76 lines
1.9 KiB
Plaintext
76 lines
1.9 KiB
Plaintext
---
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title: Supported Vector Databases
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---
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## Overview
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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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<CardGroup>
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<Card title="Qdrant" href="#qdrant"></Card>
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<Card title="Chroma" href="#chroma"></Card>
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</CardGroup>
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## Qdrant
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[Qdrant](https://qdrant.tech/) is an open-source vector search engine. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data.
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To use Qdrant you can do like this:
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```python
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import os
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from mem0 import Memory
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config = {
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"vector_store": {
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"provider": "qdrant",
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"config": {
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"collection_name": "test",
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"host": "localhost",
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"port": 6333,
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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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## Chroma
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[Chroma](https://www.trychroma.com/) is an AI-native open-source vector database that simplifies building LLM apps by providing tools for storing, embedding, and searching embeddings with a focus on simplicity and speed.
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To use ChromaDB you can do like this:
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```python
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import os
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from mem0 import Memory
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config = {
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"vector_store": {
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"provider": "chroma",
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"config": {
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"collection_name": "test",
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"path": "db",
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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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## Common issues
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### Using model with different dimensions
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If you are using customized model, which is having different dimensions other than 1536
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for example 768, you may encounter below error:
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`ValueError: shapes (0,1536) and (768,) not aligned: 1536 (dim 1) != 768 (dim 0)`
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you could add `"embedding_model_dims": 768,` to the config of the vector_store to overcome this issue.
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