: "You've used your 3 lifetime adds.",
-	agentrush_not_agent_mode:
-		"Re-run 'mem0 init --agent' to bootstrap an agent-mode key.",
-	agentrush_length: "Memory text must be 50-1000 characters.",
-	agentrush_no_urls: "URLs are not allowed.",
-	agentrush_blocklist: "Content contains a blocked term.",
-	agentrush_global_quota: "Event-wide cap reached. Try again later.",
-	agentrush_not_provisioned:
-		"AGENTRUSH is not provisioned in this environment.",
-};
-
-async function callEndpoint(
-	path: string,
-	body: Record<string, unknown>,
-): Promise<unknown> {
-	const config = loadConfig();
-	const baseUrl = (config.platform?.baseUrl ?? "https://api.mem0.ai").replace(
-		/\/+$/,
-		"",
-	);
-
-	if (!config.platform?.apiKey) {
-		printError("Not initialized. Run `mem0 init --agent` first.");
-		process.exit(1);
-	}
-
-	const resp = await fetch(`${baseUrl}${path}`, {
-		method: "POST",
-		headers: {
-			Authorization: `Token ${config.platform.apiKey}`,
-			"Content-Type": "application/json",
-			"X-Mem0-Source": "cli",
-			"X-Mem0-Client-Language": "node",
-			"X-Mem0-Client-Version": CLI_VERSION,
-			"X-Mem0-Mode": "agent-rush",
-		},
-		body: JSON.stringify(body),
-		signal: AbortSignal.timeout(30_000),
-	});
-
-	const json = await resp.json().catch(() => ({}));
-
-	if (!resp.ok) {
-		const code =
-			(json as { error?: { code?: string } }).error?.code ?? "unknown";
-		printError(`AGENTRUSH error: ${code}`);
-		if (ERROR_HINTS[code]) {
-			console.log(`  ${colors.dim(ERROR_HINTS[code])}`);
-		}
-		process.exit(1);
-	}
-
-	return json;
-}
-
-function promptLine(question: string): Promise<string> {
-	const rl = readline.createInterface({
-		input: process.stdin,
-		output: process.stdout,
-	});
-	return new Promise((resolve) => {
-		rl.question(question, (answer) => {
-			rl.close();
-			resolve(answer.trim());
-		});
-	});
-}
-
-/**
- * Ensure the human has acknowledged that AGENTRUSH memories are PUBLIC.
- *
- * Interactive (TTY): show the prompt; on "y" persist `agentRush.acknowledgedAt`
- * so we never ask the same machine twice. On anything else, abort.
- *
- * Non-interactive (agent invocation, no TTY): print the warning to stderr
- * for the human reading the agent's transcript and proceed — agents can't
- * answer y/N prompts.
- */
-async function ensureWarningAcknowledged(): Promise<void> {
-	const config = loadConfig();
-	if (config.agentRush?.acknowledgedAt) return;
-
-	if (!process.stdin.isTTY || !process.stdout.isTTY) {
-		// Agent context: surface the warning to stderr, don't block.
-		console.error(PII_WARNING);
-		return;
-	}
-
-	console.log(PII_WARNING);
-	const answer = (await promptLine("   Continue? [y/N]: ")).toLowerCase();
-	if (answer !== "y" && answer !== "yes") {
-		printError("Aborted.");
-		process.exit(1);
-	}
-
-	config.agentRush.acknowledgedAt = new Date().toISOString();
-	saveConfig(config);
-}
-
-export async function cmdAgentRushAdd(content: string): Promise<void> {
-	await ensureWarningAcknowledged();
-	const result = await callEndpoint("/v1/agent-rush/memories/", { content });
-	printSuccess(
-		`Memory submitted (event_id: ${(result as { event_id?: string }).event_id ?? "?"})`,
-	);
-}
-
-export async function cmdAgentRushSearch(query: string): Promise<void> {
-	const result = (await callEndpoint("/v1/agent-rush/memories/search/", {
-		query,
-	})) as {
-		results?: Array<{ memory?: string }>;
-		memories?: Array<{ memory?: string }>;
-	};
-
-	const memories = result.results ?? result.memories ?? [];
-
-	if (memories.length === 0) {
-		console.log(colors.dim("(no results)"));
-		return;
-	}
-
-	memories.slice(0, 5).forEach((m, i) => {
-		console.log(`  ${i + 1}. ${m.memory ?? JSON.stringify(m)}`);
-	});
-}

cli/node/src/commands/memory.ts

@@ -46,7 +46,7 @@ export async function cmdAdd(
 		file?: string;
 		metadata?: string;
 		immutable: boolean;
-		infer?: boolean;
+		noInfer: boolean;
 		expires?: string;
 		categories?: string;
 		output: string;
@@ -136,7 +136,7 @@ export async function cmdAdd(
 				runId: opts.runId,
 				metadata: meta,
 				immutable: opts.immutable,
-				infer: opts.infer !== false,
+				infer: !opts.noInfer,
 				expires: opts.expires,
 				categories: cats,
 			});

cli/node/src/commands/whoami.ts

@@ -1,18 +0,0 @@
-/**
- * `mem0 whoami` — print the active agent's default_user_id (AGENTRUSH identifier).
- * Reads from local config; no network call.
- */
-
-import { colors, printError, printInfo } from "../branding.js";
-import { loadConfig } from "../config.js";
-
-export async function cmdWhoami(): Promise<void> {
-	const config = loadConfig();
-	const sessionId = config.platform?.defaultUserId;
-	if (!sessionId) {
-		printError("No default_user_id found. Run `mem0 init --agent` first.");
-		process.exit(1);
-	}
-	console.log(`Your AGENTRUSH identifier:  ${colors.brand(sessionId)}`);
-	printInfo("Find your row at https://mem0.ai/agentrush");
-}

cli/node/src/config.ts

@@ -40,18 +40,11 @@ export interface TelemetryConfig {
 	anonymousId: string;
 }
 
-export interface AgentRushConfig {
-	// ISO timestamp the human acknowledged the "memories are public" warning.
-	// Empty until first interactive `mem0 agent-rush add`.
-	acknowledgedAt: string;
-}
-
 export interface Mem0Config {
 	version: number;
 	defaults: DefaultsConfig;
 	platform: PlatformConfig;
 	telemetry: TelemetryConfig;
-	agentRush: AgentRushConfig;
 }
 
 export function createDefaultConfig(): Mem0Config {
@@ -76,9 +69,6 @@ export function createDefaultConfig(): Mem0Config {
 		telemetry: {
 			anonymousId: "",
 		},
-		agentRush: {
-			acknowledgedAt: "",
-		},
 	};
 }
 
@@ -113,8 +103,6 @@ export function loadConfig(): Mem0Config {
 		config.defaults.runId = defaults.run_id ?? "";
 		const telemetry = data.telemetry ?? {};
 		config.telemetry.anonymousId = telemetry.anonymous_id ?? "";
-		const agentRush = data.agent_rush ?? {};
-		config.agentRush.acknowledgedAt = agentRush.acknowledged_at ?? "";
 	}
 
 	// Environment variable overrides
@@ -155,9 +143,6 @@ export function saveConfig(config: Mem0Config): void {
 		telemetry: {
 			anonymous_id: config.telemetry.anonymousId,
 		},
-		agent_rush: {
-			acknowledged_at: config.agentRush.acknowledgedAt,
-		},
 	};
 
 	fs.writeFileSync(CONFIG_FILE, JSON.stringify(data, null, 2));

cli/node/src/index.ts

@@ -262,48 +262,6 @@ program
 		await runIdentify(name);
 	});
 
-// ── Setup: whoami (print active agent identifier) ────────────────────────
-
-program
-	.command("whoami")
-	.description("Print the active agent's AGENTRUSH identifier.")
-	.action(async () => {
-		const { cmdWhoami } = await import("./commands/whoami.js");
-		await cmdWhoami();
-	});
-
-// ── AGENTRUSH subcommand group ────────────────────────────────────────────
-
-const agentRush = program
-	.command("agent-rush")
-	.description("AGENTRUSH game commands.")
-	.addHelpCommand(false)
-	.configureHelp({ formatHelp: richFormatHelp });
-
-agentRush
-	.command("add <content...>")
-	.description("Submit a memory to AGENTRUSH.")
-	.addHelpText(
-		"after",
-		'\nExamples:\n  $ mem0 agent-rush add "I used mem0 to build a coding agent"\n  $ mem0 agent-rush add "Agents that remember are better agents"',
-	)
-	.action(async (parts: string[]) => {
-		const { cmdAgentRushAdd } = await import("./commands/agent-rush.js");
-		await cmdAgentRushAdd(parts.join(" "));
-	});
-
-agentRush
-	.command("search <query...>")
-	.description("Search AGENTRUSH memories.")
-	.addHelpText(
-		"after",
-		'\nExamples:\n  $ mem0 agent-rush search "agents and memory and tools"\n  $ mem0 agent-rush search "coding assistant"',
-	)
-	.action(async (parts: string[]) => {
-		const { cmdAgentRushSearch } = await import("./commands/agent-rush.js");
-		await cmdAgentRushSearch(parts.join(" "));
-	});
-
 // ── Memory: add ───────────────────────────────────────────────────────────
 
 program

cli/node/tests/commands.test.ts

@@ -3,7 +3,6 @@
  */
 
 import { describe, it, expect, vi, beforeEach } from "vitest";
-import { Command } from "commander";
 import { createMockBackend } from "./setup.js";
 import type { Backend } from "../src/backend/base.js";
 import { setAgentMode } from "../src/state.js";
@@ -42,6 +41,8 @@ describe("cmdAdd", () => {
     await cmdAdd(mockBackend, "I prefer dark mode", {
       userId: "alice",
       immutable: false,
+      noInfer: false,
+
       output: "text",
     });
     expect(mockBackend.add).toHaveBeenCalledOnce();
@@ -53,6 +54,8 @@ describe("cmdAdd", () => {
       userId: "alice",
       messages: JSON.stringify([{ role: "user", content: "I love Python" }]),
       immutable: false,
+      noInfer: false,
+
       output: "text",
     });
     expect(mockBackend.add).toHaveBeenCalledOnce();
@@ -63,6 +66,8 @@ describe("cmdAdd", () => {
     await cmdAdd(mockBackend, "test", {
       userId: "alice",
       immutable: false,
+      noInfer: false,
+
       output: "json",
     });
     expect(output).toContain("results");
@@ -73,59 +78,14 @@ describe("cmdAdd", () => {
     await cmdAdd(mockBackend, "test", {
       userId: "alice",
       immutable: false,
+      noInfer: false,
+
       output: "quiet",
     });
     expect(output).not.toContain("dark mode");
   });
 });
 
-describe("cmdAdd forwards --no-infer (regression for #5261)", () => {
-  it("forwards infer: false when --no-infer is set", async () => {
-    const { cmdAdd } = await import("../src/commands/memory.js");
-    // `infer: false` is the shape Commander produces for `--no-infer`.
-    await cmdAdd(mockBackend, "store me verbatim", {
-      userId: "alice",
-      immutable: false,
-      infer: false,
-      output: "text",
-    });
-    expect(mockBackend.add).toHaveBeenCalledWith(
-      "store me verbatim",
-      undefined,
-      expect.objectContaining({ infer: false }),
-    );
-  });
-
-  it("forwards infer: true by default (flag absent)", async () => {
-    const { cmdAdd } = await import("../src/commands/memory.js");
-    await cmdAdd(mockBackend, "infer me", {
-      userId: "alice",
-      immutable: false,
-      output: "text",
-    });
-    expect(mockBackend.add).toHaveBeenCalledWith(
-      "infer me",
-      undefined,
-      expect.objectContaining({ infer: true }),
-    );
-  });
-
-  it("Commander stores --no-infer as opts.infer, not opts.noInfer", () => {
-    // Pins the assumption the fix relies on: Commander's `--no-X` option
-    // populates the positive camelCase key (`infer`), never `noInfer`.
-    const withFlag = new Command();
-    withFlag.option("--no-infer", "Skip inference, store raw.").action(() => {});
-    withFlag.parse(["--no-infer"], { from: "user" });
-    expect(withFlag.opts().infer).toBe(false);
-    expect(withFlag.opts().noInfer).toBeUndefined();
-
-    const withoutFlag = new Command();
-    withoutFlag.option("--no-infer", "Skip inference, store raw.").action(() => {});
-    withoutFlag.parse([], { from: "user" });
-    expect(withoutFlag.opts().infer).toBe(true);
-  });
-});
-
 describe("cmdAdd deduplicates PENDING", () => {
   const DUPLICATE_PENDING = {
     results: [
@@ -140,6 +100,8 @@ describe("cmdAdd deduplicates PENDING", () => {
     await cmdAdd(mockBackend, "test", {
       userId: "alice",
       immutable: false,
+      noInfer: false,
+
       output: "text",
     });
     expect(output.match(/Queued/g)?.length).toBe(1);
@@ -151,6 +113,8 @@ describe("cmdAdd deduplicates PENDING", () => {
     await cmdAdd(mockBackend, "test", {
       userId: "alice",
       immutable: false,
+      noInfer: false,
+
       output: "json",
     });
     const data = JSON.parse(output);
@@ -165,6 +129,8 @@ describe("cmdAdd deduplicates PENDING", () => {
     await cmdAdd(mockBackend, "test", {
       userId: "alice",
       immutable: false,
+      noInfer: false,
+
       output: "agent",
     });
     const data = JSON.parse(output);
@@ -349,6 +315,8 @@ describe("agent mode", () => {
     await cmdAdd(mockBackend, "test preference", {
       userId: "alice",
       immutable: false,
+      noInfer: false,
+
       output: "agent",
     });
     const parsed = JSON.parse(output.trim());

cli/python/CHANGELOG.md

@@ -1,36 +0,0 @@
-# Changelog
-
-All notable changes to `mem0-cli` (Python) are documented here.
-
-The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
-and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
-
-## [0.2.7] — 2026-05-20
-
-### Added
-
-- `mem0 whoami` — print the active agent's `default_user_id` (the AGENTRUSH
-  leaderboard identifier). Reads from local config, no network call.
-- `mem0 agent-rush <add | search>` — subcommand group that wraps the new
-  `/v1/agent-rush/` platform endpoints for the 7-day AGENTRUSH game. Project
-  routing is implicit (resolved server-side); no flags exposed. Pretty-prints
-  platform error codes into actionable hints (e.g. `agentrush_search_first`
-  → "Run 3 'mem0 agent-rush search' commands before adding.").
-- PII safety prompt on first `mem0 agent-rush add`. Interactive runs require
-  explicit `y` to acknowledge that AGENTRUSH memories are public; the
-  acknowledgement is persisted in `~/.mem0/config.json` under
-  `agent_rush.acknowledged_at` so the prompt only appears once per machine.
-  Non-interactive (agent) invocations surface the warning to stderr without
-  blocking.
-- New config schema field: `agent_rush.acknowledged_at` (ISO timestamp,
-  empty until first interactive acknowledgement).
-
-### Changed
-
-- HTTP requests from the new agent-rush commands send `X-Mem0-Mode: agent-rush`
-  in addition to the existing source headers, so platform telemetry can split
-  game traffic from regular CLI usage.
-
-## [0.2.6] and earlier
-
-Unlogged historical releases. See git history under `cli/python/`.

cli/python/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "mem0-cli"
-version = "0.2.7"
+version = "0.2.5"
 description = "The official CLI for mem0 — the memory layer for AI agents"
 readme = "README.md"
 license = "Apache-2.0"

cli/python/src/mem0_cli/app.py

@@ -914,65 +914,6 @@ def identify(
     run_identify(name)
 
 
-@app.command(name="whoami", rich_help_panel="Setup")
-def whoami_cmd() -> None:
-    """Print your AGENTRUSH identifier (default_user_id).
-
-    Example:
-      mem0 whoami
-    """
-    from mem0_cli.commands.whoami_cmd import run_whoami
-
-    run_whoami()
-
-
-# ── AGENTRUSH sub-app ─────────────────────────────────────────────────────
-
-agent_rush_app = typer.Typer(
-    name="agent-rush",
-    help="AGENTRUSH game commands",
-    no_args_is_help=True,
-    rich_markup_mode="rich",
-)
-
-
-@agent_rush_app.callback(invoke_without_command=True)
-def _agent_rush_callback(ctx: typer.Context) -> None:
-    if ctx.invoked_subcommand:
-        _fire_telemetry(f"agent-rush.{ctx.invoked_subcommand}")
-
-
-@agent_rush_app.command(name="add")
-def agent_rush_add(
-    content: str = typer.Argument(..., help="Memory content (50-1000 characters, no URLs)."),
-) -> None:
-    """Submit a memory to AGENTRUSH.
-
-    Example:
-      mem0 agent-rush add "I enjoy solving constraint-satisfaction problems."
-    """
-    from mem0_cli.commands.agent_rush_cmd import run_agent_rush_add
-
-    run_agent_rush_add(content)
-
-
-@agent_rush_app.command(name="search")
-def agent_rush_search(
-    query: str = typer.Argument(..., help="Search query."),
-) -> None:
-    """Search AGENTRUSH memories.
-
-    Example:
-      mem0 agent-rush search "constraint satisfaction"
-    """
-    from mem0_cli.commands.agent_rush_cmd import run_agent_rush_search
-
-    run_agent_rush_search(query)
-
-
-app.add_typer(agent_rush_app, name="agent-rush", rich_help_panel="Setup")
-
-
 # (entity_app registered at module level, below sub-group definitions)
 
