Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>
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---
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title: MiniMax
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description: "Configure MiniMax as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
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---
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To use MiniMax LLM models, you have to set the `MINIMAX_API_KEY` environment variable. You can also optionally set `MINIMAX_API_BASE` if you need to use a different API endpoint (defaults to "https://api.minimax.io/v1").
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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["MINIMAX_API_KEY"] = "your-api-key"
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os.environ["OPENAI_API_KEY"] = "your-api-key" # for embedder model
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config = {
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"llm": {
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"provider": "minimax",
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"config": {
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"model": "MiniMax-M2.7", # default model
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"temperature": 0.2,
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"max_tokens": 2000,
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"top_p": 1.0
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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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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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m.add(messages, user_id="alice", metadata={"category": "movies"})
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```
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You can also configure the API base URL in the config:
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```python
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config = {
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"llm": {
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"provider": "minimax",
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"config": {
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"model": "MiniMax-M2.7",
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"minimax_base_url": "https://your-custom-endpoint.com",
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"api_key": "your-api-key" # alternatively to using environment variable
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}
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}
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}
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```
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## Config
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All available parameters for the `minimax` config are present in [Master List of All Params in Config](../config).
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