59 lines
1.9 KiB
Plaintext
59 lines
1.9 KiB
Plaintext
## What is Config?
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Config in mem0 is a dictionary that specifies the settings for your embedding models. It allows you to customize the behavior and connection details of your chosen embedder.
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## How to Define Config
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The config is defined as a Python dictionary with two main keys:
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- `embedder`: Specifies the embedder provider and its configuration
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- `provider`: The name of the embedder (e.g., "openai", "ollama")
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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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"embedder": {
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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 embedding model to use.
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2. Providing necessary connection details (e.g., model, api_key, embedding_dims).
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3. Ensuring proper initialization and connection to your chosen embedder.
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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 embedders:
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| Parameter | Description |
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|-----------|-------------|
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| `model` | Embedding model to use |
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| `api_key` | API key of the provider |
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| `embedding_dims` | Dimensions of the embedding model |
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| `http_client_proxies` | Allow proxy server settings |
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| `ollama_base_url` | Base URL for the Ollama embedding model |
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| `model_kwargs` | Key-Value arguments for the Huggingface embedding model |
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## Supported Embedding Models
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For detailed information on configuring specific embedders, please visit the [Embedding Models](./models) section. There you'll find information for each supported embedder with provider-specific usage examples and configuration details.
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