RAG Oil & Gas Prototype
Connects diverse data sources with Large Language Models using the Multi-Context Protocol, enabling intelligent querying and retrieval-augmented generation for an AI agent framework.
About
The RAG Oil & Gas Prototype introduces an innovative AI agent framework built on the Multi-Context Protocol (MCP) and Retrieval-Augmented Generation (RAG), specifically tailored for the oil and gas industry. It addresses the critical challenge of disparate operational data spread across various domains like drilling, production, HSSE, and purchase orders. By leveraging an MCP Router, user queries are intelligently directed to the appropriate data sources, while RAG capabilities combine keyword and semantic search to retrieve and cite relevant information from technical documents. This solution empowers users to obtain accurate, real-time answers to complex, cross-domain questions, significantly reducing the time and inconsistencies associated with manual data aggregation, thereby enhancing efficiency and decision-making within the enterprise.
Key Features
- Streaming Chat with Server-Sent Events (SSE) for interactive, LLM-powered responses, including planning and execution.
- Hybrid Retrieval-Augmented Generation (RAG) using BM25 and cosine similarity on MySQL document chunks.
- Multi-Context Protocol (MCP) Router and specialized tools for various data domains (e.g., production, drilling, purchase orders).
- Automatic Plan Normalizer to refine and correct LLM-generated routes and optimize RAG queries.
- Answers with citations from retrieved documents for enhanced accuracy and verifiability.
- 3 GitHub stars
Use Cases
- Quickly retrieve and summarize operational data across diverse oil and gas domains (e.g., drilling events, production metrics, HSSE incidents).
- Streamline data access and reduce the burden of manual information gathering for cross-departmental inquiries.
- Generate accurate, cited answers to natural language queries by combining LLM capabilities with internal technical documents.
