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[docs] API References and search Doc fix (#3712)

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Parth Sharma
2025-11-04 13:53:37 -08:00
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parent 978babd3db
commit 76abd5117d
4 changed files with 66 additions and 35 deletions
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docs/core-concepts/memory-operations/search.mdx
+59 -27
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@@ -7,7 +7,7 @@ iconType: "solid"
# How Mem0 Searches Memory
Mem0’s search operation lets agents ask natural-language questions and get back the memories that matter most. It’s the bridge between everything you’ve stored and the next response your agent writes.
Mem0's search operation lets agents ask natural-language questions and get back the memories that matter most. Like a smart librarian, it finds exactly what you need from everything you've stored.
<Info>
**Why it matters**
@@ -46,7 +46,39 @@ Formatted memories (with metadata and timestamps) return to your agent or callin
This pipeline runs the same way for the hosted Platform API and the OSS SDK.
---
## How does it work?
Search converts your natural language question into a vector embedding, then finds memories with similar embeddings in your database. The results are ranked by similarity score and can be further refined with filters or reranking.
```python
# Minimal example that shows the concept in action
# Platform API
client.search("What are Alice's hobbies?", filters={"user_id": "alice"})
# OSS
m.search("What are Alice's hobbies?", user_id="alice")
```
<Tip>
Always provide at least a `user_id` filter to scope searches to the right user's memories. This prevents cross-contamination between users.
</Tip>
## When should you use it?
- **Context retrieval** - When your agent needs past context to generate better responses
- **Personalization** - To recall user preferences, history, or past interactions
- **Fact checking** - To verify information against stored memories before responding
- **Decision support** - When agents need relevant background information to make decisions
## Platform vs OSS usage
| Capability | Mem0 Platform | Mem0 OSS |
| --- | --- | --- |
| **user_id usage** | In `filters={"user_id": "alice"}` for search/get_all | As parameter `user_id="alice"` for all operations |
| **Filter syntax** | Logical operators (`AND`, `OR`, comparisons) with field-level access | Basic field filters, extend via Python hooks |
| **Reranking** | Toggle `rerank=True` with managed reranker catalog | Requires configuring local or third-party rerankers |
| **Thresholds** | Request-level configuration (`threshold`, `top_k`) | Controlled via SDK parameters |
| **Response metadata** | Includes confidence scores, timestamps, dashboard visibility | Determined by your storage backend |
## Search with Mem0 Platform
@@ -85,8 +117,6 @@ const results = await client.search(query, {
```
</CodeGroup>
---
## Search with Mem0 Open Source
<CodeGroup>
@@ -122,8 +152,6 @@ const memories = memory.search("food preferences", {
```
</CodeGroup>
---
<Info icon="check">
Expect an array of memory documents. Platform responses include vectors, metadata, and timestamps; OSS returns your stored schema.
</Info>
@@ -133,6 +161,20 @@ const memories = memory.search("food preferences", {
Filters help narrow down search results. Common use cases:
**Filter by Session Context:**
*Platform API:*
```python
# Get memories from a specific agent session
client.search("query", filters={
"AND": [
{"user_id": "alice"},
{"agent_id": "chatbot"},
{"run_id": "session-123"}
]
})
```
*OSS:*
```python
# Get memories from a specific agent session
m.search("query", user_id="alice", agent_id="chatbot", run_id="session-123")
@@ -160,32 +202,22 @@ client.search("preferences", filters={
})
```
---
## Tips for better search
- **Use natural language**: Mem0 understands intent, so describe what you're looking for naturally
- **Scope with session IDs**: Always provide at least `user_id` to scope search to relevant memories
- **Scope with user ID**: Always provide `user_id` to scope search to relevant memories
- **Platform API**: Use `filters={"user_id": "alice"}`
- **OSS**: Use `user_id="alice"` as parameter
- **Combine filters**: Use AND/OR logic to create precise queries (Platform)
- **Consider wildcard filters**: Use wildcard filters (e.g., `run_id: "*"`) for broader matches
- **Tune parameters**: Adjust `top_k` for result count, `threshold` for relevance cutoff
- **Enable reranking**: Use `rerank=True` (default) when you have a reranker configured
### More Details
For the full list of filter logic, comparison operators, and optional search parameters, see the
[Search Memory API Reference](/api-reference/memory/search-memories).
## Managed vs OSS differences
| Capability | Mem0 Platform | Mem0 OSS |
| --- | --- | --- |
| Filters | Logical operators (`AND`, `OR`, comparisons) with field-level access | Basic field filters, extend via Python hooks |
| Reranking | Toggle `rerank=True` with managed reranker catalog | Requires configuring local or third-party rerankers |
| Thresholds | Request-level configuration (`threshold`, `top_k`) | Controlled via SDK parameters |
| Response metadata | Includes confidence scores, timestamps, dashboard visibility | Determined by your storage backend |
## Put it into practice
- Revisit the <Link href="/core-concepts/memory-operations/add">Add Memory</Link> guide to ensure you capture the context you expect to retrieve.
@@ -200,15 +232,15 @@ For the full list of filter logic, comparison operators, and optional search par
<CardGroup cols={2}>
<Card
title="Tune Update Concepts"
description="Learn how to adjust memories after search results come back."
icon="pencil"
href="/core-concepts/memory-operations/update"
title="Search Memory API"
description="Complete API reference with all filter operators and parameters."
icon="book"
href="/api-reference/memory/search-memories"
/>
<Card
title="Build Hybrid Search"
description="Follow the cookbook to combine Mem0 with external search."
title="Support Inbox Cookbook"
description="Build a complete support system with scoped search and reranking."
icon="rocket"
href="/cookbooks/integrations/tavily-search"
href="/cookbooks/operations/support-inbox"
/>
</CardGroup>
</CardGroup>