Merge remote-tracking branch 'origin/main' into feat/zapier-mem0
This commit is contained in:
kartik-mem0
@@ -7,6 +7,18 @@ mode: "wide"
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<Tabs>
|
||||
<Tab title="Python">
|
||||
|
||||
<Update label="2026-07-25" description="v2.0.14">
|
||||
|
||||
**New Features:**
|
||||
- **Vector Stores:** Add an Oracle AI Vector Search provider (`oracledb`) with connection pooling, `HNSW`/`IVF` indexes, JSON metadata filtering, and six selectable distance metrics ([#5358](https://github.com/mem0ai/mem0/pull/5358))
|
||||
|
||||
**Bug Fixes:**
|
||||
- **Vector Stores:** Translate a `"*"` filter value in OpenSearch into an `exists` query for every key, not just identity keys. It was previously ignored or matched literally against the string `"*"`, so a wildcard filter returned nothing ([#6522](https://github.com/mem0ai/mem0/pull/6522))
|
||||
- **Vector Stores:** Re-raise errors from OpenSearch `search()` instead of returning `[]`, so a transport, auth, or index misconfiguration surfaces instead of looking like zero matches. `keyword_search()` still degrades on failure, since it is a best-effort BM25 signal ([#6519](https://github.com/mem0ai/mem0/pull/6519))
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||||
- **Vector Stores:** Guard the `text` field in Milvus `update()` behind the `_has_bm25_schema` check, matching `insert()`, so updating a memory in a collection without the BM25 `text`/`sparse` schema no longer fails ([#5705](https://github.com/mem0ai/mem0/pull/5705))
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026-07-22" description="v2.0.13">
|
||||
|
||||
**Bug Fixes:**
|
||||
@@ -1145,6 +1157,18 @@ See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-
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|
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<Tab title="TypeScript">
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||||
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||||
<Update label="2026-07-25" description="v3.1.2">
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||||
|
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**Bug Fixes:**
|
||||
- **Vector Stores:** Apply every operator in a Cassandra compound field filter (e.g. `{ age: { gte: 10, lte: 20 } }`) instead of stopping after the first, so the remaining bounds are no longer silently ignored ([#6511](https://github.com/mem0ai/mem0/pull/6511))
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||||
- **Vector Stores:** Stop the Chroma where-clause translator from dropping filter conditions. Same-field ranges (`gte` + `lte`), multi-field conditions inside `$or`, and negated `contains`/`icontains` under `$not` each collapsed to a single clause or vanished, widening the search instead of narrowing it ([#6521](https://github.com/mem0ai/mem0/pull/6521))
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- **Vector Stores:** Skip `"*"` wildcard filter values in Milvus instead of matching them literally, so a filter like `{ user_id: "*" }` no longer returns zero memories ([#6508](https://github.com/mem0ai/mem0/pull/6508))
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||||
- **Vector Stores:** Read `textLemmatized` for BM25 keyword search on Milvus, OpenSearch, and MongoDB, matching the field the memory layer actually writes, so hybrid search on those backends no longer loses the keyword signal ([#6497](https://github.com/mem0ai/mem0/pull/6497))
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- **LLMs:** Forward `responseFormat` to Gemini's `responseMimeType` in `generateResponse()`, so requesting `json_object` returns JSON instead of free-form text ([#6468](https://github.com/mem0ai/mem0/pull/6468))
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||||
- **LLMs:** Find the Anthropic text block by type instead of indexing `content[0]`, so a thinking-enabled model whose `thinking` block comes first no longer throws `Unexpected response type from Anthropic API` ([#6506](https://github.com/mem0ai/mem0/pull/6506))
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|
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</Update>
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<Update label="2026-07-22" description="v3.1.1">
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|
||||
**New Features:**
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|
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@@ -7,7 +7,7 @@ description: "Reference for vector database configuration options in Mem0, inclu
|
||||
|
||||
The `config` is defined as an object with two main keys:
|
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- `vector_store`: Specifies the vector database provider and its configuration
|
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- `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search", "valkey")
|
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- `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search", "valkey", "oracledb")
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- `config`: A nested dictionary containing provider-specific settings
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||||
|
||||
|
||||
@@ -95,6 +95,11 @@ Here's a comprehensive list of all parameters that can be used across different
|
||||
| `connection_string` | PostgreSQL connection string (for Supabase/PGVector) |
|
||||
| `index_method` | Vector index method (for Supabase) |
|
||||
| `index_measure` | Distance measure for similarity search (for Supabase) |
|
||||
| `connection_params` | Connection settings for Oracle AI Vector Search |
|
||||
| `use_connection_pool` | Create an Oracle connection pool from `connection_params` |
|
||||
| `distance_metric` | Distance metric for Oracle vector indexing and search |
|
||||
| `index_type` | Oracle vector index type: `HNSW` or `IVF` |
|
||||
| `index_parameters` | Oracle vector-index parameters for the selected index type |
|
||||
</Tab>
|
||||
<Tab title="TypeScript">
|
||||
| Parameter | Description |
|
||||
|
||||
@@ -0,0 +1,134 @@
|
||||
---
|
||||
title: "Oracle AI Vector Search"
|
||||
description: "Use Oracle Database AI Vector Search as a vector store in Mem0 for semantic and relational queries."
