41 lines
1.4 KiB
Plaintext
41 lines
1.4 KiB
Plaintext
[pgvector](https://github.com/pgvector/pgvector) is open-source vector similarity search for Postgres. After connecting with postgres run `CREATE EXTENSION IF NOT EXISTS vector;` to create the vector extension.
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### Usage
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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"] = "sk-xx"
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config = {
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"vector_store": {
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"provider": "pgvector",
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"config": {
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"user": "test",
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"password": "123",
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"host": "127.0.0.1",
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"port": "5432",
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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's the parameters available for configuring pgvector:
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `dbname` | The name of the | `postgres` |
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| `collection_name` | The name of the collection | `mem0` |
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| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
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| `user` | User name to connect to the database | `None` |
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| `password` | Password to connect to the database | `None` |
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| `host` | The host where the Postgres server is running | `None` |
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| `port` | The port where the Postgres server is running | `None` |
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| `diskann` | Whether to use diskann for vector similarity search (requires pgvectorscale) | `True` |
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| `hnsw` | Whether to use hnsw for vector similarity search | `False` | |