Rod Boev
54 lines
3.7 KiB
Plaintext
54 lines
3.7 KiB
Plaintext
---
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title: Overview
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description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, and more."
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---
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Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
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## Supported Vector Databases
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See the list of supported vector databases below.
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<Note>
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The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, and an in-memory store.
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</Note>
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<CardGroup cols={3}>
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<Card title="Qdrant" icon="/images/provider-icons/qdrant.svg" href="/components/vectordbs/dbs/qdrant"></Card>
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<Card title="Chroma" icon="/images/provider-icons/chroma.svg" href="/components/vectordbs/dbs/chroma"></Card>
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<Card title="PGVector" icon="/images/provider-icons/postgresql.svg" href="/components/vectordbs/dbs/pgvector"></Card>
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<Card title="Upstash Vector" icon="/images/provider-icons/upstash.svg" href="/components/vectordbs/dbs/upstash-vector"></Card>
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<Card title="Milvus" icon="/images/provider-icons/milvus.svg" href="/components/vectordbs/dbs/milvus"></Card>
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<Card title="Pinecone" icon="/images/provider-icons/pinecone.svg" href="/components/vectordbs/dbs/pinecone"></Card>
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<Card title="MongoDB" icon="/images/provider-icons/mongodb.svg" href="/components/vectordbs/dbs/mongodb"></Card>
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<Card title="Azure" icon="/images/provider-icons/azure-color.svg" href="/components/vectordbs/dbs/azure"></Card>
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<Card title="Redis" icon="/images/provider-icons/redis.svg" href="/components/vectordbs/dbs/redis"></Card>
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<Card title="Valkey" icon="/images/provider-icons/valkey.svg" href="/components/vectordbs/dbs/valkey"></Card>
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<Card title="Elasticsearch" icon="/images/provider-icons/elasticsearch.svg" href="/components/vectordbs/dbs/elasticsearch"></Card>
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<Card title="OpenSearch" icon="/images/provider-icons/opensearch.svg" href="/components/vectordbs/dbs/opensearch"></Card>
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<Card title="Supabase" icon="/images/provider-icons/supabase.svg" href="/components/vectordbs/dbs/supabase"></Card>
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<Card title="Vertex AI" icon="/images/provider-icons/vertexai.svg" href="/components/vectordbs/dbs/vertex_ai"></Card>
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<Card title="Weaviate" icon="circle-nodes" href="/components/vectordbs/dbs/weaviate"></Card>
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<Card title="FAISS" icon="layer-group" href="/components/vectordbs/dbs/faiss"></Card>
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<Card title="LangChain" icon="/images/provider-icons/langchain-color.svg" href="/components/vectordbs/dbs/langchain"></Card>
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<Card title="Amazon S3 Vectors" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/s3_vectors"></Card>
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<Card title="Databricks" icon="/images/provider-icons/databricks.svg" href="/components/vectordbs/dbs/databricks"></Card>
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<Card title="Turbopuffer" icon="/images/provider-icons/turbopuffer.svg" href="/components/vectordbs/dbs/turbopuffer"></Card>
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</CardGroup>
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## Usage
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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.
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For a comprehensive list of available parameters for vector database configuration, please refer to [Config](./config).
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## Common issues
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### Using Model with Different Dimensions
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If you are using a customized model with different dimensions other than 1536 (for example, 768), you may encounter the following error:
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`ValueError: shapes (0,1536) and (768,) not aligned: 1536 (dim 1) != 768 (dim 0)`
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You can add `"embedding_model_dims": 768,` to the config of the vector_store to resolve this issue.
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