kartik-mem0
fbb8e71224
Each fix verified against mem0/vector_stores/ source and configs:
- baidu: table_name default mem0_table → mem0 (BaiduDBConfig)
- langchain: TS import mem0ai → mem0ai/oss; fix undefined LangchainVectorStore
variable to use MemoryVectorStore directly
- mongodb: collection_name default mem0_collection → mem0; mongo_uri default
corrected to mongodb://localhost:27017 (MongoDBConfig)
- neon: hnsw default False → True (PGVectorConfig.hnsw = True)
- neptune_analytics: extra name vector_stores → vector-stores (pyproject.toml)
- pgvector: diskann default True → False; hnsw default False → True
(PGVectorConfig)
- upstash-vector: move enable_embeddings inside config: {} block
(UpstashVectorConfig field, not top-level VectorStoreConfig)
- valkey: remove non-existent distance_metric row (not in ValkeyConfig);
fix extra name vector_stores → vector-stores
- vertex_ai: region is required (no default in GoogleMatchingEngineConfig)
- weaviate: install pip install weaviate weaviate-client → weaviate-client
(weaviate is not a separate package)
- overview: update TS supported vector DBs list to reflect actual
mem0-ts/src/oss/src/vector_stores/ files
50 lines
2.0 KiB
Plaintext
50 lines
2.0 KiB
Plaintext
---
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title: Vertex AI Vector Search
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description: "Use Google Cloud Vertex AI Vector Search as a managed vector store in Mem0 with endpoint and index configuration."
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---
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### Usage
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To use Google Cloud Vertex AI Vector Search with `mem0`, you need to configure the `vector_store` in your `mem0` config:
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```python
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import os
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from mem0 import Memory
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os.environ["GOOGLE_API_KEY"] = "sk-xx"
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config = {
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"vector_store": {
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"provider": "vertex_ai_vector_search",
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"config": {
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"endpoint_id": "YOUR_ENDPOINT_ID", # Required: Vector Search endpoint ID
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"index_id": "YOUR_INDEX_ID", # Required: Vector Search index ID
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"deployment_index_id": "YOUR_DEPLOYMENT_INDEX_ID", # Required: Deployment-specific ID
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"project_id": "YOUR_PROJECT_ID", # Required: Google Cloud project ID
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"project_number": "YOUR_PROJECT_NUMBER", # Required: Google Cloud project number
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"region": "YOUR_REGION", # Required: Google Cloud region
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"credentials_path": "path/to/credentials.json", # Optional: Defaults to GOOGLE_APPLICATION_CREDENTIALS
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"vector_search_api_endpoint": "YOUR_API_ENDPOINT" # Required for get operations
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}
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}
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}
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m = Memory.from_config(config)
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m.add("Your text here", user_id="user", metadata={"category": "example"})
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```
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### Required Parameters
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| Parameter | Description | Required |
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|-----------|-------------|----------|
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| `endpoint_id` | Vector Search endpoint ID | Yes |
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| `index_id` | Vector Search index ID | Yes |
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| `deployment_index_id` | Deployment-specific index ID | Yes |
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| `project_id` | Google Cloud project ID | Yes |
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| `project_number` | Google Cloud project number | Yes |
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| `vector_search_api_endpoint` | Vector search API endpoint | Yes (for get operations) |
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| `region` | Google Cloud region | Yes |
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| `credentials_path` | Path to service account credentials | No (defaults to GOOGLE_APPLICATION_CREDENTIALS) |
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