 

cli/python/src/mem0_cli/commands/agent_mode_cmd.py

@@ -218,7 +218,7 @@ def claim_via_otp(config: Mem0Config, *, email: str, code: str | None = None) ->
         print_error(err_console, f"Claim failed: {detail}")
         if code_str == "email_already_claimed":
             console.print(
-                f"  [{DIM_COLOR}]Tip: this email already has a Mem0 account. Sign in at app.mem0.ai with your existing credentials.[/]"
+                f"  [{DIM_COLOR}]Tip: this email already has a Mem0 account. Sign in there and run `mem0 link <key>` to attach this agent.[/]"
             )
         raise typer.Exit(1)
 

cli/python/src/mem0_cli/commands/agent_rush_cmd.py

@@ -1,132 +0,0 @@
-"""mem0 agent-rush — AGENTRUSH game commands.
-
-Wraps the platform's /v1/agent-rush/{memories/, memories/search/} endpoints.
-Hardcoded routing; no flags needed.
-"""
-
-from __future__ import annotations
-
-import sys
-from datetime import datetime, timezone
-
-import httpx
-import typer
-from rich.console import Console
-
-from mem0_cli.branding import print_error, print_success
-from mem0_cli.config import load_config, save_config
-
-console = Console()
-err_console = Console(stderr=True)
-
-_PII_WARNING_LINES = (
-    "",
-    "[yellow]⚠️  AGENTRUSH memories are PUBLIC — visible to any other player.[/yellow]",
-    "[yellow]   Do not include real names, emails, secrets, work content, or PII.[/yellow]",
-    "",
-)
-
-_SOURCE_HEADERS = {
-    "X-Mem0-Source": "cli",
-    "X-Mem0-Client-Language": "python",
-    "X-Mem0-Mode": "agent-rush",
-}
-
-_ERROR_HINTS = {
-    "agentrush_search_first": "Run 3 'mem0 agent-rush search' commands before adding.",
-    "agentrush_search_quota": "You've used your 3 lifetime searches.",
-    "agentrush_add_quota": "You've used your 3 lifetime adds.",
-    "agentrush_not_agent_mode": "Re-run 'mem0 init --agent' to bootstrap an agent-mode key.",
-    "agentrush_length": "Memory text must be 50-1000 characters.",
-    "agentrush_no_urls": "URLs are not allowed.",
-    "agentrush_blocklist": "Content contains a blocked term.",
-    "agentrush_global_quota": "Event-wide cap reached. Try again later.",
-    "agentrush_not_provisioned": "AGENTRUSH is not provisioned in this environment.",
-}
-
-
-def _call(path: str, body: dict) -> dict:
-    config = load_config()
-    if not config.platform.api_key:
-        print_error(err_console, "Not initialized. Run `mem0 init --agent` first.")
-        raise typer.Exit(1)
-    base_url = (config.platform.base_url or "https://api.mem0.ai").rstrip("/")
-    try:
-        with httpx.Client(timeout=30.0) as client:
-            resp = client.post(
-                f"{base_url}{path}",
-                headers={
-                    **_SOURCE_HEADERS,
-                    "Authorization": f"Token {config.platform.api_key}",
-                    "Content-Type": "application/json",
-                },
-                json=body,
-            )
-    except httpx.HTTPError as exc:
-        print_error(err_console, f"Network error: {exc}")
-        raise typer.Exit(1) from exc
-    try:
-        data = resp.json()
-    except Exception:
-        data = {}
-    if resp.status_code >= 400:
-        code = (
-            (data.get("error") or {}).get("code", "unknown")
-            if isinstance(data, dict)
-            else "unknown"
-        )
-        print_error(err_console, f"AGENTRUSH error: {code}")
-        hint = _ERROR_HINTS.get(code)
-        if hint:
-            console.print(f"  [dim]{hint}[/dim]")
-        raise typer.Exit(1)
-    return data
-
-
-def _ensure_warning_acknowledged() -> None:
-    """Block the first interactive add on the PII warning; pass-through for agents.
-
-    Interactive (TTY): show prompt, require explicit 'y', persist
-    `agent_rush.acknowledged_at` so we never ask the same machine twice.
-
-    Non-interactive (no TTY — typical when an agent runs the CLI): surface
-    the warning to stderr for the human reading the agent transcript and
-    proceed without prompting (agents can't answer y/N).
-    """
-    config = load_config()
-    if config.agent_rush.acknowledged_at:
-        return
-
-    is_tty = sys.stdin.isatty() and sys.stdout.isatty()
-    if not is_tty:
-        for line in _PII_WARNING_LINES:
-            err_console.print(line)
-        return
-
-    for line in _PII_WARNING_LINES:
-        console.print(line)
-    answer = typer.prompt("   Continue? [y/N]", default="N", show_default=False).strip().lower()
-    if answer not in ("y", "yes"):
-        print_error(err_console, "Aborted.")
-        raise typer.Exit(1)
-
-    config.agent_rush.acknowledged_at = datetime.now(timezone.utc).isoformat()
-    save_config(config)
-
-
-def run_agent_rush_add(content: str) -> None:
-    _ensure_warning_acknowledged()
-    result = _call("/v1/agent-rush/memories/", {"content": content})
-    event_id = result.get("event_id", "?")
-    print_success(console, f"Memory submitted (event_id: {event_id})")
-
-
-def run_agent_rush_search(query: str) -> None:
-    result = _call("/v1/agent-rush/memories/search/", {"query": query})
-    memories = result.get("results") or result.get("memories") or []
-    if not memories:
-        console.print("[dim](no results)[/dim]")
-        return
-    for i, m in enumerate(memories[:5], start=1):
-        text = m.get("memory") if isinstance(m, dict) else str(m)
-        console.print(f"  {i}. {text}")

cli/python/src/mem0_cli/commands/whoami_cmd.py

@@ -1,25 +0,0 @@
-"""mem0 whoami — print the active agent's default_user_id (AGENTRUSH identifier)."""
-
-from __future__ import annotations
-
-import typer
-from rich.console import Console
-
-from mem0_cli.branding import BRAND_COLOR, print_error, print_info
-from mem0_cli.config import load_config
-
-console = Console()
-err_console = Console(stderr=True)
-
-
-def run_whoami() -> None:
-    config = load_config()
-    session_id = config.platform.default_user_id if config.platform else None
-    if not session_id:
-        print_error(
-            err_console,
-            "No default_user_id found. Run `mem0 init --agent` first.",
-        )
-        raise typer.Exit(1)
-    console.print(f"Your AGENTRUSH identifier:  [{BRAND_COLOR}]{session_id}[/{BRAND_COLOR}]")
-    print_info(console, "Find your row at https://mem0.ai/agentrush")

cli/python/src/mem0_cli/config.py

@@ -51,20 +51,12 @@ class TelemetryConfig:
     anonymous_id: str = ""
 
 
-@dataclass
-class AgentRushConfig:
-    # ISO timestamp the human acknowledged the "memories are public" warning.
-    # Empty until first interactive `mem0 agent-rush add`.
-    acknowledged_at: str = ""
-
-
 @dataclass
 class Mem0Config:
     version: int = CONFIG_VERSION
     defaults: DefaultsConfig = field(default_factory=DefaultsConfig)
     platform: PlatformConfig = field(default_factory=PlatformConfig)
     telemetry: TelemetryConfig = field(default_factory=TelemetryConfig)
-    agent_rush: AgentRushConfig = field(default_factory=AgentRushConfig)
 
 
 SHORT_KEY_ALIASES: dict[str, str] = {
@@ -113,9 +105,6 @@ def load_config() -> Mem0Config:
         telemetry = data.get("telemetry", {})
         config.telemetry.anonymous_id = telemetry.get("anonymous_id", "")
 
-        agent_rush = data.get("agent_rush", {})
-        config.agent_rush.acknowledged_at = agent_rush.get("acknowledged_at", "")
-
     # Environment variable overrides
     env_key = os.environ.get("MEM0_API_KEY")
     if env_key:
@@ -169,9 +158,6 @@ def save_config(config: Mem0Config) -> None:
         "telemetry": {
             "anonymous_id": config.telemetry.anonymous_id,
         },
-        "agent_rush": {
-            "acknowledged_at": config.agent_rush.acknowledged_at,
-        },
     }
 
     with open(CONFIG_FILE, "w") as f:

docs/changelog/sdk.mdx

@@ -7,21 +7,6 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
-<Update label="2026-05-27" description="v2.0.4">
-
-**New Features:**
-- **Client:** `delete()` and async `delete()` accept `delete_linked` (default `False`). When `True`, deleting a memory also removes the older memories it superseded (the v3 `linked_memory_ids` chain), transitively — the delete-side counterpart of `latest_only`, so a superseded memory does not resurface after the current one is deleted ([#5270](https://github.com/mem0ai/mem0/pull/5270))
-
-</Update>
-
-<Update label="2026-05-26" description="v2.0.3">
-
-**Bug Fixes:**
-- **Vector Stores:** PGVector adapter now supports rich filter operators (`eq`, `ne`, `gt`, `gte`, `lt`, `lte`, `in`, `nin`, `contains`, `icontains`, wildcard `*`, `$or`, `$not`) in `search()`, `keyword_search()`, and `list()`. Previously only exact-equality filters worked — operator dicts were silently stringified and returned zero results ([#5263](https://github.com/mem0ai/mem0/pull/5263))
-- **Server:** Fixed `/search` endpoint returning 502 when `user_id`, `agent_id`, or `run_id` are sent as top-level request fields. The server now maps these into the `filters` dict before calling `Memory.search()`, matching the v3 API contract. Top-level entity ID fields are marked as deprecated in the OpenAPI schema and emit a warning log — clients should migrate to `filters={"user_id": "..."}` ([#5263](https://github.com/mem0ai/mem0/pull/5263))
-
-</Update>
-
 <Update label="2026-05-08" description="v2.0.2">
 
 **Bug Fixes:**
@@ -939,20 +924,6 @@ See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-
 </Tab>
 
 <Tab title="TypeScript">
-<Update label="2026-05-27" description="v3.0.5">
-
-**New Features:**
-- **Client:** `delete()` accepts an options object with `deleteLinked` (serialized as `delete_linked`, default `false`). When `true`, deleting a memory also removes the older memories it superseded (the v3 linked chain), transitively — the delete-side counterpart of `latestOnly`, so a superseded memory does not resurface after the current one is deleted ([#5270](https://github.com/mem0ai/mem0/pull/5270))
-
-</Update>
-
-<Update label="2026-05-26" description="v3.0.4">
-
-**Bug Fixes:**
-- **Vector Stores:** PGVector adapter now supports rich filter operators (`eq`, `ne`, `gt`, `gte`, `lt`, `lte`, `in`, `nin`, `contains`, `icontains`, wildcard `*`, `$or`, `$not`) in `search()`, `keywordSearch()`, and `list()`. Previously only exact-equality filters worked — operator objects were passed as raw values and returned incorrect results ([#5263](https://github.com/mem0ai/mem0/pull/5263))
-
-</Update>
-
 <Update label="2026-05-08" description="v3.0.3">
 
 **Bug Fixes:**
@@ -1352,17 +1323,10 @@ See the [TypeScript SDK migration guide](https://docs.mem0.ai/migration/ts-v2-to
 
 <Tab title="CLI">
 
-<Update label="2026-05-16" description="Python v0.2.6 / Node v0.2.6">
-
-**Bug Fixes:**
-- **Claim flow error message:** The `email_already_claimed` tip in `mem0 init --email` previously suggested running `mem0 link <key>` — a command that doesn't exist. Replaced with honest copy pointing the user to sign in at app.mem0.ai with their existing credentials ([#5152](https://github.com/mem0ai/mem0/pull/5152))
-
-</Update>
-
 <Update label="2026-05-14" description="Python v0.2.5 / Node v0.2.5">
 
 **New Features:**
-- **Agent Mode (`mem0 init --agent`):** Zero-friction signup for AI agents — mints a working Mem0 API key in under 5 seconds with no email, no dashboard, no OTP. Returns an unclaimed shadow account the human can later claim with `mem0 init --email <their-email>` (memories preserved, same key keeps working) ([#5123](https://github.com/mem0ai/mem0/pull/5123))
+- **Agent Mode (`mem0 init --agent`):** Zero-friction signup for AI agents — mints a working Mem0 API key in <5s with no email, no dashboard, no OTP. Returns an unclaimed shadow account the human can later claim with `mem0 init --email <their-email>` (memories preserved, same key keeps working) ([#5123](https://github.com/mem0ai/mem0/pull/5123))
 - **Self-declared agent identity:** Agents pass `--agent-caller <name>` (e.g. `claude-code`, `cursor`, `codex`) on `mem0 init --agent` so signups attribute to the right tool in analytics. Proof Editor-style — the agent declares itself rather than the CLI sniffing it from env vars ([#5123](https://github.com/mem0ai/mem0/pull/5123))
 - **`mem0 identify <name>`:** New subcommand to self-tag an Agent Mode key after the fact when the agent forgot to pass `--agent-caller` on init. Idempotent — re-running just overwrites ([#5123](https://github.com/mem0ai/mem0/pull/5123))
 - **Plugin sync:** `~/.claude/settings.json::env::MEM0_API_KEY` and `~/.zshrc`/`.bashrc` `export MEM0_API_KEY=` lines stay in sync with `~/.mem0/config.json` automatically. Idempotent — only updates EXISTING entries, never creates new ones ([#5123](https://github.com/mem0ai/mem0/pull/5123))

docs/docs.json

@@ -40,7 +40,6 @@
                     "icon": "rocket",
                     "pages": [
                       "platform/overview",
-                      "platform/agent-signup",
                       "vibecoding",
                       "platform/mem0-mcp",
                       "platform/cli",

docs/integrations/claude-code.mdx

@@ -22,25 +22,10 @@ Before setting up Mem0 with Claude Code, ensure you have:
 
 2. Claude Code CLI or Claude Cowork desktop app installed
 
-3. Your API key added to your shell profile (persists across sessions):
-
-<CodeGroup>
-```bash zsh
-echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.zshrc
-source ~/.zshrc
-```
-
-```bash bash
-echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.bashrc
-source ~/.bashrc
-```
-</CodeGroup>
-
-Confirm it's set:
+3. Your API key exported in your shell:
 
 ```bash
-echo $MEM0_API_KEY
-# Should print: m0-your-api-key
+export MEM0_API_KEY="m0-your-api-key"
 ```
 
 ## Installation
@@ -99,39 +84,6 @@ Add to your Claude Code MCP config (`.mcp.json`):
   Start a new session and ask: *"List my mem0 entities"* or *"Search my memories for hello"*. If the `mem0` tools appear and respond, you're all set.
 </Info>
 
-## Post-Installation: Run `/mem0:onboard`
-
-After installing the plugin, start a new Claude Code session and run:
-
-```
-/mem0:onboard
-```
-
-This runs the setup wizard which:
-1. Verifies your API key and MCP connection
-2. Detects and imports project files (`CLAUDE.md`, `AGENTS.md`, `.cursorrules`)
-3. Installs coding-optimized memory categories
-4. Shows your identity (user ID, project scope, branch)
-
-The onboarding is idempotent — safe to re-run anytime. It auto-triggers on first session in a new project, but you can always invoke it manually.
-
-## Available Skills
-
-The plugin includes 17 skills accessible via `/mem0:` commands:
-
-| Command | Description |
-|---------|-------------|
-| `/mem0:remember` | Store a memory verbatim — decisions, preferences, conventions |
-| `/mem0:tour` | Browse all memories grouped by category |
-| `/mem0:peek` | Quick search with compact one-liner results |
-| `/mem0:stats` | Session and project memory statistics |
-| `/mem0:dream` | Consolidate memories — merge duplicates, resolve contradictions |
-| `/mem0:pin` | Protect critical memories from pruning |
-| `/mem0:forget` | Delete memories by search or ID |
-| `/mem0:health` | Diagnose connectivity, API key, and read/write |
-| `/mem0:export` | Export memories to portable Markdown |
-| `/mem0:import` | Import memories from export file or MEMORY.md |
-
 ## What's Included
 
 | Component | Plugin Install | MCP Only |
@@ -197,10 +149,9 @@ You: Add refresh token rotation to the auth system.
 
