---
title: Self-Hosted AI Companion
description: "Run Deja Vu end-to-end on your machine using Ollama-powered LLMs and embedders."
---


Deja Vu 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.

## Overview

By using Ollama, you can run Deja Vu 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.

## Setup

Before you begin, ensure you have Deja Vu and Ollama installed and properly configured on your local machine.

## Full Code Example

Below is the complete code to set up and use Deja Vu locally with Ollama:

```python
from dejavu import Memory

config = {
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "collection_name": "test",
            "host": "localhost",
            "port": 6333,
            "embedding_model_dims": 768,  # Change this according to your local model's dimensions
        },
    },
    "llm": {
        "provider": "ollama",
        "config": {
            "model": "llama3.1:latest",
            "temperature": 0,
            "max_tokens": 2000,
            "ollama_base_url": "http://localhost:11434",  # Ensure this URL is correct
        },
    },
    "embedder": {
        "provider": "ollama",
        "config": {
            "model": "nomic-embed-text:latest",
            # Alternatively, you can use "snowflake-arctic-embed:latest"
            "ollama_base_url": "http://localhost:11434",
        },
    },
}

# Initialize Memory with the configuration
m = Memory.from_config(config)

# Add a memory
m.add("I'm visiting Paris", user_id="john")

# Retrieve memories
memories = m.get_all(filters={"user_id": "john"})
```

## Key Points

- **Configuration**: The setup involves configuring the vector store, language model, and embedding model to use local resources
- **Vector Store**: Qdrant is used as the vector store, running on localhost
- **Language Model**: Ollama is used as the LLM provider, with the `llama3.1:latest` model
- **Embedding Model**: Ollama is also used for embeddings, with the `nomic-embed-text:latest` model

## Conclusion

This local setup of Deja Vu 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 Deja Vu.

---

<CardGroup cols={2}>
  <Card title="Configure Open Source" icon="gear" href="/open-source/configuration">
    Explore advanced configuration options for vector stores, LLMs, and embedders.
  </Card>
  <Card title="Build a Deja Vu Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
    Learn core companion patterns that work with any LLM provider.
  </Card>
</CardGroup>
