-
securityA
license-
qualityA server that enables document searching using Vertex AI with Gemini grounding, improving search results by grounding responses in private data stored in Vertex AI Datastore.
Last updated -
29
Apache 2.0
Manages environment variables and sensitive configuration through .env files for secure API key storage.
Enables version control for contributing features through forking, branching, committing, and pull requests.
Supports Jupyter notebook functionality through ipykernel, allowing interactive development and testing.
Integrates with OpenAI API for generating completions, enabling RAG capabilities with various models including GPT-4.
Provides type-safety and validation through Pydantic models for robust data handling and configuration.
Built on Python 3.12+ with support for virtual environments and package management.
MCP-RAG: Model Context Protocol with RAG 🚀
A powerful and efficient RAG (Retrieval-Augmented Generation) implementation using GroundX and OpenAI, built with Modern Context Processing (MCP).
🌟 Features
Advanced RAG Implementation: Utilizes GroundX for high-accuracy document retrieval
Model Context Protocol: Seamless integration with MCP for enhanced context handling
Type-Safe: Built with Pydantic for robust type checking and validation
Flexible Configuration: Easy-to-customize settings through environment variables
Document Ingestion: Support for PDF document ingestion and processing
Intelligent Search: Semantic search capabilities with scoring
🛠️ Prerequisites
Python 3.12 or higher
OpenAI API key
GroundX API key
MCP CLI tools
📦 Installation
Clone the repository:
git clone <repository-url>
cd mcp-rag
Create and activate a virtual environment:
uv sync
source .venv/bin/activate # On Windows, use `.venv\Scripts\activate`
⚙️ Configuration
Copy the example environment file:
cp .env.example .env
Configure your environment variables in
.env:
GROUNDX_API_KEY="your-groundx-api-key"
OPENAI_API_KEY="your-openai-api-key"
BUCKET_ID="your-bucket-id"
🚀 Usage
Starting the Server
Run the inspect server using:
mcp dev server.py
Document Ingestion
To ingest new documents:
from server import ingest_documents
result = ingest_documents("path/to/your/document.pdf")
print(result)
Performing Searches
Basic search query:
from server import process_search_query
response = process_search_query("your search query here")
print(f"Query: {response.query}")
print(f"Score: {response.score}")
print(f"Result: {response.result}")
With custom configuration:
from server import process_search_query, SearchConfig
config = SearchConfig(
completion_model="gpt-4",
bucket_id="custom-bucket-id"
)
response = process_search_query("your query", config)
📚 Dependencies
groundx(≥2.3.0): Core RAG functionalityopenai(≥1.75.0): OpenAI API integrationmcp[cli](≥1.6.0): Modern Context Processing toolsipykernel(≥6.29.5): Jupyter notebook support
🔒 Security
Never commit your
.envfile containing API keysUse environment variables for all sensitive information
Regularly rotate your API keys
Monitor API usage for any unauthorized access
🤝 Contributing
Fork the repository
Create your feature branch (
git checkout -b feature/amazing-feature)Commit your changes (
git commit -m 'Add some amazing feature')Push to the branch (
git push origin feature/amazing-feature)Open a Pull Request
This server cannot be installed
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
A server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.
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