|
||||
---
|
||||
|
||||
[Oracle AI Vector Search](https://www.oracle.com/database/ai-vector-search/) stores embeddings in an Oracle table using the native `VECTOR` data type, so you can combine semantic search over unstructured data with relational queries over business data in a single database.
|
||||
|
||||
### Requirements
|
||||
|
||||
- Oracle Database 23.4 or later, with a user that can create tables and vector indexes
|
||||
- The `python-oracledb` driver. In thick mode, Oracle Client 23.4 or later is also required.
|
||||
|
||||
```bash
|
||||
pip install oracledb
|
||||
```
|
||||
|
||||
### Usage
|
||||
|
||||
<CodeGroup>
|
||||
```python Python
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import os
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from mem0 import Memory
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|
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os.environ["OPENAI_API_KEY"] = "sk-xx"
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|
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config = {
|
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"vector_store": {
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"provider": "oracledb",
|
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"config": {
|
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"collection_name": "mem0",
|
||||
"embedding_model_dims": 1536,
|
||||
"connection_params": {
|
||||
"user": "mem0_user",
|
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"password": "your-password",
|
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"dsn": "localhost:1521/FREEPDB1",
|
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},
|
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}
|
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}
|
||||
}
|
||||
|
||||
m = Memory.from_config(config)
|
||||
messages = [
|
||||
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
|
||||
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
|
||||
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
|
||||
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
|
||||
]
|
||||
m.add(messages, user_id="alice", metadata={"category": "movies"})
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
To reuse a connection or pool you already manage, pass it as `client` instead of `connection_params`:
|
||||
|
||||
```python
|
||||
import oracledb
|
||||
|
||||
pool = oracledb.create_pool(user="mem0_user", password="your-password", dsn="localhost:1521/FREEPDB1")
|
||||
|
||||
config = {
|
||||
"vector_store": {
|
||||
"provider": "oracledb",
|
||||
"config": {"client": pool},
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Config
|
||||
|
||||
Here are the parameters available for configuring Oracle AI Vector Search:
|
||||
|
||||
| Parameter | Description | Default Value |
|
||||
| --- | --- | --- |
|
||||
| `connection_params` | Connection settings passed to `python-oracledb`, such as `user`, `password` and `dsn`. See the [connection handling guide](https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html). | `None` |
|
||||
| `use_connection_pool` | Create a connection pool from `connection_params` instead of a single connection | `True` |
|
||||
| `client` | An existing `oracledb.Connection` or `oracledb.ConnectionPool` to use instead of building one from `connection_params` | `None` |
|
||||
| `collection_name` | Name of the Oracle table that stores vectors and payloads | `mem0` |
|
||||
| `embedding_model_dims` | Dimension of your embedding vectors, must be greater than 0 | `1536` |
|
||||
| `distance_metric` | Distance function used for indexing and search: `COSINE`, `EUCLIDEAN`, `EUCLIDEAN_SQUARED`, `DOT`, `HAMMING` or `MANHATTAN` | `COSINE` |
|
||||
| `do_create_index` | Whether to create a vector index on the collection | `True` |
|
||||
| `index_type` | Vector index type: `HNSW` or `IVF` | `HNSW` |
|
||||
| `index_name` | Name of the vector index | `<collection_name>_VEC_IDX` |
|
||||
| `index_parameters` | Index tuning parameters. For `HNSW`: `neighbors`, `efconstruction`. For `IVF`: `neighbor partitions`, `samples_per_partition`, `min_vectors_per_partition`. | `None` |
|
||||
| `index_accuracy` | Target index accuracy from 1 to 100, applied as `WITH TARGET ACCURACY <n>` | `None` |
|
||||
|
||||
<Note>
|
||||
When you pass a pre-built `client`, Mem0 uses it as-is and ignores `connection_params` and `use_connection_pool`. Mem0 does not close a client it did not create.
