09451401cc
- Updated vector store provider references throughout documentation - Changed default vector store from Qdrant to Supabase (pgvector) - Updated configuration examples to use Supabase connection strings - Modified navigation structure to remove qdrant-specific references - Updated examples in mem0-with-ollama and llama-index integration - Corrected API reference and architecture documentation 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
74 lines
2.5 KiB
Plaintext
74 lines
2.5 KiB
Plaintext
---
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title: Mem0 with Ollama
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---
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<Snippet file="blank-notif.mdx" />
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## Running Mem0 Locally with Ollama
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Mem0 can be utilized entirely locally by leveraging Ollama for both the embedding model and the language model (LLM). This guide will walk you through the necessary steps and provide the complete code to get you started.
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### Overview
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By using Ollama, you can run Mem0 locally, which allows for greater control over your data and models. This setup uses Ollama for both the embedding model and the language model, providing a fully local solution.
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### Setup
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Before you begin, ensure you have Mem0 and Ollama installed and properly configured on your local machine.
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### Full Code Example
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Below is the complete code to set up and use Mem0 locally with Ollama:
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```python
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import os
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from mem0 import Memory
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config = {
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"vector_store": {
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"provider": "supabase",
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"config": {
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"connection_string": "postgresql://supabase_admin:your_password@localhost:5435/postgres",
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"collection_name": "memories",
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"embedding_model_dims": 768, # Change this according to your local model's dimensions
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},
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},
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"llm": {
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"provider": "ollama",
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"config": {
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"model": "llama3.1:latest",
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"temperature": 0,
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"max_tokens": 2000,
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"ollama_base_url": "http://localhost:11434", # Ensure this URL is correct
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},
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},
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"embedder": {
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"provider": "ollama",
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"config": {
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"model": "nomic-embed-text:latest",
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# Alternatively, you can use "snowflake-arctic-embed:latest"
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"ollama_base_url": "http://localhost:11434",
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},
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},
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}
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# Initialize Memory with the configuration
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m = Memory.from_config(config)
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# Add a memory
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m.add("I'm visiting Paris", user_id="john")
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# Retrieve memories
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memories = m.get_all(user_id="john")
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```
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### Key Points
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- **Configuration**: The setup involves configuring the vector store, language model, and embedding model to use local resources.
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- **Vector Store**: Supabase with pgvector is used as the vector store, running on localhost.
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- **Language Model**: Ollama is used as the LLM provider, with the "llama3.1:latest" model.
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- **Embedding Model**: Ollama is also used for embeddings, with the "nomic-embed-text:latest" model.
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### Conclusion
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This local setup of Mem0 using Ollama provides a fully self-contained solution for memory management and AI interactions. It allows for greater control over your data and models while still leveraging the powerful capabilities of Mem0. |