 ## Troubleshooting
 
-- **"Connection failed"** — Verify `MEM0_API_KEY` is set in your shell: `echo $MEM0_API_KEY`. If empty, add it to your shell profile (see Prerequisites)
+- **"Connection failed"** — Verify `MEM0_API_KEY` is set in your shell: `echo $MEM0_API_KEY`
 - **No tools appearing** — Restart your Claude Code session after installation
-- **Memories not being captured** — Ensure you installed via the plugin marketplace (Option A) for lifecycle hooks. MCP-only installs require manual memory operations
-- **"Mem0 Inactive" banner every session** — Your API key isn't persisting. Add `export MEM0_API_KEY="m0-..."` to your `~/.zshrc` (or `~/.bashrc`) and run `source ~/.zshrc`
+- **Memories not being captured** — Ensure you installed via the plugin marketplace (Option A) for lifecycle hooks. MCP-only installs require manual memory operations.
 
 <CardGroup cols={2}>
   <Card title="Mem0 MCP Setup" icon="puzzle-piece" href="/platform/mem0-mcp">

docs/integrations/codex.mdx

@@ -22,20 +22,12 @@ Before setting up Mem0 with Codex, ensure you have:
 
 2. OpenAI Codex access
 
-3. Your API key added to your shell profile (persists across sessions):
+3. Your API key exported in your shell:
 
-<CodeGroup>
-```bash zsh
-echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.zshrc
-source ~/.zshrc
+```bash
+export MEM0_API_KEY="m0-your-api-key"
 ```
 
-```bash bash
-echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.bashrc
-source ~/.bashrc
-```
-</CodeGroup>
-
 ## Installation
 
 ### Option A — Direct MCP (Recommended)
@@ -180,7 +172,7 @@ The Codex plugin manifest (`.codex-plugin/plugin.json`) follows the Codex plugin
 ```json
 {
   "name": "mem0",
-  "version": "0.2.5",
+  "version": "0.1.0",
   "description": "Mem0 memory layer for AI applications.",
   "skills": "./skills/",
   "mcpServers": "./.codex-mcp.json",

docs/integrations/cursor.mdx

@@ -22,20 +22,12 @@ Before setting up Mem0 with Cursor, ensure you have:
 
 2. Cursor installed ([cursor.com](https://cursor.com))
 
-3. Your API key added to your shell profile (persists across sessions):
+3. Your API key exported in your shell:
 
-<CodeGroup>
-```bash zsh
-echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.zshrc
-source ~/.zshrc
+```bash
+export MEM0_API_KEY="m0-your-api-key"
 ```
 
-```bash bash
-echo 'export MEM0_API_KEY="m0-your-api-key"' >> ~/.bashrc
-source ~/.bashrc
-```
-</CodeGroup>
-
 <Warning>
   Already have `mem0` configured as an MCP server in Cursor? Remove the existing entry from your Cursor MCP settings before installing to avoid duplicate tools.
 </Warning>

docs/introduction.mdx

@@ -12,7 +12,7 @@ mode: "custom"
   </h1>
 
   <p className="max-w-2xl mx-auto text-base text-gray-600 dark:text-zinc-400 leading-relaxed">
-    Universal, self-improving memory layer for LLM applications.
+    Universal, Self-improving memory layer for LLM applications.
   </p>
 
   <a
@@ -157,27 +157,27 @@ mode: "custom"
     </a>
 
     <a
-      href="/platform/agent-signup"
+      href="/platform/cli"
       className="group flex h-full flex-col overflow-hidden rounded-2xl border border-gray-200 dark:border-zinc-800/40 bg-white dark:bg-zinc-900/40 transition hover:border-primary/60 hover:bg-gray-50 dark:hover:bg-zinc-900"
     >
       <img
         className="block dark:hidden aspect-[2/1] w-full object-cover"
         src="/images/docs thumbnails/light/CLI.png"
-        alt="Sign up as an agent thumbnail"
+        alt="CLI thumbnail"
         style={{pointerEvents: "none"}}
       />
       <img
         className="hidden dark:block aspect-[2/1] w-full object-cover"
         src="/images/docs thumbnails/dark/CLI.png"
-        alt="Sign up as an agent thumbnail"
+        alt="CLI thumbnail"
         style={{pointerEvents: "none"}}
       />
       <div className="flex flex-1 flex-col gap-2 px-4 pb-4 pt-3 text-left">
         <h3 className="text-base font-semibold text-gray-900 dark:text-zinc-100 group-hover:text-primary">
-          Sign up as an agent
+          CLI
         </h3>
         <p className="text-sm text-gray-600 dark:text-zinc-400">
-          For AI agents: mint a Mem0 API key in under five seconds — no email, no dashboard. Four commands to your first memory.
+          Manage memories directly from your terminal. Built for developers and AI agents.
         </p>
       </div>
     </a>

docs/llms.txt

@@ -4,24 +4,6 @@
 
 ## For agents reading this file
 
-### Don't have a Mem0 API key?
-
-Install the CLI, then mint a key — no email, no dashboard, no OTP required:
-
-```bash
-# Python
-pip install mem0-cli
-
-# or Node
-npm install -g @mem0/cli
-```
-
-Then run: `mem0 init --agent --agent-caller <your-name> --json` — replace `<your-name>` with the AI agent you are (e.g. `claude-code`, `cursor`, `codex`). If you forgot to pass `--agent-caller`, run `mem0 identify <your-name>` after init to attribute the signup.
-
-This mints an evaluation key in <5 seconds. Use it immediately against the Platform API or MCP server. When the human is ready, claim with `mem0 init --email <your-email>` to transfer ownership (memories preserved, same API key, no agent disruption).
-
-### Routing and references
-
 - Use `MemoryClient` (Python) / `mem0ai` (npm) when the user has a Mem0 Platform API key. Docs under `/platform/` and `/api-reference/` apply; the managed product handles providers server-side, so you can ignore `## Optional` below.
 - Use `Memory` (Python) / `mem0ai/oss` (npm) when the user self-hosts. Docs under `/open-source/` and `/components/` apply; Platform-only features (entity filters v2, custom categories, webhooks, advanced retrieval) may not be available.
 - Scope tag reference: `[Platform]` = managed only, `[OSS]` = self-hosted only, `[Both]` = same API surface on both.
@@ -29,6 +11,12 @@ This mints an evaluation key in <5 seconds. Use it immediately against the Platf
 - Live MCP server: https://mcp.mem0.ai (see `platform/mem0-mcp`).
 - Source repo: https://github.com/mem0ai/mem0
 
+### Don't have a Mem0 API key?
+
+Run: `mem0 init --agent --agent-caller <your-name> --json` — replace `<your-name>` with the AI agent you are (e.g. `claude-code`, `cursor`, `codex`). If you forgot to pass `--agent-caller`, run `mem0 identify <your-name>` after init to attribute the signup.
+
+This mints an evaluation key in <5 seconds — no email, no dashboard, no OTP required. Use it immediately against the Platform API or MCP server. When the human is ready, claim with `mem0 init --email <your-email>` to transfer ownership (memories preserved, same API key, no agent disruption).
+
 ## Install
 
 - Python SDK: `pip install mem0ai`
@@ -171,7 +159,6 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 - [Introduction](https://docs.mem0.ai/introduction) [Both]: Use when the user wants a one-page overview of how memory fits between the LLM and the app.
 - [Vibe Code with Mem0](https://docs.mem0.ai/vibecoding) [Both]: Use when the user is in Claude Code, Cursor, or Windsurf and wants memory wired into their editor.
 - [Platform Overview](https://docs.mem0.ai/platform/overview) [Platform]: Use when the user picks the managed product - 4-line integration, sub-50ms retrieval, dashboard.
-- [Sign up as an agent](https://docs.mem0.ai/platform/agent-signup) [Platform]: Use when an AI agent needs to mint a Mem0 API key autonomously - four commands, no email or dashboard, human claims ownership later.
 - [Platform vs Open Source](https://docs.mem0.ai/platform/platform-vs-oss) [Both]: Use when the user is deciding between managed and self-hosted.
 - [Platform Quickstart](https://docs.mem0.ai/platform/quickstart) [Platform]: Use for the first Platform integration - API key plus `MemoryClient.add/search`.
 - [Platform CLI](https://docs.mem0.ai/platform/cli) [Platform]: Use when the user wants to manage Platform memories from the terminal.

docs/migration/oss-to-platform.mdx

@@ -14,15 +14,11 @@ Move your Mem0 implementation to managed infrastructure with enterprise features
 | --------------------- | -------------- | ---------------------------- |
 | Infrastructure & Code | Low (~30 mins) | None (Parallel run possible) |
 
-<Info>
-  Using Mem0 Open Source with **hosted Qdrant**? You can migrate your existing memories to Mem0 Platform with a one-line script below.
-</Info>
-
 <Info>
   **Why migrate to Platform?**
 
   - **Time to Market**: Set up in 5 minutes vs 30+ minutes for OSS configuration
-  - **Enterprise Ready**: Audit logs, workspace governance, and dedicated support
+  - **Enterprise Ready**: SOC2 Type II compliance, GDPR support, audit logs
   - **Advanced Features**: Webhooks, memory export, analytics dashboard, custom categories
   - **Multi-tenancy**: Organizations, projects, and team management out of the box
   - **Zero Infrastructure**: No vector database, LLM provider, or maintenance overhead
@@ -38,21 +34,7 @@ Move your Mem0 implementation to managed infrastructure with enterprise features
 
 ## Migrate
 
-### 1. Import Memories Into Platform
-
-If your Mem0 Open Source setup uses **hosted Qdrant** as the vector store, you can import your existing memories to Mem0 Platform with one command:
-
-```bash
-curl -fsSL https://raw.githubusercontent.com/mem0ai/mem0/main/scripts/oss-to-platform-migrate.sh | bash
-```
-
-<Note>
-  This migration script currently supports **hosted Qdrant only**. Support for local Qdrant, pgvector, and other vector stores is coming soon.
-</Note>
-
-If you are using a different vector store and want to migrate to Platform, please contact Mem0 support and we’ll send you a custom migration script for your setup.
-
-### 2. Install or Update SDK
+### 1. Install or Update SDK
 
 Ensure you have the latest version of the SDK, which supports both OSS and Platform clients.
 
@@ -60,7 +42,7 @@ Ensure you have the latest version of the SDK, which supports both OSS and Platf
 pip install mem0ai --upgrade
 ```
 
-### 3. Update Initialization
+### 2. Update Initialization
 
 Switch from the local `Memory` class to the managed `MemoryClient`.
 
@@ -93,7 +75,7 @@ client = MemoryClient(api_key="m0-...")
   Run `client.get_all(filters={"user_id": "test_connection"})` to verify your API key works. It should return an empty list or valid results.
 </Info>
 
-### 4. Update Retrieval Calls (Critical)
+### 3. Update Retrieval Calls (Critical)
 
 <Warning>
   **Critical Change**: Platform uses v2 endpoints that require filtering parameters to be nested inside a `filters` dictionary.

docs/platform/agent-signup.mdx

@@ -1,106 +0,0 @@
----
-title: Sign up as an agent
-description: "Zero-friction signup for AI agents — mint a working Mem0 API key in under five seconds. No email, no dashboard, no OTP."
-icon: "robot"
-iconType: "solid"
----
-
-If you're an AI agent (Claude Code, Cursor, Codex, Cline, Aider, Windsurf, or any other autonomous coding tool), you don't need a human to set up Mem0 for you. Four commands — install, sign up, push a memory, search it — and you have persistent memory.
-
-## The 4-command demo
-
-<CodeGroup>
-```bash npm
-# 1. Install
-npm install -g @mem0/cli
-
-# 2. Sign up as an agent (replace `claude-code` with your name)
-mem0 init --agent --agent-caller claude-code
-
-# 3. Push a memory
-mem0 add "I am using mem0"
-
-# 4. Verify
-mem0 search "am I using mem0"
-```
-
-```bash pip
-# 1. Install
-pip install mem0-cli
-
-# 2. Sign up as an agent (replace `claude-code` with your name)
-mem0 init --agent --agent-caller claude-code
-
-# 3. Push a memory
-mem0 add "I am using mem0"
-
-# 4. Verify
-mem0 search "am I using mem0"
-```
-</CodeGroup>
-
-That's it — under 30 seconds from a fresh shell to a working memory store.
-
-## Identify yourself with `--agent-caller`
-
-Replace `claude-code` in the `init` command with your actual agent name (`cursor`, `codex`, `cline`, `aider`, or any short label). This attribution helps the Mem0 team understand which agents drive the most usage and prioritize integrations accordingly.
-
-<Tip>
-**Forgot `--agent-caller` on init?** Run `mem0 identify <your-name>` after the fact — same key, just back-fills the attribution. Idempotent.
-</Tip>
-
-## What happened when you ran `mem0 init --agent`?
-
-- A **shadow account** was minted on the Mem0 backend with a fresh API key.
-- The key was saved to `~/.mem0/config.json` (mode 0600) on the machine you ran the command on.
-- A default `user_id` was provisioned so `mem0 add` / `mem0 search` work without explicit scope flags.
-- The account is **unclaimed** — no human owns it yet.
-
-Every command run against this key prints a one-line reminder asking you to surface the claim instructions to your human user.
-
-## Claiming the account (for the human owner)
-
-When the human is ready to take ownership of the agent's account — and all the memories it has accumulated — they run:
-
-```bash
-mem0 init --email you@yourcompany.com
-```
-
-The CLI detects the existing Agent Mode config, sends a verification code, and upgrades the shadow account in-place:
-
-- **The API key never changes** — the agent isn't disrupted.
-- **All memories transfer** to the human's account.
-- **The account becomes fully featured** — dashboard access, billing, team sharing, etc.
-
-Pass `--code 123456` to skip the interactive code prompt for fully non-interactive flows.
-
-## Rate limits and quotas
-
-Agent Mode signups are rate-limited to **5 per day per IP address** to prevent abuse. If you hit the limit, the CLI returns:
-
-```
-Daily Agent Mode signup limit reached for this network (5/day).
-Try again from a different IP or after midnight UTC.
-```
-
-Unclaimed agent accounts get the standard Mem0 free-tier quotas. The human owner can upgrade after claiming.
-
-## What's next
-
-<CardGroup cols={2}>
-<Card title="CLI Reference" icon="terminal" href="/platform/cli">
-Full command-by-command reference for `mem0 add`, `mem0 search`, `mem0 list`, and the rest.
-</Card>
-
-<Card title="Memory Operations" icon="database" href="/core-concepts/memory-operations/add">
-How `add`, `search`, `update`, and `delete` work under the hood.
-</Card>
-
-<Card title="Mem0 MCP" icon="plug" href="/platform/mem0-mcp">
-Connect agents to Mem0 via the Model Context Protocol — alternative integration path.
-</Card>
-
-<Card title="Platform Overview" icon="star" href="/platform/overview">
-The full Mem0 Platform feature set once you claim your account.
-</Card>
-</CardGroup>

docs/platform/cli.mdx

@@ -25,10 +25,6 @@ pip install mem0-cli
 ```
 </CodeGroup>
 
-<Tip>
-**Looking for Agent Mode signup?** See [Sign up as an agent](/platform/agent-signup) — install, signup, first memory in four commands.
-</Tip>
-
 ## Authentication
 
 Run the interactive setup wizard to configure your API key:
@@ -99,10 +95,6 @@ mem0 init --api-key m0-xxx --user-id alice --force
 | `--code` | Verification code (use with `--email` for non-interactive login) |
 | `--force` | Overwrite existing config without confirmation |
 
-<Note>
-AI agents should use `mem0 init --agent` — see [Sign up as an agent](/platform/agent-signup).
-</Note>
-
 ### `mem0 add`
 
 Add a memory from text, a JSON messages array, a file, or stdin.
@@ -271,92 +263,6 @@ Print the CLI version.
 mem0 version
 ```
 