|
||||
</Note>
|
||||
|
||||
### Vector indexes
|
||||
|
||||
Set the index type with `index_type` and tune it with `index_parameters`:
|
||||
|
||||
```python
|
||||
config = {
|
||||
"vector_store": {
|
||||
"provider": "oracledb",
|
||||
"config": {
|
||||
"connection_params": {"user": "mem0_user", "password": "your-password", "dsn": "localhost:1521/FREEPDB1"},
|
||||
"index_type": "HNSW",
|
||||
"index_parameters": {"neighbors": 32, "efconstruction": 200},
|
||||
"index_accuracy": 95,
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
For the full list of supported options, see the Oracle [`CREATE VECTOR INDEX`](https://docs.oracle.com/en/database/oracle/oracle-database/26/sqlrf/create-vector-index.html) reference.
|
||||
|
||||
### Search scores
|
||||
|
||||
Oracle returns a distance from `VECTOR_DISTANCE`, which Mem0 converts to a `score` where higher means more similar. `COSINE` and the other non-negative metrics produce scores in the range `[0, 1]`. `DOT` returns the inner product, which can fall outside that range.
|
||||
|
||||
### Metadata filters
|
||||
|
||||
Filters run against the JSON `payload` column and support:
|
||||
|
||||
| Filter type | Examples |
|
||||
| --- | --- |
|
||||
| Scalar equality | `{"user_id": "alice"}` |
|
||||
| Field existence | `{"agent_id": "*"}` |
|
||||
| Comparison | `{"score": {"gte": 0.5}}`, also `eq`, `ne`, `gt`, `lt`, `lte` |
|
||||
| Membership | `{"category": {"in": ["movies", "books"]}}`, also `nin` |
|
||||
| String matching | `{"title": {"contains": "sci-fi"}}`, also `icontains` for case-insensitive |
|
||||
| Logical groups | `{"AND": [...]}`, `{"OR": [...]}`, `{"NOT": [...]}` |
|
||||
|
||||
Multiple fields at the top level are combined with `AND`:
|
||||
|
||||
```python
|
||||
m.search(
|
||||
"movie recommendations",
|
||||
user_id="alice",
|
||||
filters={"category": {"in": ["movies", "books"]}, "rating": {"gte": 4}},
|
||||
)
|
||||
```
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
title: Overview
|
||||
description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, and more."
|
||||
description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, Oracle, and more."
|
||||
---
|
||||
|
||||
Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
|
||||
@@ -21,6 +21,7 @@ See the list of supported vector databases below.
|
||||
<Card title="Milvus" icon="/images/provider-icons/milvus.svg" href="/components/vectordbs/dbs/milvus"></Card>
|
||||
<Card title="Pinecone" icon="/images/provider-icons/pinecone.svg" href="/components/vectordbs/dbs/pinecone"></Card>
|
||||
<Card title="MongoDB" icon="/images/provider-icons/mongodb.svg" href="/components/vectordbs/dbs/mongodb"></Card>
|
||||
<Card title="Oracle AI Vector Search" icon="/images/provider-icons/oracle.svg" href="/components/vectordbs/dbs/oracledb"></Card>
|
||||
<Card title="Azure" icon="/images/provider-icons/azure-color.svg" href="/components/vectordbs/dbs/azure"></Card>
|
||||
<Card title="Redis" icon="/images/provider-icons/redis.svg" href="/components/vectordbs/dbs/redis"></Card>
|
||||
<Card title="Valkey" icon="/images/provider-icons/valkey.svg" href="/components/vectordbs/dbs/valkey"></Card>
|
||||
|
||||
+6
-1
@@ -207,6 +207,7 @@
|
||||
"components/vectordbs/dbs/milvus",
|
||||
"components/vectordbs/dbs/pinecone",
|
||||
"components/vectordbs/dbs/mongodb",
|
||||
"components/vectordbs/dbs/oracledb",
|
||||
"components/vectordbs/dbs/azure",
|
||||
"components/vectordbs/dbs/azure_mysql",
|
||||
"components/vectordbs/dbs/redis",
|
||||
@@ -634,7 +635,7 @@
|
||||
},
|
||||
{
|
||||
"source": "/platform/features/expiration-date",
|
||||
"destination": "/"
|
||||
"destination": "/platform/features/memory-expiration"
|
||||
},
|
||||
{
|
||||
"source": "/cookbooks/essentials/memory-expiration-short-and-long-term",
|
||||
@@ -1235,6 +1236,10 @@
|
||||
{
|
||||
"source": "/platform/features/criteria-retrieval",
|
||||
"destination": "/platform/features/advanced-retrieval"
|
||||
},
|
||||
{
|
||||
"source": "/integrations/keywords",
|
||||
"destination": "/integrations/respan"
|
||||
}
|
||||
]
|
||||
}
|
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|
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@@ -0,0 +1 @@
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||||
<svg fill="#8F74E0" role="img" viewBox="0 0 93.9 59.4" xmlns="http://www.w3.org/2000/svg"><title>Oracle</title><path d="M30.5,59.4H65c16.4-0.4,29.3-14.1,28.9-30.4C93.5,13.1,80.7,0.4,65,0H30.5C14.1-0.4,0.4,12.5,0,28.9s12.5,30,28.9,30.4C29.4,59.4,29.9,59.4,30.5,59.4 M64.2,48.9h-33c-10.6-0.3-18.9-9.2-18.6-19.8C13,19,21.1,10.8,31.2,10.5h33c10.6-0.3,19.5,8,19.8,18.6c0.3,10.6-8,19.5-18.6,19.8C65,48.9,64.6,48.9,64.2,48.9"/></svg>
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||||
|
After Width: | Height: | Size: 427 B |
+1
-1
@@ -417,7 +417,6 @@ Editor-specific setup docs (already listed above under `## Integrations > AI Cod
|
||||
### MCP Endpoints
|
||||
|
||||
- Hosted MCP server: `https://mcp.mem0.ai` - requires Platform API key. See `platform/mem0-mcp`.
