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
54 lines
2.8 KiB
Plaintext
54 lines
2.8 KiB
Plaintext
---
|
|
title: Overview
|
|
description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, 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.
|
|
|
|
## Supported Vector Databases
|
|
|
|
See the list of supported vector databases below.
|
|
|
|
<Note>
|
|
The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, and Vectorize.
|
|
</Note>
|
|
|
|
<CardGroup cols={3}>
|
|
<Card title="Qdrant" href="/components/vectordbs/dbs/qdrant"></Card>
|
|
<Card title="Chroma" href="/components/vectordbs/dbs/chroma"></Card>
|
|
<Card title="PGVector" href="/components/vectordbs/dbs/pgvector"></Card>
|
|
<Card title="Upstash Vector" href="/components/vectordbs/dbs/upstash-vector"></Card>
|
|
<Card title="Milvus" href="/components/vectordbs/dbs/milvus"></Card>
|
|
<Card title="Pinecone" href="/components/vectordbs/dbs/pinecone"></Card>
|
|
<Card title="MongoDB" href="/components/vectordbs/dbs/mongodb"></Card>
|
|
<Card title="Azure" href="/components/vectordbs/dbs/azure"></Card>
|
|
<Card title="Redis" href="/components/vectordbs/dbs/redis"></Card>
|
|
<Card title="Valkey" href="/components/vectordbs/dbs/valkey"></Card>
|
|
<Card title="Elasticsearch" href="/components/vectordbs/dbs/elasticsearch"></Card>
|
|
<Card title="OpenSearch" href="/components/vectordbs/dbs/opensearch"></Card>
|
|
<Card title="Supabase" href="/components/vectordbs/dbs/supabase"></Card>
|
|
<Card title="Vertex AI" href="/components/vectordbs/dbs/vertex_ai"></Card>
|
|
<Card title="Weaviate" href="/components/vectordbs/dbs/weaviate"></Card>
|
|
<Card title="FAISS" href="/components/vectordbs/dbs/faiss"></Card>
|
|
<Card title="LangChain" href="/components/vectordbs/dbs/langchain"></Card>
|
|
<Card title="Amazon S3 Vectors" href="/components/vectordbs/dbs/s3_vectors"></Card>
|
|
<Card title="Databricks" href="/components/vectordbs/dbs/databricks"></Card>
|
|
<Card title="Turbopuffer" href="/components/vectordbs/dbs/turbopuffer"></Card>
|
|
</CardGroup>
|
|
|
|
## Usage
|
|
|
|
To utilize a vector database, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `Qdrant` will be used as the vector database.
|
|
|
|
For a comprehensive list of available parameters for vector database configuration, please refer to [Config](./config).
|
|
|
|
## Common issues
|
|
|
|
### Using Model with Different Dimensions
|
|
|
|
If you are using a customized model with different dimensions other than 1536 (for example, 768), you may encounter the following error:
|
|
|
|
`ValueError: shapes (0,1536) and (768,) not aligned: 1536 (dim 1) != 768 (dim 0)`
|
|
|
|
You can add `"embedding_model_dims": 768,` to the config of the vector_store to resolve this issue.
|