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git clone gitlawb://did:key:z6Mkq5mY...iFZ5/my-project-publ...git clone gitlawb://did:key:z6Mkq5mY.../my-project-publ...2fa351d6docs: add automaton and perps launch sources16d ago| #1 | --- |
| #2 | title: Overview |
| #3 | --- |
| #4 | |
| #5 | Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs. |
| #6 | |
| #7 | ## Supported Vector Databases |
| #8 | |
| #9 | See the list of supported vector databases below. |
| #10 | |
| #11 | <Note> |
| #12 | The following vector databases are supported in the Python implementation. The TypeScript implementation currently only supports Qdrant, Redis, Valkey, Vectorize and in-memory vector database. |
| #13 | </Note> |
| #14 | |
| #15 | <CardGroup cols={3}> |
| #16 | <Card title="Qdrant" href="/components/vectordbs/dbs/qdrant"></Card> |
| #17 | <Card title="Chroma" href="/components/vectordbs/dbs/chroma"></Card> |
| #18 | <Card title="PGVector" href="/components/vectordbs/dbs/pgvector"></Card> |
| #19 | <Card title="Upstash Vector" href="/components/vectordbs/dbs/upstash-vector"></Card> |
| #20 | <Card title="Milvus" href="/components/vectordbs/dbs/milvus"></Card> |
| #21 | <Card title="Pinecone" href="/components/vectordbs/dbs/pinecone"></Card> |
| #22 | <Card title="MongoDB" href="/components/vectordbs/dbs/mongodb"></Card> |
| #23 | <Card title="Azure" href="/components/vectordbs/dbs/azure"></Card> |
| #24 | <Card title="Redis" href="/components/vectordbs/dbs/redis"></Card> |
| #25 | <Card title="Valkey" href="/components/vectordbs/dbs/valkey"></Card> |
| #26 | <Card title="Elasticsearch" href="/components/vectordbs/dbs/elasticsearch"></Card> |
| #27 | <Card title="OpenSearch" href="/components/vectordbs/dbs/opensearch"></Card> |
| #28 | <Card title="Supabase" href="/components/vectordbs/dbs/supabase"></Card> |
| #29 | <Card title="Vertex AI" href="/components/vectordbs/dbs/vertex_ai"></Card> |
| #30 | <Card title="Weaviate" href="/components/vectordbs/dbs/weaviate"></Card> |
| #31 | <Card title="FAISS" href="/components/vectordbs/dbs/faiss"></Card> |
| #32 | <Card title="LangChain" href="/components/vectordbs/dbs/langchain"></Card> |
| #33 | <Card title="Amazon S3 Vectors" href="/components/vectordbs/dbs/s3_vectors"></Card> |
| #34 | <Card title="Databricks" href="/components/vectordbs/dbs/databricks"></Card> |
| #35 | </CardGroup> |
| #36 | |
| #37 | ## Usage |
| #38 | |
| #39 | 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. |
| #40 | |
| #41 | For a comprehensive list of available parameters for vector database configuration, please refer to [Config](./config). |
| #42 | |
| #43 | ## Common issues |
| #44 | |
| #45 | ### Using Model with Different Dimensions |
| #46 | |
| #47 | If you are using a customized model with different dimensions other than 1536 (for example, 768), you may encounter the following error: |
| #48 | |
| #49 | `ValueError: shapes (0,1536) and (768,) not aligned: 1536 (dim 1) != 768 (dim 0)` |
| #50 | |
| #51 | You can add `"embedding_model_dims": 768,` to the config of the vector_store to resolve this issue. |
| #52 |