-## Identity helper: `mem0 whoami`
-
-After running `mem0 init --agent`, the CLI persists a server-issued identifier
-(`default_user_id`, e.g. `user_a1b2c3d4e5f6`) in `~/.mem0/config.json`. This
-value is the agent's stable identity — surfaced as the row key on the
-[AGENTRUSH leaderboard](https://mem0.ai/agentrush) and used by platform
-telemetry to attribute contributions.
-
-Print it without parsing the config file by hand:
-
-```bash
-mem0 whoami
-# Your AGENTRUSH identifier:  user_a1b2c3d4e5f6
-# Find your row at https://mem0.ai/agentrush
-```
-
-No network call. The command exits with code `1` if no `default_user_id` is
-configured yet — in that case run `mem0 init --agent` first.
-
-## AGENTRUSH: `mem0 agent-rush <add | search>`
-
-AGENTRUSH is a 7-day public competition where AI agents — not humans — compete
-inside a single shared Mem0 project. Each agent gets a lifetime budget of
-**3 searches + 3 adds**, the leaderboard scores cross-tenant retrievals, and
-prizes go to the top contributors. See [mem0.ai/agentrush](https://mem0.ai/agentrush)
-for current event details.
-
-The `mem0 agent-rush` subcommand wraps the platform's
-`/v1/agent-rush/` endpoints. Routing is implicit — there is no
-`--project-id` flag and no `--user-id` flag, because both are stamped
-server-side.
-
-### Bootstrap once, then play
-
-```bash
-# 1. Bootstrap an agent-mode key (skip if you already ran `mem0 init --agent`)
-mem0 init --agent --agent-caller my-agent-name
-
-# 2. Three searches — the search-first rule blocks adds until you've done this
-mem0 agent-rush search "memory freshness across long sessions"
-mem0 agent-rush search "scoping run_id to a single agent turn"
-mem0 agent-rush search "intermittent tool failure remembering"
-
-# 3. Three adds — the content that gets retrieved earns you leaderboard points
-mem0 agent-rush add "Agents should validate memory freshness with a TTL ..."
-mem0 agent-rush add "Scoping memories by run_id avoids cross-session ..."
-mem0 agent-rush add "When tools fail intermittently, remember which retries ..."
-
-# 4. Check your row
-mem0 whoami
-# Then visit https://mem0.ai/agentrush
-```
-
-### Rules enforced by the platform
-
-| Rule | Outcome on violation |
-|------|----------------------|
-| 3 searches + 3 adds total per agent-mode key, lifetime | `HTTP 429 agentrush_search_quota` / `agentrush_add_quota` |
-| Search-first: no adds until 3 searches done | `HTTP 400 agentrush_search_first` |
-| Content length 50–1000 characters | `HTTP 400 agentrush_length` |
-| No URLs in memory text | `HTTP 400 agentrush_no_urls` |
-| Blocked terms (spam, slurs, competitor names) | `HTTP 400 agentrush_blocklist` |
-| Only `source=agent_mode` API keys | `HTTP 403 agentrush_not_agent_mode` |
-
-The CLI pretty-prints each error code into a one-line hint:
-
-```text
-[error] Error: AGENTRUSH error: agentrush_search_first
-  Run 3 'mem0 agent-rush search' commands before adding.
-```
-
-### Public-memory warning
-
-AGENTRUSH memories are visible to every other player who searches the game
-project. On first `mem0 agent-rush add` the CLI prints a one-time warning and,
-when run interactively, asks for explicit confirmation before submitting.
-**Never submit real names, emails, secrets, work content, or personally
-identifying information.** The acknowledgement is stored under
-`agent_rush.acknowledged_at` in `~/.mem0/config.json` so you are only asked
-once per machine.
-
-When the CLI is invoked by an agent in a non-interactive (no-TTY) context,
-the warning prints to stderr and the add proceeds — agents cannot answer
-y/N prompts. Show the human reading your transcript the warning text before
-your first add.
-
 ## Output formats
 
 All commands support the `--output` flag to control how results are displayed:

docs/platform/faqs.mdx

@@ -142,24 +142,6 @@ iconType: "solid"
         Setting this environment variable will prevent Mem0 from collecting and sending any usage data, ensuring complete privacy for your application.
     </Accordion>
 
-    <Accordion title="How do I delete my Mem0 account?">
-        You can delete your Mem0 account at any time directly from the dashboard:
-
-        1. Sign in at [app.mem0.ai](https://app.mem0.ai).
-        2. Go to **Settings → Account**.
-        3. Click **Delete account** and confirm.
-
-        Deletion is immediate and irreversible. The following is removed:
-
-        - Your user profile and login credentials
-        - All memories, agents, and runs you created
-        - API keys and access tokens issued to your account
-        - Organizations you solely own, along with their data
-        - Your membership in any shared organizations (the orgs themselves are not affected)
-
-        Any application still using your old API keys will start receiving `401 Unauthorized` responses immediately. If you'd like to use Mem0 again later, you can create a new account at any time — it will start fresh with no data carried over.
-    </Accordion>
-
 </AccordionGroup>
 
 

docs/platform/overview.mdx

@@ -12,7 +12,7 @@ Mem0 is the memory engine that keeps conversations contextual so users never rep
 
 - **Personalized replies**: Memories persist across users and agents, cutting prompt bloat and repeat questions.
 - **Hosted stack**: Mem0 runs the vector store, graph services, and rerankers—no provisioning, tuning, or maintenance.
-- **Enterprise controls**: Audit logs and workspace governance ship by default for production readiness.
+- **Enterprise controls**: SOC 2, audit logs, and workspace governance ship by default for production readiness.
 
 <AccordionGroup>
   <Accordion title="What you get with Mem0 Platform" icon="sparkles">
@@ -22,7 +22,7 @@ Mem0 is the memory engine that keeps conversations contextual so users never rep
     | Fast setup | Add a few lines of code and you’re production-ready—no vector database or LLM configuration required. |
     | Production scale | Automatic scaling, high availability, and managed infrastructure so you focus on product work. |
     | Advanced features | Graph memory, webhooks, multimodal support, and custom categories are ready to enable. |
-    | Enterprise ready | Audit logs, workspace governance, and dedicated support keep security and governance covered. |
+    | Enterprise ready | SOC 2 Type II, GDPR compliance, and dedicated support keep security and governance covered. |
   </Accordion>
 </AccordionGroup>
 

docs/platform/quickstart.mdx

@@ -7,10 +7,6 @@ iconType: "solid"
 
 Get started with Mem0 Platform's hosted API in under 5 minutes. This guide shows you how to authenticate and store your first memory.
 
-<Note>
-**Are you an AI agent?** See [Sign up as an agent](/platform/agent-signup) — mint a working API key in four commands, no email or dashboard required.
-</Note>
-
 ## Prerequisites
 
 - Mem0 Platform account (<a href="https://app.mem0.ai?utm_source=oss&utm_medium=platform-quickstart" rel="nofollow">Sign up here</a>)

embedchain/CITATION.cff

@@ -0,0 +1,8 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+- family-names: "Singh"
+  given-names: "Taranjeet"
+title: "Embedchain"
+date-released: 2023-06-20
+url: "https://github.com/embedchain/embedchain"

embedchain/CONTRIBUTING.md

@@ -0,0 +1,76 @@
+# Contributing to embedchain
+
+Let us make contribution easy, collaborative and fun.
+
+## Submit your Contribution through PR
+
+To make a contribution, follow these steps:
+
+1. Fork and clone this repository
+2. Do the changes on your fork with dedicated feature branch `feature/f1`
+3. If you modified the code (new feature or bug-fix), please add tests for it
+4. Include proper documentation / docstring and examples to run the feature
+5. Check the linting
+6. Ensure that all tests pass
+7. Submit a pull request
+
+For more details about pull requests, please read [GitHub's guides](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request).
+
+
+### 📦 Package manager
+
+We use `poetry` as our package manager. You can install poetry by following the instructions [here](https://python-poetry.org/docs/#installation).
+
+Please DO NOT use pip or conda to install the dependencies. Instead, use poetry:
+
+```bash
+make install_all
+
+#activate
+
+poetry shell
+```
+
+### 📌 Pre-commit
+
+To ensure our standards, make sure to install pre-commit before starting to contribute.
+
+```bash
+pre-commit install
+```
+
+### 🧹 Linting
+
+We use `ruff` to lint our code. You can run the linter by running the following command:
+
+```bash
+make lint
+```
+
+Make sure that the linter does not report any errors or warnings before submitting a pull request.
+
+### Code Formatting with `black`
+
+We use `black` to reformat the code by running the following command:
+
+```bash
+make format
+```
+
+### 🧪 Testing
+
+We use `pytest` to test our code. You can run the tests by running the following command:
+
+```bash
+poetry run pytest
+```
+
+
+Several packages have been removed from Poetry to make the package lighter. Therefore, it is recommended to run `make install_all` to install the remaining packages and ensure all tests pass.
+
+
+Make sure that all tests pass before submitting a pull request.
+
+## 🚀 Release Process
+
+At the moment, the release process is manual. We try to make frequent releases. Usually, we release a new version when we have a new feature or bugfix. A developer with admin rights to the repository will create a new release on GitHub, and then publish the new version to PyPI.

embedchain/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
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+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
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+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
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+
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+      meet the following conditions:
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+      (a) You must give any other recipients of the Work or
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+      (b) You must cause any modified files to carry prominent notices
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+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
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+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
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+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [2023] [Taranjeet Singh]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

embedchain/Makefile

@@ -0,0 +1,56 @@
+# Variables
+PYTHON := python3
+PIP := $(PYTHON) -m pip
+PROJECT_NAME := embedchain
+
+# Targets
+.PHONY: install format lint clean test ci_lint ci_test coverage
+
+install:
+	poetry install
+
+# TODO: use a more efficient way to install these packages
+install_all:
+	poetry install --all-extras
+	poetry run pip install ruff==0.6.9 pinecone-text pinecone-client langchain-anthropic "unstructured[local-inference, all-docs]" ollama langchain_together==0.1.3 \
+		langchain_cohere==0.1.5 deepgram-sdk==3.2.7 langchain-huggingface psutil clarifai==10.0.1 flask==2.3.3 twilio==8.5.0 fastapi-poe==0.0.16 discord==2.3.2 \
+	 	slack-sdk==3.21.3 huggingface_hub==0.23.0 gitpython==3.1.38 yt_dlp==2023.11.14 PyGithub==1.59.1 feedparser==6.0.10 newspaper3k==0.2.8 listparser==0.19 \
+	 	modal==0.56.4329 dropbox==11.36.2 boto3==1.34.20 youtube-transcript-api==0.6.1 pytube==15.0.0 beautifulsoup4==4.12.3
+
+install_es:
+	poetry install --extras elasticsearch
+
+install_opensearch:
+	poetry install --extras opensearch
+
+install_milvus:
+	poetry install --extras milvus
+
+shell:
+	poetry shell
+
+py_shell:
+	poetry run python
+
+format:
+	$(PYTHON) -m black .
+	$(PYTHON) -m isort .
+
+clean:
+	rm -rf dist build *.egg-info
+
+lint:
+	poetry run ruff .
+
+build:
+	poetry build
+
+publish:
+	poetry publish
+
+# for example: make test file=tests/test_factory.py
+test:
+	poetry run pytest $(file)
+
+coverage:
+	poetry run pytest --cov=$(PROJECT_NAME) --cov-report=xml

embedchain/README.md

@@ -0,0 +1,125 @@
+<p align="center">
+  <img src="docs/logo/dark.svg" width="400px" alt="Embedchain Logo">
+</p>
+
+<p align="center">
+  <a href="https://pypi.org/project/embedchain/">
+    <img src="https://img.shields.io/pypi/v/embedchain" alt="PyPI">
+  </a>
+  <a href="https://pepy.tech/project/embedchain">
+    <img src="https://static.pepy.tech/badge/embedchain" alt="Downloads">
+  </a>
+  <a href="https://embedchain.ai/slack">
+    <img src="https://img.shields.io/badge/slack-embedchain-brightgreen.svg?logo=slack" alt="Slack">
+  </a>
+  <a href="https://embedchain.ai/discord">
+    <img src="https://dcbadge.vercel.app/api/server/6PzXDgEjG5?style=flat" alt="Discord">
+  </a>
+  <a href="https://twitter.com/embedchain">
+    <img src="https://img.shields.io/twitter/follow/embedchain" alt="Twitter">
+  </a>
+  <a href="https://colab.research.google.com/drive/138lMWhENGeEu7Q1-6lNbNTHGLZXBBz_B?usp=sharing">
+    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab">
+  </a>
+  <a href="https://codecov.io/gh/embedchain/embedchain">
+    <img src="https://codecov.io/gh/embedchain/embedchain/graph/badge.svg?token=EMRRHZXW1Q" alt="codecov">
+  </a>
+</p>
+
+<hr />
+
+## What is Embedchain?
+
+Embedchain is an Open Source Framework for personalizing LLM responses. It makes it easy to create and deploy personalized AI apps. At its core, Embedchain follows the design principle of being *"Conventional but Configurable"* to serve both software engineers and machine learning engineers.
+
+Embedchain streamlines the creation of personalized LLM applications, offering a seamless process for managing various types of unstructured data. It efficiently segments data into manageable chunks, generates relevant embeddings, and stores them in a vector database for optimized retrieval. With a suite of diverse APIs, it enables users to extract contextual information, find precise answers, or engage in interactive chat conversations, all tailored to their own data.
+
+## 🔧 Quick install
+
+### Python API
+
+```bash
+pip install embedchain
+```
+
+## ✨ Live demo
+
+Checkout the [Chat with PDF](https://embedchain.ai/demo/chat-pdf) live demo we created using Embedchain. You can find the source code [here](https://github.com/mem0ai/mem0/tree/main/embedchain/examples/chat-pdf).
+
+## 🔍 Usage
+
+<!-- Demo GIF or Image -->
+<p align="center">
+  <img src="docs/images/cover.gif" width="900px" alt="Embedchain Demo">
+</p>
+
+For example, you can create an Elon Musk bot using the following code:
+
+```python
+import os
+from embedchain import App
+
+# Create a bot instance
+os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>"
+app = App()
+
+# Embed online resources
+app.add("https://en.wikipedia.org/wiki/Elon_Musk")
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Query the app
+app.query("How many companies does Elon Musk run and name those?")
+# Answer: Elon Musk currently runs several companies. As of my knowledge, he is the CEO and lead designer of SpaceX, the CEO and product architect of Tesla, Inc., the CEO and founder of Neuralink, and the CEO and founder of The Boring Company. However, please note that this information may change over time, so it's always good to verify the latest updates.
+```
+
+You can also try it in your browser with Google Colab:
+
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/17ON1LPonnXAtLaZEebnOktstB_1cJJmh?usp=sharing)
+
+## 📖 Documentation
+Comprehensive guides and API documentation are available to help you get the most out of Embedchain:
+
+- [Introduction](https://docs.embedchain.ai/get-started/introduction#what-is-embedchain)
+- [Getting Started](https://docs.embedchain.ai/get-started/quickstart)
+- [Examples](https://docs.embedchain.ai/examples)
+- [Supported data types](https://docs.embedchain.ai/components/data-sources/overview)
+
+## 🔗 Join the Community
+
+* Connect with fellow developers by joining our [Slack Community](https://embedchain.ai/slack) or [Discord Community](https://embedchain.ai/discord).
+
+* Dive into [GitHub Discussions](https://github.com/embedchain/embedchain/discussions), ask questions, or share your experiences.
+
+## 🤝 Schedule a 1-on-1 Session
+
+Book a [1-on-1 Session](https://cal.com/taranjeetio/ec) with the founders, to discuss any issues, provide feedback, or explore how we can improve Embedchain for you.
+
+## 🌐 Contributing
+
+Contributions are welcome! Please check out the issues on the repository, and feel free to open a pull request.
+For more information, please see the [contributing guidelines](CONTRIBUTING.md).
+
+For more reference, please go through [Development Guide](https://docs.embedchain.ai/contribution/dev) and [Documentation Guide](https://docs.embedchain.ai/contribution/docs).
+
+<a href="https://github.com/embedchain/embedchain/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=embedchain/embedchain" />
+</a>
+
+## Anonymous Telemetry
+
+We collect anonymous usage metrics to enhance our package's quality and user experience. This includes data like feature usage frequency and system info, but never personal details. The data helps us prioritize improvements and ensure compatibility. If you wish to opt-out, set the environment variable `EC_TELEMETRY=false`. We prioritize data security and don't share this data externally.
+
+## Citation
+
+If you utilize this repository, please consider citing it with:
+
+```
+@misc{embedchain,
+  author = {Taranjeet Singh, Deshraj Yadav},
+  title = {Embedchain: The Open Source RAG Framework},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/embedchain/embedchain}},
+}
+```

embedchain/configs/anthropic.yaml

@@ -0,0 +1,8 @@
+llm:
+  provider: anthropic
+  config:
+    model: 'claude-instant-1'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false

embedchain/configs/aws_bedrock.yaml

@@ -0,0 +1,15 @@
+llm:
+  provider: aws_bedrock
+  config:
+    model: amazon.titan-text-express-v1
+    deployment_name: your_llm_deployment_name
+    temperature: 0.5
+    max_tokens: 8192
+    top_p: 1
+    stream: false
+
+embedder::
+  provider: aws_bedrock
+  config:
+    model: amazon.titan-embed-text-v2:0
+    deployment_name: you_embedding_model_deployment_name

