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
77 lines
2.3 KiB
Plaintext
77 lines
2.3 KiB
Plaintext
---
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title: "Upstash Vector"
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description: "Use Upstash Vector as a serverless vector database in Mem0 with optional built-in embedding models."
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---
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[Upstash Vector](https://upstash.com/docs/vector) is a serverless vector database with built-in embedding models.
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### Usage with Upstash embeddings
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You can enable the built-in embedding models by setting `enable_embeddings` to `True`. This allows you to use Upstash's embedding models for vectorization.
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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["UPSTASH_VECTOR_REST_URL"] = "..."
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os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."
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config = {
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"vector_store": {
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"provider": "upstash_vector",
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"config": {
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"enable_embeddings": True,
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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("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
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```
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<Note>
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Setting `enable_embeddings` to `True` will bypass any external embedding provider you have configured.
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</Note>
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### Usage with external embedding providers
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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["OPENAI_API_KEY"] = "..."
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os.environ["UPSTASH_VECTOR_REST_URL"] = "..."
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os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."
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config = {
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"vector_store": {
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"provider": "upstash_vector",
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},
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"embedder": {
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"provider": "openai",
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"config": {
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"model": "text-embedding-3-large"
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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("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
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```
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### Config
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Here are the parameters available for configuring Upstash Vector:
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| Parameter | Description | Default Value |
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| ------------------- | ---------------------------------- | ------------- |
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| `url` | URL for the Upstash Vector index | `None` |
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| `token` | Token for the Upstash Vector index | `None` |
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| `client` | An `upstash_vector.Index` instance | `None` |
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| `collection_name` | The default namespace used | `""` |
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| `enable_embeddings` | Whether to use Upstash embeddings | `False` |
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<Note>
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When `url` and `token` are not provided, the `UPSTASH_VECTOR_REST_URL` and
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`UPSTASH_VECTOR_REST_TOKEN` environment variables are used.
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</Note>
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