75 lines
2.6 KiB
Plaintext
75 lines
2.6 KiB
Plaintext
---
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title: Configurations
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icon: "gear"
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iconType: "solid"
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---
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## How to define configurations?
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The `config` is defined as a Python dictionary with two main keys:
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- `vector_store`: Specifies the vector database provider and its configuration
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- `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus","azure_ai_search")
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- `config`: A nested dictionary containing provider-specific settings
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## How to Use Config
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Here's a general example of how to use the config with mem0:
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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": "your_chosen_provider",
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"config": {
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# Provider-specific settings go here
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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("Your text here", user_id="user", metadata={"category": "example"})
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```
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## Why is Config Needed?
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Config is essential for:
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1. Specifying which vector database to use.
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2. Providing necessary connection details (e.g., host, port, credentials).
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3. Customizing database-specific settings (e.g., collection name, path).
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4. Ensuring proper initialization and connection to your chosen vector store.
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## Master List of All Params in Config
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Here's a comprehensive list of all parameters that can be used across different vector databases:
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| Parameter | Description |
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|-----------|-------------|
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| `collection_name` | Name of the collection |
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| `embedding_model_dims` | Dimensions of the embedding model |
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| `client` | Custom client for the database |
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| `path` | Path for the database |
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| `host` | Host where the server is running |
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| `port` | Port where the server is running |
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| `user` | Username for database connection |
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| `password` | Password for database connection |
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| `dbname` | Name of the database |
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| `url` | Full URL for the server |
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| `api_key` | API key for the server |
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| `on_disk` | Enable persistent storage |
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## Customizing Config
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Each vector database has its own specific configuration requirements. To customize the config for your chosen vector store:
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1. Identify the vector database you want to use from [supported vector databases](./dbs).
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2. Refer to the `Config` section in the respective vector database's documentation.
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3. Include only the relevant parameters for your chosen database in the `config` dictionary.
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## Supported Vector Databases
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For detailed information on configuring specific vector databases, please visit the [Supported Vector Databases](./dbs) section. There you'll find individual pages for each supported vector store with provider-specific usage examples and configuration details.
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