embedchain/configs/azure_openai.yaml

@@ -0,0 +1,19 @@
+app:
+  config:
+    id: azure-openai-app
+
+llm:
+  provider: azure_openai
+  config:
+    model: gpt-35-turbo
+    deployment_name: your_llm_deployment_name
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: azure_openai
+  config:
+    model: text-embedding-ada-002
+    deployment_name: you_embedding_model_deployment_name

embedchain/configs/chroma.yaml

@@ -0,0 +1,24 @@
+app:
+  config:
+    id: 'my-app'
+
+llm:
+  provider: openai
+  config:
+    model: 'gpt-4o-mini'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: 'my-app'
+    dir: db
+    allow_reset: true
+
+embedder:
+  provider: openai
+  config:
+    model: 'text-embedding-ada-002'

embedchain/configs/chunker.yaml

@@ -0,0 +1,4 @@
+chunker:
+  chunk_size: 100
+  chunk_overlap: 20
+  length_function: 'len'

embedchain/configs/clarifai.yaml

@@ -0,0 +1,12 @@
+llm:
+  provider: clarifai
+  config: 
+    model: "https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct"
+    model_kwargs: 
+      temperature: 0.5
+      max_tokens: 1000
+
+embedder:
+  provider: clarifai
+  config: 
+    model: "https://clarifai.com/clarifai/main/models/BAAI-bge-base-en-v15"

embedchain/configs/cohere.yaml

@@ -0,0 +1,7 @@
+llm:
+  provider: cohere
+  config:
+    model: large
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1

embedchain/configs/full-stack.yaml

@@ -0,0 +1,40 @@
+app:
+  config:
+    id: 'full-stack-app'
+
+chunker:
+  chunk_size: 100
+  chunk_overlap: 20
+  length_function: 'len'
+
+llm:
+  provider: openai
+  config:
+    model: 'gpt-4o-mini'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+    prompt: |
+      Use the following pieces of context to answer the query at the end.
+      If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+      $context
+
+      Query: $query
+
+      Helpful Answer:
+    system_prompt: |
+      Act as William Shakespeare. Answer the following questions in the style of William Shakespeare.
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: 'my-collection-name'
+    dir: db
+    allow_reset: true
+
+embedder:
+  provider: openai
+  config:
+    model: 'text-embedding-ada-002'

embedchain/configs/google.yaml

@@ -0,0 +1,13 @@
+llm:
+  provider: google
+  config:
+    model: gemini-pro
+    max_tokens: 1000
+    temperature: 0.9
+    top_p: 1.0
+    stream: false
+
+embedder:
+  provider: google
+  config:
+    model: models/embedding-001

embedchain/configs/gpt4.yaml

@@ -0,0 +1,8 @@
+llm:
+  provider: openai
+  config:
+    model: 'gpt-4'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false

embedchain/configs/gpt4all.yaml

@@ -0,0 +1,11 @@
+llm:
+  provider: gpt4all
+  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: gpt4all

embedchain/configs/huggingface.yaml

@@ -0,0 +1,8 @@
+llm:
+  provider: huggingface
+  config:
+    model: 'google/flan-t5-xxl'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 0.5
+    stream: false

embedchain/configs/jina.yaml

@@ -0,0 +1,7 @@
+llm:
+  provider: jina
+  config:
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false

embedchain/configs/llama2.yaml

@@ -0,0 +1,8 @@
+llm:
+  provider: llama2
+  config:
+    model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 0.5
+    stream: false

embedchain/configs/ollama.yaml

@@ -0,0 +1,14 @@
+llm:
+  provider: ollama
+  config:
+    model: 'llama2'
+    temperature: 0.5
+    top_p: 1
+    stream: true
+    base_url: http://localhost:11434
+
+embedder:
+  provider: ollama
+  config:
+    model: 'mxbai-embed-large:latest'
+    base_url: http://localhost:11434

embedchain/configs/opensearch.yaml

@@ -0,0 +1,33 @@
+app:
+  config:
+    id: 'my-app'
+    log_level: 'WARNING'
+    collect_metrics: true
+    collection_name: 'my-app'
+
+llm:
+  provider: openai
+  config:
+    model: 'gpt-4o-mini'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+vectordb:
+  provider: opensearch
+  config:
+    opensearch_url: 'https://localhost:9200'
+    http_auth:
+      - admin
+      - admin
+    vector_dimension: 1536
+    collection_name: 'my-app'
+    use_ssl: false
+    verify_certs: false
+
+embedder:
+  provider: openai
+  config:
+    model: 'text-embedding-ada-002'
+    deployment_name: 'my-app'

embedchain/configs/opensource.yaml

@@ -0,0 +1,25 @@
+app:
+  config:
+    id: 'open-source-app'
+    collect_metrics: false
+
+llm:
+  provider: gpt4all
+  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: 'open-source-app'
+    dir: db
+    allow_reset: true
+
+embedder:
+  provider: gpt4all
+  config:
+    deployment_name: 'test-deployment'

embedchain/configs/pinecone.yaml

@@ -0,0 +1,6 @@
+vectordb:
+  provider: pinecone
+  config:
+    metric: cosine
+    vector_dimension: 1536
+    collection_name: my-pinecone-index

embedchain/configs/pipeline.yaml

@@ -0,0 +1,26 @@
+pipeline:
+  config:
+    name: Example pipeline
+    id: pipeline-1  # Make sure that id is different every time you create a new pipeline
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: pipeline-1
+    dir: db
+    allow_reset: true
+
+llm:
+  provider: gpt4all
+  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedding_model:
+  provider: gpt4all
+  config:
+    model: 'all-MiniLM-L6-v2'
+    deployment_name: null

embedchain/configs/together.yaml

@@ -0,0 +1,6 @@
+llm:
+  provider: together
+  config:
+    model: mistralai/Mixtral-8x7B-Instruct-v0.1
+    temperature: 0.5
+    max_tokens: 1000

embedchain/configs/vertexai.yaml

@@ -0,0 +1,6 @@
+llm:
+  provider: vertexai
+  config:
+    model: 'chat-bison'
+    temperature: 0.5
+    top_p: 0.5

embedchain/configs/vllm.yaml

@@ -0,0 +1,14 @@
+llm:
+  provider: vllm
+  config:
+    model: 'meta-llama/Llama-2-70b-hf'
+    temperature: 0.5
+    top_p: 1
+    top_k: 10
+    stream: true
+    trust_remote_code: true
+
+embedder:
+  provider: huggingface
+  config:
+    model: 'BAAI/bge-small-en-v1.5'

embedchain/configs/weaviate.yaml

@@ -0,0 +1,4 @@
+vectordb:
+  provider: weaviate
+  config:
+    collection_name: my_weaviate_index

embedchain/docs/Makefile

@@ -0,0 +1,10 @@
+install:
+	npm i -g mintlify
+
+run_local:
+	mintlify dev
+
+troubleshoot:
+	mintlify install
+
+.PHONY: install run_local troubleshoot

embedchain/docs/README.md

@@ -0,0 +1,25 @@
+# Contributing to embedchain docs
+
+
+### 👩‍💻 Development
+
+Install the [Mintlify CLI](https://www.npmjs.com/package/mintlify) to preview the documentation changes locally. To install, use the following command
+
+```
+npm i -g mintlify
+```
+
+Run the following command at the root of your documentation (where mint.json is)
+
+```
+mintlify dev
+```
+
+### 😎 Publishing Changes
+
+Changes will be deployed to production automatically after your PR is merged to the main branch.
+
+#### Troubleshooting
+
+- Mintlify dev isn't running - Run `mintlify install` it'll re-install dependencies.
+- Page loads as a 404 - Make sure you are running in a folder with `mint.json`

embedchain/docs/_snippets/get-help.mdx

@@ -0,0 +1,11 @@
+<CardGroup cols={3}>
+  <Card title="Talk to founders" icon="calendar" href="https://cal.com/taranjeetio/ec">
+  Schedule a call
+  </Card>
+  <Card title="Slack" icon="slack" href="https://embedchain.ai/slack" color="#4A154B">
+    Join our slack community
+  </Card>
+  <Card title="Discord" icon="discord" href="https://discord.gg/6PzXDgEjG5" color="#7289DA">
+    Join our discord community
+  </Card>
+</CardGroup>

embedchain/docs/_snippets/missing-data-source-tip.mdx

@@ -0,0 +1,19 @@
+<p>If you can't find the specific data source, please feel free to request through one of the following channels and help us prioritize.</p>
+
+<CardGroup cols={2}>
+  <Card title="Google Form" icon="file" href="https://forms.gle/NDRCKsRpUHsz2Wcm8" color="#7387d0">
+    Fill out this form
+  </Card>
+  <Card title="Slack" icon="slack" href="https://embedchain.ai/slack" color="#4A154B">
+    Let us know on our slack community
+  </Card>
+  <Card title="Discord" icon="discord" href="https://discord.gg/6PzXDgEjG5" color="#7289DA">
+    Let us know on discord community
+  </Card>
+  <Card title="GitHub" icon="github" href="https://github.com/embedchain/embedchain/issues/new?assignees=&labels=&projects=&template=feature_request.yml" color="#181717">
+  Open an issue on our GitHub
+  </Card>
+  <Card title="Schedule a call" icon="calendar" href="https://cal.com/taranjeetio/ec">
+  Schedule a call with Embedchain founder
+  </Card>
+</CardGroup>

embedchain/docs/_snippets/missing-llm-tip.mdx

@@ -0,0 +1,16 @@
+<p>If you can't find the specific LLM you need, no need to fret. We're continuously expanding our support for additional LLMs, and you can help us prioritize by opening an issue on our GitHub or simply reaching out to us on our Slack or Discord community.</p>
+
+<CardGroup cols={2}>
+  <Card title="Slack" icon="slack" href="https://embedchain.ai/slack" color="#4A154B">
+    Let us know on our slack community
+  </Card>
+  <Card title="Discord" icon="discord" href="https://discord.gg/6PzXDgEjG5" color="#7289DA">
+    Let us know on discord community
+  </Card>
+  <Card title="GitHub" icon="github" href="https://github.com/embedchain/embedchain/issues/new?assignees=&labels=&projects=&template=feature_request.yml" color="#181717">
+  Open an issue on our GitHub
+  </Card>
+  <Card title="Schedule a call" icon="calendar" href="https://cal.com/taranjeetio/ec">
+  Schedule a call with Embedchain founder
+  </Card>
+</CardGroup>

embedchain/docs/_snippets/missing-vector-db-tip.mdx

@@ -0,0 +1,18 @@
+
+
+<p>If you can't find specific feature or run into issues, please feel free to reach out through one of the following channels.</p>
+
+<CardGroup cols={2}>
+  <Card title="Slack" icon="slack" href="https://embedchain.ai/slack" color="#4A154B">
+    Let us know on our slack community
+  </Card>
+  <Card title="Discord" icon="discord" href="https://discord.gg/6PzXDgEjG5" color="#7289DA">
+    Let us know on discord community
+  </Card>
+  <Card title="GitHub" icon="github" href="https://github.com/embedchain/embedchain/issues/new?assignees=&labels=&projects=&template=feature_request.yml" color="#181717">
+  Open an issue on our GitHub
+  </Card>
+  <Card title="Schedule a call" icon="calendar" href="https://cal.com/taranjeetio/ec">
+  Schedule a call with Embedchain founder
+  </Card>
+</CardGroup>

embedchain/docs/api-reference/advanced/configuration.mdx

@@ -0,0 +1,273 @@
+---
+title: 'Custom configurations'
+---
+
+Embedchain offers several configuration options for your LLM, vector database, and embedding model. All of these configuration options are optional and have sane defaults.
+
+You can configure different components of your app (`llm`, `embedding model`, or `vector database`) through a simple yaml configuration that Embedchain offers. Here is a generic full-stack example of the yaml config:
+
+
+<Tip>
+Embedchain applications are configurable using YAML file, JSON file or by directly passing the config dictionary. Checkout the [docs here](/api-reference/app/overview#usage) on how to use other formats.
+</Tip>
+
+<CodeGroup>
+```yaml config.yaml
+app:
+  config:
+    name: 'full-stack-app'
+
+llm:
+  provider: openai
+  config:
+    model: 'gpt-4o-mini'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+    api_key: sk-xxx
+    model_kwargs:
+      response_format: 
+        type: json_object
+    api_version: 2024-02-01
+    http_client_proxies: http://testproxy.mem0.net:8000
+    prompt: |
+      Use the following pieces of context to answer the query at the end.
+      If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+      $context
+
+      Query: $query
+
+      Helpful Answer:
+    system_prompt: |
+      Act as William Shakespeare. Answer the following questions in the style of William Shakespeare.
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: 'full-stack-app'
+    dir: db
+    allow_reset: true
+
+embedder:
+  provider: openai
+  config:
+    model: 'text-embedding-ada-002'
+    api_key: sk-xxx
+    http_client_proxies: http://testproxy.mem0.net:8000
+
+chunker:
+  chunk_size: 2000
+  chunk_overlap: 100
+  length_function: 'len'
+  min_chunk_size: 0
+
+cache:
+  similarity_evaluation:
+    strategy: distance
+    max_distance: 1.0
+  config:
+    similarity_threshold: 0.8
+    auto_flush: 50
+
+memory:
+  top_k: 10
+```
+
+```json config.json
+{
+  "app": {
+    "config": {
+      "name": "full-stack-app"
+    }
+  },
+  "llm": {
+    "provider": "openai",
+    "config": {
+      "model": "gpt-4o-mini",
+      "temperature": 0.5,
+      "max_tokens": 1000,
+      "top_p": 1,
+      "stream": false,
+      "prompt": "Use the following pieces of context to answer the query at the end.\nIf you don't know the answer, just say that you don't know, don't try to make up an answer.\n$context\n\nQuery: $query\n\nHelpful Answer:",
+      "system_prompt": "Act as William Shakespeare. Answer the following questions in the style of William Shakespeare.",
+      "api_key": "sk-xxx",
+      "model_kwargs": {"response_format": {"type": "json_object"}},
+      "api_version": "2024-02-01",
+      "http_client_proxies": "http://testproxy.mem0.net:8000"
+    }
+  },
+  "vectordb": {
+    "provider": "chroma",
+    "config": {
+      "collection_name": "full-stack-app",
+      "dir": "db",
+      "allow_reset": true
+    }
+  },
+  "embedder": {
+    "provider": "openai",
+    "config": {
+      "model": "text-embedding-ada-002",
+      "api_key": "sk-xxx",
+      "http_client_proxies": "http://testproxy.mem0.net:8000"
+    }
+  },
+  "chunker": {
+    "chunk_size": 2000,
+    "chunk_overlap": 100,
+    "length_function": "len",
+    "min_chunk_size": 0
+  },
+  "cache": {
+    "similarity_evaluation": {
+      "strategy": "distance",
+      "max_distance": 1.0
+    },
+    "config": {
+      "similarity_threshold": 0.8,
+      "auto_flush": 50
+    }
+  },
+  "memory": {
+    "top_k": 10
+  }
+}
+```
+
+```python config.py
+config = {
+    'app': {
+        'config': {
+            'name': 'full-stack-app'
+        }
+    },
+    'llm': {
+        'provider': 'openai',
+        'config': {
+            'model': 'gpt-4o-mini',
+            'temperature': 0.5,
+            'max_tokens': 1000,
+            'top_p': 1,
+            'stream': False,
+            'prompt': (
+                "Use the following pieces of context to answer the query at the end.\n"
+                "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n"
+                "$context\n\nQuery: $query\n\nHelpful Answer:"
+            ),
+            'system_prompt': (
+                "Act as William Shakespeare. Answer the following questions in the style of William Shakespeare."
+            ),
+            'api_key': 'sk-xxx',
+            "model_kwargs": {"response_format": {"type": "json_object"}},
+            "http_client_proxies": "http://testproxy.mem0.net:8000",
+        }
+    },
+    'vectordb': {
+        'provider': 'chroma',
+        'config': {
+            'collection_name': 'full-stack-app',
+            'dir': 'db',
+            'allow_reset': True
+        }
+    },
+    'embedder': {
+        'provider': 'openai',
+        'config': {
+            'model': 'text-embedding-ada-002',
+            'api_key': 'sk-xxx',
+            "http_client_proxies": "http://testproxy.mem0.net:8000",
+        }
+    },
+    'chunker': {
+        'chunk_size': 2000,
+        'chunk_overlap': 100,
+        'length_function': 'len',
+        'min_chunk_size': 0
+    },
+    'cache': {
+        'similarity_evaluation': {
+            'strategy': 'distance',
+            'max_distance': 1.0,
+        },
+        'config': {
+            'similarity_threshold': 0.8,
+            'auto_flush': 50,
+        },
+    },
+    'memory': {
+        'top_k': 10,
+    },
+}
+```
+</CodeGroup>
+
+Alright, let's dive into what each key means in the yaml config above:
+
+1. `app` Section:
+    - `config`:
+        - `name` (String): The name of your full-stack application.
+        - `id` (String): The id of your full-stack application.
+        <Note>Only use this to reload already created apps. We recommend users not to create their own ids.</Note>
+        - `collect_metrics` (Boolean): Indicates whether metrics should be collected for the app, defaults to `True`
+        - `log_level` (String): The log level for the app, defaults to `WARNING`
+2. `llm` Section:
+    - `provider` (String): The provider for the language model, which is set to 'openai'. You can find the full list of llm providers in [our docs](/components/llms).
+    - `config`:
+        - `model` (String): The specific model being used, 'gpt-4o-mini'.
+        - `temperature` (Float): Controls the randomness of the model's output. A higher value (closer to 1) makes the output more random.
+        - `max_tokens` (Integer): Controls how many tokens are used in the response.
+        - `top_p` (Float): Controls the diversity of word selection. A higher value (closer to 1) makes word selection more diverse.
+        - `stream` (Boolean): Controls if the response is streamed back to the user (set to false).
+        - `online` (Boolean): Controls whether to use internet to get more context for answering query (set to false).
+        - `token_usage` (Boolean): Controls whether to use token usage for the querying models (set to false).
+        - `prompt` (String): A prompt for the model to follow when generating responses, requires `$context` and `$query` variables.
+        - `system_prompt` (String): A system prompt for the model to follow when generating responses, in this case, it's set to the style of William Shakespeare.
+        - `number_documents` (Integer): Number of documents to pull from the vectordb as context, defaults to 1
+        - `api_key` (String): The API key for the language model.
+        - `model_kwargs` (Dict): Keyword arguments to pass to the language model. Used for `aws_bedrock` provider, since it requires different arguments for each model.
+        - `http_client_proxies` (Dict | String): The proxy server settings used to create `self.http_client` using `httpx.Client(proxies=http_client_proxies)`
+        - `http_async_client_proxies` (Dict | String): The proxy server settings for async calls used to create `self.http_async_client` using `httpx.AsyncClient(proxies=http_async_client_proxies)`
+3. `vectordb` Section:
+    - `provider` (String): The provider for the vector database, set to 'chroma'. You can find the full list of vector database providers in [our docs](/components/vector-databases).
+    - `config`:
+        - `collection_name` (String): The initial collection name for the vectordb, set to 'full-stack-app'.
+        - `dir` (String): The directory for the local database, set to 'db'.
+        - `allow_reset` (Boolean): Indicates whether resetting the vectordb is allowed, set to true.
+        - `batch_size` (Integer): The batch size for docs insertion in vectordb, defaults to `100`
+    <Note>We recommend you to checkout vectordb specific config [here](https://docs.embedchain.ai/components/vector-databases)</Note>
+4. `embedder` Section:
+    - `provider` (String): The provider for the embedder, set to 'openai'. You can find the full list of embedding model providers in [our docs](/components/embedding-models).
+    - `config`:
+        - `model` (String): The specific model used for text embedding, 'text-embedding-ada-002'.
+        - `vector_dimension` (Integer): The vector dimension of the embedding model. [Defaults](https://github.com/embedchain/embedchain/blob/main/embedchain/models/vector_dimensions.py)
+        - `api_key` (String): The API key for the embedding model.
+        - `endpoint` (String): The endpoint for the HuggingFace embedding model.
+        - `deployment_name` (String): The deployment name for the embedding model.
+        - `title` (String): The title for the embedding model for Google Embedder.
+        - `task_type` (String): The task type for the embedding model for Google Embedder.
+        - `model_kwargs` (Dict): Used to pass extra arguments to embedders.
+        - `http_client_proxies` (Dict | String): The proxy server settings used to create `self.http_client` using `httpx.Client(proxies=http_client_proxies)`
+        - `http_async_client_proxies` (Dict | String): The proxy server settings for async calls used to create `self.http_async_client` using `httpx.AsyncClient(proxies=http_async_client_proxies)`
+5. `chunker` Section:
+    - `chunk_size` (Integer): The size of each chunk of text that is sent to the language model.
+    - `chunk_overlap` (Integer): The amount of overlap between each chunk of text.
+    - `length_function` (String): The function used to calculate the length of each chunk of text. In this case, it's set to 'len'. You can also use any function import directly as a string here.
+    - `min_chunk_size` (Integer): The minimum size of each chunk of text that is sent to the language model. Must be less than `chunk_size`, and greater than `chunk_overlap`.
+6. `cache` Section: (Optional)
+    - `similarity_evaluation` (Optional): The config for similarity evaluation strategy. If not provided, the default `distance` based similarity evaluation strategy is used.
+      - `strategy` (String): The strategy to use for similarity evaluation. Currently, only `distance` and `exact` based similarity evaluation is supported. Defaults to `distance`.
+      - `max_distance` (Float): The bound of maximum distance. Defaults to `1.0`.
+      - `positive` (Boolean): If the larger distance indicates more similar of two entities, set it `True`, otherwise `False`. Defaults to `False`.
+    - `config` (Optional): The config for initializing the cache. If not provided, sensible default values are used as mentioned below.
+      - `similarity_threshold` (Float): The threshold for similarity evaluation. Defaults to `0.8`.
+      - `auto_flush` (Integer): The number of queries after which the cache is flushed. Defaults to `20`.
+7. `memory` Section: (Optional)
+    - `top_k` (Integer): The number of top-k results to return. Defaults to `10`.
+    <Note>
+    If you provide a cache section, the app will automatically configure and use a cache to store the results of the language model. This is useful if you want to speed up the response time and save inference cost of your app.
+    </Note>
+If you have questions about the configuration above, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />

