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65 lines
2.1 KiB
Plaintext
65 lines
2.1 KiB
Plaintext
---
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title: Ollama
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description: "Configure Ollama as an LLM provider in Mem0 for running local language models with tool-calling support."
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---
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You can use LLMs from Ollama to run Mem0 locally. These [models](https://ollama.com/search?c=tools) support tool calling.
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## Usage
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<CodeGroup>
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```python Python
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import os
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from mem0 import Memory
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os.environ["OPENAI_API_KEY"] = "your-api-key" # for embedder
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config = {
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"llm": {
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"provider": "ollama",
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"config": {
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"model": "mixtral:8x7b",
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"temperature": 0.1,
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"max_tokens": 2000,
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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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```typescript TypeScript
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import { Memory } from 'mem0ai/oss';
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const config = {
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llm: {
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provider: 'ollama',
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config: {
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model: 'llama3.1:8b', // or any other Ollama model
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url: 'http://localhost:11434', // Ollama server URL
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temperature: 0.1,
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},
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},
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};
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const memory = new Memory(config);
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const 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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await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
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```
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</CodeGroup>
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## Config
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All available parameters for the `ollama` config are present in [Master List of All Params in Config](../config). |