fix: update skills and docs (#4868)
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Kartik
@@ -5,7 +5,7 @@ Additional platform capabilities beyond core CRUD operations.
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## Table of Contents
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- [Advanced Retrieval](#advanced-retrieval)
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- [Graph Memory](#graph-memory)
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- [Entity Linking](#entity-linking)
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- [Custom Categories](#custom-categories)
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- [Custom Instructions](#custom-instructions)
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- [Criteria Retrieval](#criteria-retrieval)
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@@ -18,124 +18,58 @@ Additional platform capabilities beyond core CRUD operations.
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## Advanced Retrieval
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Three enhancement options for tuning search precision, recall, and latency.
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### Hybrid Search (v3 Default)
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### Keyword Search (`keyword_search=True`)
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v3 uses multi-signal hybrid search combining:
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- **Semantic search** (vector similarity)
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- **BM25 keyword search** (normalized term matching)
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- **Entity matching** (entity graph boost)
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Expands results to include memories with specific terms, names, and technical keywords.
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- Latency: +10ms
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- Recall: Significantly increased
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- Best for: entity-heavy queries, comprehensive coverage
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This is automatic — no configuration needed.
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### Reranking (`rerank=True`)
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Deep semantic reordering of results — most relevant first.
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- Latency: +150-200ms
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- Accuracy: Significantly improved
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- Default: `False` (was `True` in v2)
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- Best for: user-facing results, top-N precision
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### Filter Memories (`filter_memories=True`)
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Precision filtering — removes low-relevance results entirely.
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- Latency: +200-300ms
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- Precision: Maximized
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- Best for: safety-critical applications, production systems
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### Recommended Combinations
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**Python:**
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```python
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# Fast & broad
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results = client.search(query, keyword_search=True, user_id="user123")
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# Balanced (recommended for most apps)
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results = client.search(query, keyword_search=True, rerank=True, user_id="user123")
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# High precision (critical apps)
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results = client.search(query, rerank=True, filter_memories=True, user_id="user123")
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results = client.search(query, filters={"user_id": "user123"}, rerank=True)
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```
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**TypeScript:**
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```typescript
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const results = await client.search(query, {
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user_id: 'user123',
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keyword_search: true,
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filters: { user_id: 'user123' },
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rerank: true,
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});
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```
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---
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## Graph Memory
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## Entity Linking
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Entity-level knowledge graph that creates relationships between memories.
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v3 replaces graph memory with built-in entity linking. Entities (proper nouns, quoted text, compound noun phrases) are automatically extracted and linked across memories.
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### How It Works
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1. **Extraction**: LLM analyzes conversation and identifies entities and relationships
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2. **Storage**: Embeddings go to vector store; entity nodes and edges go to graph store
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3. **Retrieval**: Vector search returns semantic matches; graph relations are appended to results
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1. **Extraction**: During `add()`, entities are automatically extracted from memory text
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2. **Storage**: Entities are stored in a parallel collection (`{collection}_entities`)
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3. **Retrieval**: During `search()`, query entities are matched and used to boost relevant memories
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Graph relations **augment** vector results without reordering them. Vector similarity always determines hit sequence.
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Entity linking is automatic — no configuration required. The boost is folded into the combined `score` on each result.
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### Enabling Graph Memory
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### v2 Migration Note
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**Per request:**
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```python
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client.add(messages, user_id="alice", enable_graph=True)
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client.search("query", user_id="alice", enable_graph=True)
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client.get_all(filters={"AND": [{"user_id": "alice"}]}, enable_graph=True)
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```
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If you were using `enable_graph=True` in v2:
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- Remove `enable_graph` from all API calls
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- Remove `graph_store` from OSS configuration
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- Entity relationships are now consumed through retrieval ranking, not exposed as a separate `relations` array
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**Project-level (default for all operations):**
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```python
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client.project.update(enable_graph=True)
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```
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```javascript
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await client.updateProject({ enable_graph: true });
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```
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### Relation Structure
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Each relation in the response contains:
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| Field | Type | Description |
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|-------|------|-------------|
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| `source` | string | Source entity name |
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| `source_type` | string | Source entity type (e.g., "Person") |
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| `relationship` | string | Relationship label (e.g., "lives_in") |
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| `target` | string | Target entity name |
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| `target_type` | string | Target entity type (e.g., "City") |
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| `score` | number | Confidence score |
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**Example:**
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```json
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{
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"relations": [
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{
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"source": "Joseph",
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"source_type": "Person",
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"relationship": "lives_in",
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"target": "Seattle",
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"target_type": "City",
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"score": 0.92
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}
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]
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}
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```
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### Technical Notes
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- Graph Memory adds processing time; see docs for current plan availability
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- Works optimally with rich conversation histories containing entity relationships
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- Best suited for long-running assistants tracking evolving information
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- Graph writes and reads toggle independently per request
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- Multi-agent context supported via `user_id`, `agent_id`, `run_id` scoping
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- Add operations are asynchronous; graph metadata may not be immediately available
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See the [v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for details.
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---
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@@ -160,7 +94,7 @@ client.project.update(custom_categories=new_categories)
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```
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```javascript
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await client.updateProject({ custom_categories: new_categories });
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await client.updateProject({ customCategories: newCategories });
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```
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**Retrieve active categories:**
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@@ -185,7 +119,7 @@ client.project.update(custom_instructions="Your guidelines here...")
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```
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```javascript
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await client.updateProject({ custom_instructions: "Your guidelines here..." });
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await client.updateProject({ customInstructions: "Your guidelines here..." });
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```
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### Template Structure
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@@ -229,7 +163,7 @@ client.project.update(retrieval_criteria=retrieval_criteria)
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```typescript
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await client.updateProject({
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retrieval_criteria: [
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retrievalCriteria: [
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{ name: 'joy', description: 'Positive emotions', weight: 3 },
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{ name: 'urgency', description: 'Time-sensitive items', weight: 4 },
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],
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@@ -281,7 +215,7 @@ for item in feedback_data:
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```typescript
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await client.feedback('mem-123', {
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feedback: 'POSITIVE',
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feedback_reason: 'Accurately captured dietary preference',
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feedbackReason: 'Accurately captured dietary preference',
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});
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```
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