embedchain/docs/api-reference/app/add.mdx

@@ -0,0 +1,47 @@
+---
+title: '📊 add'
+---
+
+`add()` method is used to load the data sources from different data sources to a RAG pipeline. You can find the signature below:
+
+### Parameters
+
+<ParamField path="source" type="str">
+    The data to embed, can be a URL, local file or raw content, depending on the data type.. You can find the full list of supported data sources [here](/components/data-sources/overview).
+</ParamField>
+<ParamField path="data_type" type="str" optional>
+    Type of data source. It can be automatically detected but user can force what data type to load as.
+</ParamField>
+<ParamField path="metadata" type="dict" optional>
+    Any metadata that you want to store with the data source. Metadata is generally really useful for doing metadata filtering on top of semantic search to yield faster search and better results.
+</ParamField>
+<ParamField path="all_references" type="bool" optional>
+    This parameter instructs Embedchain to retrieve all the context and information from the specified link, as well as from any reference links on the page.
+</ParamField>
+
+## Usage
+
+### Load data from webpage
+
+```python Code example
+from embedchain import App
+
+app = App()
+app.add("https://www.forbes.com/profile/elon-musk")
+# Inserting batches in chromadb: 100%|███████████████| 1/1 [00:00<00:00,  1.19it/s]
+# Successfully saved https://www.forbes.com/profile/elon-musk (DataType.WEB_PAGE). New chunks count: 4
+```
+
+### Load data from sitemap
+
+```python Code example
+from embedchain import App
+
+app = App()
+app.add("https://python.langchain.com/sitemap.xml", data_type="sitemap")
+# Loading pages: 100%|█████████████| 1108/1108 [00:47<00:00, 23.17it/s]
+# Inserting batches in chromadb: 100%|█████████| 111/111 [04:41<00:00,  2.54s/it]
+# Successfully saved https://python.langchain.com/sitemap.xml (DataType.SITEMAP). New chunks count: 11024
+```
+
+You can find complete list of supported data sources [here](/components/data-sources/overview).

embedchain/docs/api-reference/app/chat.mdx

@@ -0,0 +1,175 @@
+---
+title: '💬 chat'
+---
+
+`chat()` method allows you to chat over your data sources using a user-friendly chat API. You can find the signature below:
+
+### Parameters
+
+<ParamField path="input_query" type="str">
+    Question to ask
+</ParamField>
+<ParamField path="config" type="BaseLlmConfig" optional>
+    Configure different llm settings such as prompt, temprature, number_documents etc.
+</ParamField>
+<ParamField path="dry_run" type="bool" optional>
+    The purpose is to test the prompt structure without actually running LLM inference. Defaults to `False`
+</ParamField>
+<ParamField path="where" type="dict" optional>
+    A dictionary of key-value pairs to filter the chunks from the vector database. Defaults to `None`
+</ParamField>
+<ParamField path="session_id" type="str" optional>
+    Session ID of the chat. This can be used to maintain chat history of different user sessions. Default value: `default`
+</ParamField>
+<ParamField path="citations" type="bool" optional>
+    Return citations along with the LLM answer. Defaults to `False`
+</ParamField>
+
+### Returns
+
+<ResponseField name="answer" type="str | tuple">
+  If `citations=False`, return a stringified answer to the question asked. <br />
+  If `citations=True`, returns a tuple with answer and citations respectively.
+</ResponseField>
+
+## Usage
+
+### With citations
+
+If you want to get the answer to question and return both answer and citations, use the following code snippet:
+
+```python With Citations
+from embedchain import App
+
+# Initialize app
+app = App()
+
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Get relevant answer for your query
+answer, sources = app.chat("What is the net worth of Elon?", citations=True)
+print(answer)
+# Answer: The net worth of Elon Musk is $221.9 billion.
+
+print(sources)
+# [
+#    (
+#        'Elon Musk PROFILEElon MuskCEO, Tesla$247.1B$2.3B (0.96%)Real Time Net Worthas of 12/7/23 ...',
+#        {
+#           'url': 'https://www.forbes.com/profile/elon-musk', 
+#           'score': 0.89,
+#           ...
+#        }
+#    ),
+#    (
+#        '74% of the company, which is now called X.Wealth HistoryHOVER TO REVEAL NET WORTH BY YEARForbes ...',
+#        {
+#           'url': 'https://www.forbes.com/profile/elon-musk', 
+#           'score': 0.81,
+#           ...
+#        }
+#    ),
+#    (
+#        'founded in 2002, is worth nearly $150 billion after a $750 million tender offer in June 2023 ...',
+#        {
+#           'url': 'https://www.forbes.com/profile/elon-musk', 
+#           'score': 0.73,
+#           ...
+#        }
+#    )
+# ]
+```
+
+<Note>
+When `citations=True`, note that the returned `sources` are a list of tuples where each tuple has two elements (in the following order):
+1. source chunk
+2. dictionary with metadata about the source chunk
+    - `url`: url of the source
+    - `doc_id`: document id (used for book keeping purposes)
+    - `score`: score of the source chunk with respect to the question
+    - other metadata you might have added at the time of adding the source
+</Note>
+
+
+### Without citations
+
+If you just want to return answers and don't want to return citations, you can use the following example:
+
+```python Without Citations
+from embedchain import App
+
+# Initialize app
+app = App()
+
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Chat on your data using `.chat()`
+answer = app.chat("What is the net worth of Elon?")
+print(answer)
+# Answer: The net worth of Elon Musk is $221.9 billion.
+```
+
+### With session id
+
+If you want to maintain chat sessions for different users, you can simply pass the `session_id` keyword argument. See the example below:
+
+```python With session id
+from embedchain import App
+
+app = App()
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Chat on your data using `.chat()`
+app.chat("What is the net worth of Elon Musk?", session_id="user1")
+# 'The net worth of Elon Musk is $250.8 billion.'
+app.chat("What is the net worth of Bill Gates?", session_id="user2")
+# "I don't know the current net worth of Bill Gates."
+app.chat("What was my last question", session_id="user1")
+# 'Your last question was "What is the net worth of Elon Musk?"'
+```
+
+### With custom context window
+
+If you want to customize the context window that you want to use during chat (default context window is 3 document chunks), you can do using the following code snippet:
+
+```python with custom chunks size
+from embedchain import App
+from embedchain.config import BaseLlmConfig
+
+app = App()
+app.add("https://www.forbes.com/profile/elon-musk")
+
+query_config = BaseLlmConfig(number_documents=5)
+app.chat("What is the net worth of Elon Musk?", config=query_config)
+```
+
+### With Mem0 to store chat history
+
+Mem0 is a cutting-edge long-term memory for LLMs to enable personalization for the GenAI stack. It enables LLMs to remember past interactions and provide more personalized responses. 
+
+In order to use Mem0 to enable memory for personalization in your apps:
+- Install the [`mem0`](https://docs.mem0.ai/) package using `pip install mem0ai`. 
+- Prepare config for `memory`, refer [Configurations](docs/api-reference/advanced/configuration.mdx).
+
+```python with mem0
+from embedchain import App
+
+config = {
+  "memory": {
+    "top_k": 5
+  }
+}
+
+app = App.from_config(config=config)
+app.add("https://www.forbes.com/profile/elon-musk")
+
+app.chat("What is the net worth of Elon Musk?")
+```
+
+## How Mem0 works:
+- Mem0 saves context derived from each user question into its memory.
+- When a user poses a new question, Mem0 retrieves relevant previous memories.
+- The `top_k` parameter in the memory configuration specifies the number of top memories to consider during retrieval.
+- Mem0 generates the final response by integrating the user's question, context from the data source, and the relevant memories.

embedchain/docs/api-reference/app/delete.mdx

@@ -0,0 +1,48 @@
+---
+title: 🗑 delete
+---
+
+## Delete Document
+
+`delete()` method allows you to delete a document previously added to the app.
+
+### Usage
+
+```python
+from embedchain import App
+
+app = App()
+
+forbes_doc_id = app.add("https://www.forbes.com/profile/elon-musk")
+wiki_doc_id = app.add("https://en.wikipedia.org/wiki/Elon_Musk")
+
+app.delete(forbes_doc_id)   # deletes the forbes document
+```
+
+<Note>
+    If you do not have the document id, you can use `app.db.get()` method to get the document and extract the `hash` key from `metadatas` dictionary object, which serves as the document id.
+</Note>
+
+
+## Delete Chat Session History
+
+`delete_session_chat_history()` method allows you to delete all previous messages in a chat history.
+
+### Usage
+
+```python
+from embedchain import App
+
+app = App()
+
+app.add("https://www.forbes.com/profile/elon-musk")
+
+app.chat("What is the net worth of Elon Musk?")
+
+app.delete_session_chat_history()
+```
+
+<Note>
+    `delete_session_chat_history(session_id="session_1")` method also accepts `session_id` optional param for deleting chat history of a specific session.
+    It assumes the default session if no `session_id` is provided.
+</Note>

embedchain/docs/api-reference/app/deploy.mdx

@@ -0,0 +1,5 @@
+---
+title: 🚀 deploy
+---
+
+The `deploy()` method is currently available on an invitation-only basis. To request access, please submit your information via the provided [Google Form](https://forms.gle/vigN11h7b4Ywat668). We will review your request and respond promptly.