|
||||
- Self-hosted MCP server: ships with `openmemory/api/` (FastAPI) - runs against your own Qdrant + LLM stack.
|
||||
|
||||
## Community & Support
|
||||
|
||||
@@ -473,6 +472,7 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
|
||||
- [Milvus](https://docs.mem0.ai/components/vectordbs/dbs/milvus) [OSS]: Use for large-scale Milvus deployments.
|
||||
- [Pinecone](https://docs.mem0.ai/components/vectordbs/dbs/pinecone) [OSS]: Use when the user is on Pinecone managed.
|
||||
- [MongoDB](https://docs.mem0.ai/components/vectordbs/dbs/mongodb) [OSS]: Use when Mongo Atlas Vector Search is the backing store.
|
||||
- [Oracle AI Vector Search](https://docs.mem0.ai/components/vectordbs/dbs/oracledb) [OSS]: Use when Oracle Database AI Vector Search is the backing store.
|
||||
- [Azure AI Search](https://docs.mem0.ai/components/vectordbs/dbs/azure) [OSS]: Use when the user is on Azure AI Search.
|
||||
- [Azure MySQL](https://docs.mem0.ai/components/vectordbs/dbs/azure_mysql) [OSS]: Use when vector search runs on Azure Database for MySQL.
|
||||
- [Redis](https://docs.mem0.ai/components/vectordbs/dbs/redis) [OSS]: Use when Redis Stack is the backing store.
|
||||
|
||||
@@ -1,19 +0,0 @@
|
||||
<svg width="307" height="307" viewBox="0 0 307 307" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
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Before Width: | Height: | Size: 13 KiB |
@@ -1,9 +1,10 @@
|
||||
---
|
||||
title: Memory Expiration
|
||||
description: "Give a memory a shelf life: set an expiration date and it stops surfacing in search once that date passes, without deleting the record."
|
||||
title: "Memory Expiration in Mem0"
|
||||
sidebarTitle: "Memory Expiration"
|
||||
description: "Set an expiration date on a Mem0 memory and it stops surfacing in search once that date passes. Nothing is deleted. Works on Platform and Open Source."
|
||||
---
|
||||
|
||||
# Memory Expiration
|
||||
## Why Use Memory Expiration?
|
||||
|
||||
Some facts are only true for a while. A trial plan ends, a seasonal preference goes stale, a support ticket ages past its retention window. Set an `expiration_date` on a memory and Mem0 stops surfacing it once that date passes, so you don't need a cleanup job hunting for rows to delete.
|
||||
|
||||
@@ -18,7 +19,7 @@ Some facts are only true for a while. A trial plan ends, a seasonal preference g
|
||||
- **No expiration date means never expires.** That is the default for every memory.
|
||||
- **Malformed dates fail open**: a stored value Mem0 can't parse is treated as *not* expired. A bad date never makes a memory silently vanish.
|
||||
|
||||
## Set an expiration date
|
||||
## How do you set an expiration date on a memory?
|
||||
|
||||
Set it when you add the memory:
|
||||
|
||||
@@ -97,7 +98,7 @@ The same parameter and spelling work on the OSS `Memory` class. See <Link href="
|
||||
Expired memories are dropped *before* your `top_k` is applied, so Mem0 widens the internal candidate pool first and short result sets are rare. They are not impossible: if nearly every memory in a scope has expired, a call can still return fewer than `top_k` results. Pass `show_expired: true` to get the full set back.
|
||||
</Note>
|
||||
|
||||
## Clear an expiration date
|
||||
## How do you clear or remove an expiration date?
|
||||
|
||||
Pass an explicit `None` (Python) or `null` (TypeScript) to make the memory permanent again. The SDKs deliberately preserve that null instead of treating it as "argument not supplied".
|
||||
|
||||
|
||||
Reference in New Issue
Block a user