Setting Up and Using an Embeddings Proxy for Ollama: A Step-by-Step Guide

Ollama's embeddings provide a robust way to handle text data transformations, particularly for applications that involve natural language processing. By using a proxy for embeddings, you can enable smoother integration between Ollama and other platforms, such as GraphRAG, Vertex AI, or Gemini models. This guide walks you through setting up a proxy for Ollama embeddings, including configuration tips and usage examples based on available GitHub repositories.

Why Use an Embeddings Proxy for Ollama?

Embedding proxies serve as a middleware layer, translating responses from Ollama into formats required by different services, like Vertex AI or GraphRAG. This setup allows applications to:

• Integrate Ollama embeddings with other AI frameworks.
• Handle compatibility issues between different model formats.
• Standardize embeddings responses for consistent performance across platforms.

Setting Up an Ollama Embeddings Proxy

Step 1: Clone a Suitable GitHub Repository

A few GitHub repositories offer proxies for connecting Ollama embeddings with other platforms. Based on your use case, choose a suitable repository to get started.

• GraphRAG to Ollama Proxy:
• Repository: GraphRAG-Local-UI
• This repository provides a middleware to format embedding responses for GraphRAG’s expected input.
• Vertex AI with Ollama Proxy:
• Repository: ollama-vertex-ai
• This proxy allows Ollama to interact with Vertex AI, offering a REST API that supports Ollama’s formatting.
• Ollama for Gemini Models Proxy:
• Repository: proxy-to-gemini
• A sidecar proxy for connecting Gemini models with APIs compatible with Ollama.

Clone the repository relevant to your needs

git clone https://github.com/[username]/[repository-name].git
cd [repository-name]

Step 2: Install Dependencies

Most proxy repositories use Node.js or Python. Follow the installation instructions from the repository’s README to install necessary packages.

For Node.js-based repositories:

npm install

For Python-based repositories:

pip install -r requirements.txt

Step 3: Configure the Proxy

Each repository provides configuration settings for connecting to Ollama. Open the configuration file (config.js, .env, or similar) and input your Ollama API credentials and desired settings.

Example configuration settings in .env:

OLLAMA_API_KEY=your_api_key_here
OLLAMA_BASE_URL=https://api.ollama.ai/embedding
TARGET_API_URL=https://your.target.api/endpoint
PORT=3000

Step 4: Start the Proxy Server

With dependencies installed and configuration completed, you can start the proxy server.

For Node.js:

node server.js

For Python:

python app.py

Step 5: Test the Embedding Proxy

Send a test request to confirm the proxy’s functionality and ensure it correctly formats embedding responses. Use a tool like curl or Postman to test your proxy endpoint.

Example curl command:

curl -X POST http://localhost:3000/embedding \
  -H "Content-Type: application/json" \
  -d '{"text": "Sample text to be embedded"}'

The proxy should process the request, retrieve the embedding from Ollama, and return it in the required format.

Examples of Specific Embedding Proxies

1. GraphRAG Embedding Proxy

The GraphRAG-Local-UI embedding proxy allows GraphRAG to handle embeddings from Ollama. This proxy transforms Ollama’s embedding format into the structure GraphRAG expects.

Repository: GraphRAG-Local-UI

2. Vertex AI with Ollama Proxy

The ollama-vertex-ai repository provides a REST API proxy that mimics Ollama’s interface, enabling easy access to Vertex AI models with Ollama-compatible requests.

Repository: ollama-vertex-ai

3. Gemini Model Proxy

For those working with Gemini models, the proxy-to-gemini repository allows integration by translating requests from OpenAI or Ollama API formats.

Repository: proxy-to-gemini

Troubleshooting Tips

1. Check Proxy Logs: If the proxy fails, check the server logs in your terminal or log files. Most repos have detailed error logs.
2. Verify API Keys: Ensure your Ollama API key is correct and active.
3. Check Compatibility: Confirm that the formats used by the proxy match the requirements of the external platform (e.g., GraphRAG, Vertex AI).

Conclusion

Setting up an Ollama embeddings proxy allows seamless interaction between Ollama and other platforms, facilitating cross-service compatibility and enhancing the utility of embeddings in your projects. Following the setup steps outlined, you can create a reliable proxy to bridge Ollama’s embedding services with various AI frameworks like GraphRAG, Vertex AI, and Gemini.