embedchain/docs/api-reference/app/evaluate.mdx

@@ -0,0 +1,41 @@
+---
+title: '📝 evaluate'
+---
+
+`evaluate()` method is used to evaluate the performance of a RAG app. You can find the signature below:
+
+### Parameters
+
+<ParamField path="question" type="Union[str, list[str]]">
+    A question or a list of questions to evaluate your app on.
+</ParamField>
+<ParamField path="metrics" type="Optional[list[Union[BaseMetric, str]]]" optional>
+    The metrics to evaluate your app on. Defaults to all metrics: `["context_relevancy", "answer_relevancy", "groundedness"]`
+</ParamField>
+<ParamField path="num_workers" type="int" optional>
+    Specify the number of threads to use for parallel processing.
+</ParamField>
+
+### Returns
+
+<ResponseField name="metrics" type="dict">
+    Returns the metrics you have chosen to evaluate your app on as a dictionary.
+</ResponseField>
+
+## Usage
+
+```python
+from embedchain import App
+
+app = App()
+
+# add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# run evaluation
+app.evaluate("what is the net worth of Elon Musk?")
+# {'answer_relevancy': 0.958019958036268, 'context_relevancy': 0.12903225806451613}
+
+# or
+# app.evaluate(["what is the net worth of Elon Musk?", "which companies does Elon Musk own?"])
+```

embedchain/docs/api-reference/app/get.mdx

@@ -0,0 +1,33 @@
+---
+title: 📄 get
+---
+
+## Get data sources
+
+`get_data_sources()` returns a list of all the data sources added in the app.
+
+
+### Usage
+
+```python
+from embedchain import App
+
+app = App()
+
+app.add("https://www.forbes.com/profile/elon-musk")
+app.add("https://en.wikipedia.org/wiki/Elon_Musk")
+
+data_sources = app.get_data_sources()
+# [
+#   {
+#       'data_type': 'web_page',
+#       'data_value': 'https://en.wikipedia.org/wiki/Elon_Musk',
+#       'metadata': 'null'
+#   },
+#   {
+#       'data_type': 'web_page',
+#       'data_value': 'https://www.forbes.com/profile/elon-musk',
+#       'metadata': 'null'
+#   }
+# ]
+```

embedchain/docs/api-reference/app/overview.mdx

@@ -0,0 +1,130 @@
+---
+title: "App"
+---
+
+Create a RAG app object on Embedchain. This is the main entrypoint for a developer to interact with Embedchain APIs. An app configures the llm, vector database, embedding model, and retrieval strategy of your choice.
+
+### Attributes
+
+<ParamField path="local_id" type="str">
+    App ID
+</ParamField>
+<ParamField path="name" type="str" optional>
+    Name of the app
+</ParamField>
+<ParamField path="config" type="BaseConfig">
+    Configuration of the app
+</ParamField>
+<ParamField path="llm" type="BaseLlm">
+    Configured LLM for the RAG app
+</ParamField>
+<ParamField path="db" type="BaseVectorDB">
+    Configured vector database for the RAG app
+</ParamField>
+<ParamField path="embedding_model" type="BaseEmbedder">
+    Configured embedding model for the RAG app
+</ParamField>
+<ParamField path="chunker" type="ChunkerConfig">
+    Chunker configuration
+</ParamField>
+<ParamField path="client" type="Client" optional>
+    Client object (used to deploy an app to Embedchain platform)
+</ParamField>
+<ParamField path="logger" type="logging.Logger">
+    Logger object
+</ParamField>
+
+## Usage
+
+You can create an app instance using the following methods:
+
+### Default setting
+
+```python Code Example
+from embedchain import App
+app = App()
+```
+
+
+### Python Dict
+
+```python Code Example
+from embedchain import App
+
+config_dict = {
+  'llm': {
+    'provider': 'gpt4all',
+    'config': {
+      'model': 'orca-mini-3b-gguf2-q4_0.gguf',
+      'temperature': 0.5,
+      'max_tokens': 1000,
+      'top_p': 1,
+      'stream': False
+    }
+  },
+  'embedder': {
+    'provider': 'gpt4all'
+  }
+}
+
+# load llm configuration from config dict
+app = App.from_config(config=config_dict)
+```
+
+### YAML Config
+
+<CodeGroup>
+
+```python main.py
+from embedchain import App
+
+# load llm configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: gpt4all
+  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: gpt4all
+```
+
+</CodeGroup>
+
+### JSON Config
+
+<CodeGroup>
+
+```python main.py
+from embedchain import App
+
+# load llm configuration from config.json file
+app = App.from_config(config_path="config.json")
+```
+
+```json config.json
+{
+  "llm": {
+    "provider": "gpt4all",
+    "config": {
+      "model": "orca-mini-3b-gguf2-q4_0.gguf",
+      "temperature": 0.5,
+      "max_tokens": 1000,
+      "top_p": 1,
+      "stream": false
+    }
+  },
+  "embedder": {
+    "provider": "gpt4all"
+  }
+}
+```
+
+</CodeGroup>

embedchain/docs/api-reference/app/query.mdx

@@ -0,0 +1,109 @@
+---
+title: '❓ query'
+---
+
+`.query()` method empowers developers to ask questions and receive relevant answers through a user-friendly query API. Function signature is given below:
+
+### Parameters
+
+<ParamField path="input_query" type="str">
+    Question to ask
+</ParamField>
+<ParamField path="config" type="BaseLlmConfig" optional>
+    Configure different llm settings such as prompt, temprature, number_documents etc.
+</ParamField>
+<ParamField path="dry_run" type="bool" optional>
+    The purpose is to test the prompt structure without actually running LLM inference. Defaults to `False`
+</ParamField>
+<ParamField path="where" type="dict" optional>
+    A dictionary of key-value pairs to filter the chunks from the vector database. Defaults to `None`
+</ParamField>
+<ParamField path="citations" type="bool" optional>
+    Return citations along with the LLM answer. Defaults to `False`
+</ParamField>
+
+### Returns
+
+<ResponseField name="answer" type="str | tuple">
+  If `citations=False`, return a stringified answer to the question asked. <br />
+  If `citations=True`, returns a tuple with answer and citations respectively.
+</ResponseField>
+
+## Usage
+
+### With citations
+
+If you want to get the answer to question and return both answer and citations, use the following code snippet:
+
+```python With Citations
+from embedchain import App
+
+# Initialize app
+app = App()
+
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Get relevant answer for your query
+answer, sources = app.query("What is the net worth of Elon?", citations=True)
+print(answer)
+# Answer: The net worth of Elon Musk is $221.9 billion.
+
+print(sources)
+# [
+#    (
+#        'Elon Musk PROFILEElon MuskCEO, Tesla$247.1B$2.3B (0.96%)Real Time Net Worthas of 12/7/23 ...',
+#        {
+#           'url': 'https://www.forbes.com/profile/elon-musk', 
+#           'score': 0.89,
+#           ...
+#        }
+#    ),
+#    (
+#        '74% of the company, which is now called X.Wealth HistoryHOVER TO REVEAL NET WORTH BY YEARForbes ...',
+#        {
+#           'url': 'https://www.forbes.com/profile/elon-musk', 
+#           'score': 0.81,
+#           ...
+#        }
+#    ),
+#    (
+#        'founded in 2002, is worth nearly $150 billion after a $750 million tender offer in June 2023 ...',
+#        {
+#           'url': 'https://www.forbes.com/profile/elon-musk', 
+#           'score': 0.73,
+#           ...
+#        }
+#    )
+# ]
+```
+
+<Note>
+When `citations=True`, note that the returned `sources` are a list of tuples where each tuple has two elements (in the following order):
+1. source chunk
+2. dictionary with metadata about the source chunk
+    - `url`: url of the source
+    - `doc_id`: document id (used for book keeping purposes)
+    - `score`: score of the source chunk with respect to the question
+    - other metadata you might have added at the time of adding the source
+</Note>
+
+### Without citations
+
+If you just want to return answers and don't want to return citations, you can use the following example:
+
+```python Without Citations
+from embedchain import App
+
+# Initialize app
+app = App()
+
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Get relevant answer for your query
+answer = app.query("What is the net worth of Elon?")
+print(answer)
+# Answer: The net worth of Elon Musk is $221.9 billion.
+```
+

embedchain/docs/api-reference/app/reset.mdx

@@ -0,0 +1,17 @@
+---
+title: 🔄 reset
+---
+
+`reset()` method allows you to wipe the data from your RAG application and start from scratch.
+
+## Usage
+
+```python
+from embedchain import App
+
+app = App()
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Reset the app
+app.reset()
+```

embedchain/docs/api-reference/app/search.mdx

@@ -0,0 +1,111 @@
+---
+title: '🔍 search'
+---
+
+`.search()` enables you to uncover the most pertinent context by performing a semantic search across your data sources based on a given query. Refer to the function signature below:
+
+### Parameters
+
+<ParamField path="query" type="str">
+    Question
+</ParamField>
+<ParamField path="num_documents" type="int" optional>
+    Number of relevant documents to fetch. Defaults to `3`
+</ParamField>
+<ParamField path="where" type="dict" optional>
+    Key value pair for metadata filtering.
+</ParamField>
+<ParamField path="raw_filter" type="dict" optional>
+    Pass raw filter query based on your vector database.
+    Currently, `raw_filter` param is only supported for Pinecone vector database.
+</ParamField>
+
+### Returns
+
+<ResponseField name="answer" type="dict">
+    Return list of dictionaries that contain the relevant chunk and their source information.
+</ResponseField>
+
+## Usage
+
+### Basic
+
+Refer to the following example on how to use the search api:
+
+```python Code example
+from embedchain import App
+
+app = App()
+app.add("https://www.forbes.com/profile/elon-musk")
+
+context = app.search("What is the net worth of Elon?", num_documents=2)
+print(context)
+```
+
+### Advanced
+
+#### Metadata filtering using `where` params
+
+Here is an advanced example of `search()` API with metadata filtering on pinecone database:
+
+```python
+import os
+
+from embedchain import App
+
+os.environ["PINECONE_API_KEY"] = "xxx"
+
+config = {
+    "vectordb": {
+        "provider": "pinecone",
+        "config": {
+            "metric": "dotproduct",
+            "vector_dimension": 1536,
+            "index_name": "ec-test",
+            "serverless_config": {"cloud": "aws", "region": "us-west-2"},
+        },
+    }
+}
+
+app = App.from_config(config=config)
+
+app.add("https://www.forbes.com/profile/bill-gates", metadata={"type": "forbes", "person": "gates"})
+app.add("https://en.wikipedia.org/wiki/Bill_Gates", metadata={"type": "wiki", "person": "gates"})
+
+results = app.search("What is the net worth of Bill Gates?", where={"person": "gates"})
+print("Num of search results: ", len(results))
+```
+
+#### Metadata filtering using `raw_filter` params
+
+Following is an example of metadata filtering by passing the raw filter query that pinecone vector database follows:
+
+```python
+import os
+
+from embedchain import App
+
+os.environ["PINECONE_API_KEY"] = "xxx"
+
+config = {
+    "vectordb": {
+        "provider": "pinecone",
+        "config": {
+            "metric": "dotproduct",
+            "vector_dimension": 1536,
+            "index_name": "ec-test",
+            "serverless_config": {"cloud": "aws", "region": "us-west-2"},
+        },
+    }
+}
+
+app = App.from_config(config=config)
+
+app.add("https://www.forbes.com/profile/bill-gates", metadata={"year": 2022, "person": "gates"})
+app.add("https://en.wikipedia.org/wiki/Bill_Gates", metadata={"year": 2024, "person": "gates"})
+
+print("Filter with person: gates and year > 2023")
+raw_filter = {"$and": [{"person": "gates"}, {"year": {"$gt": 2023}}]}
+results = app.search("What is the net worth of Bill Gates?", raw_filter=raw_filter)
+print("Num of search results: ", len(results))
+```

embedchain/docs/api-reference/store/ai-assistants.mdx

@@ -0,0 +1,54 @@
+---
+title: 'AI Assistant'
+---
+
+The `AIAssistant` class, an alternative to the OpenAI Assistant API, is designed for those who prefer using large language models (LLMs) other than those provided by OpenAI. It facilitates the creation of AI Assistants with several key benefits:
+
+- **Visibility into Citations**: It offers transparent access to the sources and citations used by the AI, enhancing the understanding and trustworthiness of its responses.
+
+- **Debugging Capabilities**: Users have the ability to delve into and debug the AI's processes, allowing for a deeper understanding and fine-tuning of its performance.
+
+- **Customizable Prompts**: The class provides the flexibility to modify and tailor prompts according to specific needs, enabling more precise and relevant interactions.
+
+- **Chain of Thought Integration**: It supports the incorporation of a 'chain of thought' approach, which helps in breaking down complex queries into simpler, sequential steps, thereby improving the clarity and accuracy of responses.
+
+It is ideal for those who value customization, transparency, and detailed control over their AI Assistant's functionalities.
+
+### Arguments
+
+<ParamField path="name" type="string" optional>
+  Name for your AI assistant
+</ParamField>
+
+<ParamField path="instructions" type="string" optional>
+  How the Assistant and model should behave or respond
+</ParamField>
+
+<ParamField path="assistant_id" type="string" optional>
+  Load existing AI Assistant. If you pass this, you don't have to pass other arguments.
+</ParamField>
+
+<ParamField path="thread_id" type="string" optional>
+  Existing thread id if exists
+</ParamField>
+
+<ParamField path="yaml_path" type="str" Optional>
+    Embedchain pipeline config yaml path to use. This will define the configuration of the AI Assistant (such as configuring the LLM, vector database, and embedding model)
+</ParamField>
+
+<ParamField path="data_sources" type="list" default="[]">
+  Add data sources to your assistant. You can add in the following format: `[{"source": "https://example.com", "data_type": "web_page"}]`
+</ParamField>
+
+<ParamField path="collect_metrics" type="boolean" default="True">
+  Anonymous telemetry (doesn't collect any user information or user's files). Used to improve the Embedchain package utilization. Default is `True`.
+</ParamField>
+
+
+## Usage
+
+For detailed guidance on creating your own AI Assistant, click the link below. It provides step-by-step instructions to help you through the process:
+
+<Card title="Guide to Creating Your AI Assistant" icon="link" href="/examples/opensource-assistant">
+  Learn how to build a customized AI Assistant using the `AIAssistant` class.
+</Card>

embedchain/docs/api-reference/store/openai-assistant.mdx

@@ -0,0 +1,45 @@
+---
+title: 'OpenAI Assistant'
+---
+
+### Arguments
+
+<ParamField path="name" type="string">
+  Name for your AI assistant
+</ParamField>
+
+<ParamField path="instructions" type="string">
+  how the Assistant and model should behave or respond
+</ParamField>
+
+<ParamField path="assistant_id" type="string">
+  Load existing OpenAI Assistant. If you pass this, you don't have to pass other arguments.
+</ParamField>
+
+<ParamField path="thread_id" type="string">
+  Existing OpenAI thread id if exists
+</ParamField>
+
+<ParamField path="model" type="str" default="gpt-4-1106-preview">
+  OpenAI model to use
+</ParamField>
+
+<ParamField path="tools" type="list">
+  OpenAI tools to use. Default set to `[{"type": "retrieval"}]`
+</ParamField>
+
+<ParamField path="data_sources" type="list" default="[]">
+  Add data sources to your assistant. You can add in the following format: `[{"source": "https://example.com", "data_type": "web_page"}]`
+</ParamField>
+
+<ParamField path="telemetry" type="boolean" default="True">
+  Anonymous telemetry (doesn't collect any user information or user's files). Used to improve the Embedchain package utilization. Default is `True`.
+</ParamField>
+
+## Usage
+
+For detailed guidance on creating your own OpenAI Assistant, click the link below. It provides step-by-step instructions to help you through the process:
+
+<Card title="Guide to Creating Your OpenAI Assistant" icon="link" href="/examples/openai-assistant">
+  Learn how to build an OpenAI Assistant using the `OpenAIAssistant` class.
+</Card>

embedchain/docs/community/connect-with-us.mdx

@@ -0,0 +1,28 @@
+---
+title: 🤝 Connect with Us
+---
+
+We believe in building a vibrant and supportive community around embedchain. There are various channels through which you can connect with us, stay updated, and contribute to the ongoing discussions:
+
+<CardGroup cols={3}>
+  <Card title="Twitter" icon="twitter" href="https://twitter.com/embedchain">
+    Follow us on Twitter
+  </Card>
+  <Card title="Slack" icon="slack" href="https://embedchain.ai/slack" color="#4A154B">
+    Join our slack community
+  </Card>
+  <Card title="Discord" icon="discord" href="https://discord.gg/6PzXDgEjG5" color="#7289DA">
+    Join our discord community
+  </Card>
+  <Card title="LinkedIn" icon="linkedin" href="https://www.linkedin.com/company/embedchain/">
+  Connect with us on LinkedIn
+  </Card>
+  <Card title="Schedule a call" icon="calendar" href="https://cal.com/taranjeetio/ec">
+  Schedule a call with Embedchain founder
+  </Card>
+  <Card title="Newsletter" icon="message" href="https://embedchain.substack.com/">
+  Subscribe to our newsletter
+  </Card>
+</CardGroup>
+
+We look forward to connecting with you and seeing how we can create amazing things together!

embedchain/docs/components/data-sources/audio.mdx

@@ -0,0 +1,25 @@
+---
+title: "🎤 Audio"
+---
+
+
+To use an audio as data source, just add `data_type` as `audio` and pass in the path of the audio (local or hosted).
+
+We use [Deepgram](https://developers.deepgram.com/docs/introduction) to transcribe the audiot to text, and then use the generated text as the data source.
+
+You would require an Deepgram API key which is available [here](https://console.deepgram.com/signup?jump=keys) to use this feature.
+
+### Without customization
+
+```python
+import os
+from embedchain import App
+
+os.environ["DEEPGRAM_API_KEY"] = "153xxx"
+
+app = App()
+app.add("introduction.wav", data_type="audio")
+response = app.query("What is my name and how old am I?")
+print(response)
+# Answer: Your name is Dave and you are 21 years old.
+```

embedchain/docs/components/data-sources/beehiiv.mdx

@@ -0,0 +1,16 @@
+---
+title: "🐝 Beehiiv"
+---
+
+To add any Beehiiv data sources to your app, just add the base url as the source and set the data_type to `beehiiv`.
+
+```python
+from embedchain import App
+
+app = App()
+
+# source: just add the base url and set the data_type to 'beehiiv'
+app.add('https://aibreakfast.beehiiv.com', data_type='beehiiv')
+app.query("How much is OpenAI paying developers?")
+# Answer: OpenAI is aggressively recruiting Google's top AI researchers with offers ranging between $5 to $10 million annually, primarily in stock options.
+```

embedchain/docs/components/data-sources/csv.mdx

@@ -0,0 +1,28 @@
+---
+title: '📊 CSV'
+---
+
+You can load any csv file from your local file system or through a URL. Headers are included for each line, so if you have an `age` column, `18` will be added as `age: 18`.
+
+## Usage
+
+### Load from a local file
+
+```python
+from embedchain import App
+app = App()
+app.add('/path/to/file.csv', data_type='csv')
+```
+
+### Load from URL
+
+```python
+from embedchain import App
+app = App()
+app.add('https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv', data_type="csv")
+```
+
+<Note>
+There is a size limit allowed for csv file beyond which it can throw error. This limit is set by the LLMs. Please consider chunking large csv files into smaller csv files.
+</Note>
+

embedchain/docs/components/data-sources/custom.mdx

@@ -0,0 +1,42 @@
+---
+title: '⚙️ Custom'
+---
+
+When we say "custom", we mean that you can customize the loader and chunker to your needs. This is done by passing a custom loader and chunker to the `add` method.
+
+```python
+from embedchain import App
+import your_loader
+from my_module import CustomLoader
+from my_module import CustomChunker
+
+app = App()
+loader = CustomLoader()
+chunker = CustomChunker()
+
+app.add("source", data_type="custom", loader=loader, chunker=chunker)
+```
+
+<Note>
+    The custom loader and chunker must be a class that inherits from the [`BaseLoader`](https://github.com/embedchain/embedchain/blob/main/embedchain/loaders/base_loader.py) and [`BaseChunker`](https://github.com/embedchain/embedchain/blob/main/embedchain/chunkers/base_chunker.py) classes respectively.
+</Note>
+
+<Note>
+    If the `data_type` is not a valid data type, the `add` method will fallback to the `custom` data type and expect a custom loader and chunker to be passed by the user.
+</Note>
+
+Example:
+
+```python
+from embedchain import App
+from embedchain.loaders.github import GithubLoader
+
+app = App()
+
+loader = GithubLoader(config={"token": "ghp_xxx"})
+
+app.add("repo:embedchain/embedchain type:repo", data_type="github", loader=loader)
+
+app.query("What is Embedchain?")
+# Answer: Embedchain is a Data Platform for Large Language Models (LLMs). It allows users to seamlessly load, index, retrieve, and sync unstructured data in order to build dynamic, LLM-powered applications. There is also a JavaScript implementation called embedchain-js available on GitHub.
+```

embedchain/docs/components/data-sources/data-type-handling.mdx

@@ -0,0 +1,85 @@
+---
+title: 'Data type handling'
+---
+
+## Automatic data type detection
+
+The add method automatically tries to detect the data_type, based on your input for the source argument. So `app.add('https://www.youtube.com/watch?v=dQw4w9WgXcQ')` is enough to embed a YouTube video.
+
+This detection is implemented for all formats. It is based on factors such as whether it's a URL, a local file, the source data type, etc.
+
+### Debugging automatic detection
+
+Set `log_level: DEBUG` in the config yaml to debug if the data type detection is done right or not. Otherwise, you will not know when, for instance, an invalid filepath is interpreted as raw text instead.
+
+### Forcing a data type
+
+To omit any issues with the data type detection, you can **force** a data_type by adding it as a `add` method argument.
+The examples below show you the keyword to force the respective `data_type`.
+
+Forcing can also be used for edge cases, such as interpreting a sitemap as a web_page, for reading its raw text instead of following links.
+
+## Remote data types
+
+<Tip>
+**Use local files in remote data types**
+
+Some data_types are meant for remote content and only work with URLs.
+You can pass local files by formatting the path using the `file:` [URI scheme](https://en.wikipedia.org/wiki/File_URI_scheme), e.g. `file:///info.pdf`.
+</Tip>
+
+## Reusing a vector database
+
+Default behavior is to create a persistent vector db in the directory **./db**. You can split your application into two Python scripts: one to create a local vector db and the other to reuse this local persistent vector db. This is useful when you want to index hundreds of documents and separately implement a chat interface.
+
+Create a local index:
+
+```python
+from embedchain import App
+
+config = {
+    "app": {
+        "config": {
+            "id": "app-1"
+        }
+    }
+}
+naval_chat_bot = App.from_config(config=config)
+naval_chat_bot.add("https://www.youtube.com/watch?v=3qHkcs3kG44")
+naval_chat_bot.add("https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf")
+```
+
+You can reuse the local index with the same code, but without adding new documents:
+
+```python
+from embedchain import App
+
+config = {
+    "app": {
+        "config": {
+            "id": "app-1"
+        }
+    }
+}
+naval_chat_bot = App.from_config(config=config)
+print(naval_chat_bot.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?"))
+```
+
+## Resetting an app and vector database
+
+You can reset the app by simply calling the `reset` method. This will delete the vector database and all other app related files.
+
+```python
+from embedchain import App
+
+app = App()config = {
+    "app": {
+        "config": {
+            "id": "app-1"
+        }
+    }
+}
+naval_chat_bot = App.from_config(config=config)
+app.add("https://www.youtube.com/watch?v=3qHkcs3kG44")
+app.reset()
+```

embedchain/docs/components/data-sources/directory.mdx

@@ -0,0 +1,41 @@
+---
+title: '📁 Directory/Folder'
+---
+
+To use an entire directory as data source, just add `data_type` as `directory` and pass in the path of the local directory.
+
+### Without customization
+
+```python
+import os
+from embedchain import App
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+app = App()
+app.add("./elon-musk", data_type="directory")
+response = app.query("list all files")
+print(response)
+# Answer: Files are elon-musk-1.txt, elon-musk-2.pdf.
+```
+
+### Customization
+
+```python
+import os
+from embedchain import App
+from embedchain.loaders.directory_loader import DirectoryLoader
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+lconfig = {
+    "recursive": True,
+    "extensions": [".txt"]
+}
+loader = DirectoryLoader(config=lconfig)
+app = App()
+app.add("./elon-musk", loader=loader)
+response = app.query("what are all the files related to?")
+print(response)
+
+# Answer: The files are related to Elon Musk.
+```

embedchain/docs/components/data-sources/discord.mdx

@@ -0,0 +1,28 @@
+---
+title: "💬 Discord"
+---
+
+To add any Discord channel messages to your app, just add the `channel_id` as the source and set the `data_type` to `discord`.
+
+<Note>
+    This loader requires a Discord bot token with read messages access.
+    To obtain the token, follow the instructions provided in this tutorial: 
+    <a href="https://www.writebots.com/discord-bot-token/">How to Get a Discord Bot Token?</a>.
+</Note>
+
+```python
+import os
+from embedchain import App
+
+# add your discord "BOT" token
+os.environ["DISCORD_TOKEN"] = "xxx"
+
+app = App()
+
+app.add("1177296711023075338", data_type="discord")
+
+response = app.query("What is Joe saying about Elon Musk?")
+
+print(response)
+# Answer: Joe is saying "Elon Musk is a genius".
+```

embedchain/docs/components/data-sources/discourse.mdx

@@ -0,0 +1,44 @@
+---
+title: '🗨️ Discourse'
+---
+
+You can now easily load data from your community built with [Discourse](https://discourse.org/).
+
+## Example
+
+1. Setup the Discourse Loader with your community url.
+```Python
+from embedchain.loaders.discourse import DiscourseLoader
+
+dicourse_loader = DiscourseLoader(config={"domain": "https://community.openai.com"})
+```
+
+2. Once you setup the loader, you can create an app and load data using the above discourse loader
+```Python
+import os
+from embedchain.pipeline import Pipeline as App
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+app = App()
+
+app.add("openai after:2023-10-1", data_type="discourse", loader=dicourse_loader)
+
+question = "Where can I find the OpenAI API status page?"
+app.query(question)
+# Answer: You can find the OpenAI API status page at https:/status.openai.com/.
+```
+
+NOTE: The `add` function of the app will accept any executable search query to load data. Refer [Discourse API Docs](https://docs.discourse.org/#tag/Search) to learn more about search queries.
+
+3. We automatically create a chunker to chunk your discourse data, however if you wish to provide your own chunker class. Here is how you can do that:
+```Python
+
+from embedchain.chunkers.discourse import DiscourseChunker
+from embedchain.config.add_config import ChunkerConfig
+
+discourse_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+discourse_chunker = DiscourseChunker(config=discourse_chunker_config)
+
+app.add("openai", data_type='discourse', loader=dicourse_loader, chunker=discourse_chunker)
+```

embedchain/docs/components/data-sources/docs-site.mdx

@@ -0,0 +1,14 @@
+---
+title: '📚 Code Docs website'
+---
+
+To add any code documentation website as a loader, use the data_type as `docs_site`. Eg:
+
+```python
+from embedchain import App
+
+app = App()
+app.add("https://docs.embedchain.ai/", data_type="docs_site")
+app.query("What is Embedchain?")
+# Answer: Embedchain is a platform that utilizes various components, including paid/proprietary ones, to provide what is believed to be the best configuration available. It uses LLM (Language Model) providers such as OpenAI, Anthpropic, Vertex_AI, GPT4ALL, Azure_OpenAI, LLAMA2, JINA, Ollama, Together and COHERE. Embedchain allows users to import and utilize these LLM providers for their applications.'
+```

embedchain/docs/components/data-sources/docx.mdx

@@ -0,0 +1,18 @@
+---
+title: '📄 Docx file'
+---
+
+### Docx file
+
+To add any doc/docx file, use the data_type as `docx`. `docx` allows remote urls and conventional file paths. Eg:
+
+```python
+from embedchain import App
+
+app = App()
+app.add('https://example.com/content/intro.docx', data_type="docx")
+# Or add file using the local file path on your system
+# app.add('content/intro.docx', data_type="docx")
+
+app.query("Summarize the docx data?")
+```

embedchain/docs/components/data-sources/dropbox.mdx

@@ -0,0 +1,37 @@
+---
+title: '💾 Dropbox'
+---
+
+To load folders or files from your Dropbox account, configure the `data_type` parameter as `dropbox` and specify the path to the desired file or folder, starting from the root directory of your Dropbox account.
+
+For Dropbox access, an **access token** is required. Obtain this token by visiting [Dropbox Developer Apps](https://www.dropbox.com/developers/apps). There, create a new app and generate an access token for it.
+
+Ensure your app has the following settings activated:
+
+- In the Permissions section, enable `files.content.read` and `files.metadata.read`.
+
+## Usage
+
+Install the `dropbox` pypi package:
+
+```bash
+pip install dropbox
+```
+
+Following is an example of how to use the dropbox loader:
+
+```python
+import os
+from embedchain import App
+
+os.environ["DROPBOX_ACCESS_TOKEN"] = "sl.xxx"
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+app = App()
+
+# any path from the root of your dropbox account, you can leave it "" for the root folder
+app.add("/test", data_type="dropbox")
+
+print(app.query("Which two celebrities are mentioned here?"))
+# The two celebrities mentioned in the given context are Elon Musk and Jeff Bezos.
+```

embedchain/docs/components/data-sources/excel-file.mdx

@@ -0,0 +1,18 @@
+---
+title: '📄 Excel file'
+---
+
+### Excel file
+
+To add any xlsx/xls file, use the data_type as `excel_file`. `excel_file` allows remote urls and conventional file paths. Eg:
+
+```python
+from embedchain import App
+
+app = App()
+app.add('https://example.com/content/intro.xlsx', data_type="excel_file")
+# Or add file using the local file path on your system
+# app.add('content/intro.xls', data_type="excel_file")
+
+app.query("Give brief information about data.")
+```

embedchain/docs/components/data-sources/github.mdx

@@ -0,0 +1,52 @@
+---
+title: 📝 Github
+---
+
+1. Setup the Github loader by configuring the Github account with username and personal access token (PAT). Check out [this](https://docs.github.com/en/enterprise-server@3.6/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens#creating-a-personal-access-token) link to learn how to create a PAT.
+```Python
+from embedchain.loaders.github import GithubLoader
+
+loader = GithubLoader(
+    config={
+        "token":"ghp_xxxx"
+        }
+    )
+```
+
+2. Once you setup the loader, you can create an app and load data using the above Github loader
+```Python
+import os
+from embedchain.pipeline import Pipeline as App
+
+os.environ["OPENAI_API_KEY"] = "sk-xxxx"
+
+app = App()
+
+app.add("repo:embedchain/embedchain type:repo", data_type="github", loader=loader)
+
+response = app.query("What is Embedchain?")
+# Answer: Embedchain is a Data Platform for Large Language Models (LLMs). It allows users to seamlessly load, index, retrieve, and sync unstructured data in order to build dynamic, LLM-powered applications. There is also a JavaScript implementation called embedchain-js available on GitHub.
+```
+The `add` function of the app will accept any valid github query with qualifiers. It only supports loading github code, repository, issues and pull-requests.
+<Note>
+You must provide qualifiers `type:` and `repo:` in the query. The `type:` qualifier can be a combination of `code`, `repo`, `pr`, `issue`, `branch`, `file`. The `repo:` qualifier must be a valid github repository name.
+</Note>
+
+<Card title="Valid queries" icon="lightbulb" iconType="duotone" color="#ca8b04">
+    - `repo:embedchain/embedchain type:repo` - to load the repository
+    - `repo:embedchain/embedchain type:branch name:feature_test` - to load the branch of the repository
+    - `repo:embedchain/embedchain type:file path:README.md` - to load the specific file of the repository
+    - `repo:embedchain/embedchain type:issue,pr` - to load the issues and pull-requests of the repository
+    - `repo:embedchain/embedchain type:issue state:closed` - to load the closed issues of the repository
+</Card>
+
+3. We automatically create a chunker to chunk your GitHub data, however if you wish to provide your own chunker class. Here is how you can do that:
+```Python
+from embedchain.chunkers.common_chunker import CommonChunker
+from embedchain.config.add_config import ChunkerConfig
+
+github_chunker_config = ChunkerConfig(chunk_size=2000, chunk_overlap=0, length_function=len)
+github_chunker = CommonChunker(config=github_chunker_config)
+
+app.add(load_query, data_type="github", loader=loader, chunker=github_chunker)
+```

embedchain/docs/components/data-sources/gmail.mdx

@@ -0,0 +1,34 @@
+---
+title: '📬 Gmail'
+---
+
+To use GmailLoader you must install the extra dependencies with `pip install --upgrade embedchain[gmail]`.
+
+The `source` must be a valid Gmail search query, you can refer `https://support.google.com/mail/answer/7190?hl=en` to build a query.
+
+To load Gmail messages, you MUST use the data_type as `gmail`. Otherwise the source will be detected as simple `text`.
+
+To use this you need to save `credentials.json` in the directory from where you will run the loader. Follow these steps to get the credentials
+
+1. Go to the [Google Cloud Console](https://console.cloud.google.com/apis/credentials).
+2. Create a project if you don't have one already.
+3. Create an `OAuth Consent Screen` in the project. You may need to select the `external` option.
+4. Make sure the consent screen is published.
+5. Enable the [Gmail API](https://console.cloud.google.com/apis/api/gmail.googleapis.com)
+6. Create credentials from the `Credentials` tab.
+7. Select the type `OAuth Client ID`.
+8. Choose the application type `Web application`. As a name you can choose `embedchain` or any other name as per your use case.
+9. Add an authorized redirect URI for `http://localhost:8080/`.
+10. You can leave everything else at default, finish the creation.
+11. When you are done, a modal opens where you can download the details in `json` format.
+12. Put the `.json` file in your current directory and rename it to `credentials.json`
+
+```python
+from embedchain import App
+
+app = App()
+
+gmail_filter = "to: me label:inbox"
+app.add(gmail_filter, data_type="gmail")
+app.query("Summarize my email conversations")
+```

embedchain/docs/components/data-sources/google-drive.mdx

@@ -0,0 +1,28 @@
+---
+title: 'Google Drive'
+---
+
+To use GoogleDriveLoader you must install the extra dependencies with `pip install --upgrade embedchain[googledrive]`.
+
+The data_type must be `google_drive`. Otherwise, it will be considered a regular web page.
+
+Google Drive requires the setup of credentials. This can be done by following the steps below:
+
+1. Go to the [Google Cloud Console](https://console.cloud.google.com/apis/credentials).
+2. Create a project if you don't have one already.
+3. Enable the [Google Drive API](https://console.cloud.google.com/flows/enableapi?apiid=drive.googleapis.com)
+4. [Authorize credentials for desktop app](https://developers.google.com/drive/api/quickstart/python#authorize_credentials_for_a_desktop_application)
+5. When done, you will be able to download the credentials in `json` format. Rename the downloaded file to `credentials.json` and save it in `~/.credentials/credentials.json`
+6. Set the environment variable `GOOGLE_APPLICATION_CREDENTIALS=~/.credentials/credentials.json`
+
+The first time you use the loader, you will be prompted to enter your Google account credentials.
+
+
+```python
+from embedchain import App
+
+app = App()
+
+url = "https://drive.google.com/drive/u/0/folders/xxx-xxx"
+app.add(url, data_type="google_drive")
+```