RAG Database MCP Server

Project Report: A Retrieval-Augmented Generation (RAG) Database and Model Context Protocol (MCP) Server Date: September 5, 2025 Status: Proposal 1. Executive Summary This report outlines the proposal for the development of a sophisticated information retrieval and analysis platform. The core of this platform consists of a Retrieval-Augmented Generation (RAG) database and a server that implements a Model Context Protocol (MCP). This system is designed to ingest, understand, and query a vast corpus of unstructured text data, initially focusing on PDFs and research papers. By transforming these documents into a semantically searchable database, we can enable our AI models to request and receive relevant context, allowing them to perform deep analysis and generate informed responses. This platform represents a foundational tool that will significantly enhance our research capabilities. 2. System Architecture Overview The proposed platform is comprised of two main components: an Ingestion Pipeline and an MCP Server. The overall architecture is designed for scalability, allowing for the continuous addition of new information and the seamless integration of various AI models. Ingestion Pipeline: This component is responsible for processing incoming documents (PDFs, reports) and converting them into a format suitable for semantic search. Vector Database: At the heart of the system, this specialized database stores the semantic representations (embeddings) of the ingested content, allowing for efficient similarity searches. MCP Server: This server acts as the query and interaction layer. It implements the protocol through which AI models can search the database and receive context to formulate meaningful, grounded answers. 3. Ingestion Pipeline The ingestion pipeline is the entry point for all knowledge into the system. Its primary function is to process raw documents and extract their semantic meaning for storage. Key Stages: Document Loading: The pipeline will be configured to accept various file formats, with an initial focus on PDFs and standard text reports. It will handle the extraction of raw text from these files. Text Chunking: To effectively manage large documents, the extracted text will be segmented into smaller, coherent chunks. This process is crucial for generating focused embeddings that capture specific concepts within the document. Embedding Generation: Each text chunk is then passed through a state-of-the-art embedding model ( likely gemini embedding or qwen embedding). This model converts the semantic meaning of the text into a high-dimensional vector. Vector Storage: The generated embeddings, along with their corresponding text chunks and metadata (e.g., source document, page number), are then stored and indexed in a specialized vector database (e.g., Pinecone, Weaviate, or Chroma DB). 4. Model Context Protocol (MCP) Server The MCP server is the core operational component that enables AI models to interact with the knowledge base. It facilitates the search and retrieval process according to a defined protocol, ensuring that models have the necessary information to answer queries accurately. The protocol defines the handshake between an AI model and the knowledge base: Query Reception: The server receives an initial query intended for an AI model. Context Request: The server, acting on behalf of the AI model, initiates a search for context. It first converts the user's query into an embedding using the same model from the ingestion pipeline. Semantic Search & Retrieval: This query embedding is used to perform a similarity search against the vector database. The search returns the text chunks whose embeddings are most closely related to the query's meaning. These retrieved chunks form the "context." Context Augmentation: The retrieved text chunks are packaged according to the protocol and sent to the Large Language Model (LLM). This context is combined with the original query to form a comprehensive prompt. Grounded Response Generation: The LLM, now equipped with specific, relevant information from our internal knowledge base, generates a detailed and accurate response. This process, Retrieval-Augmented Generation (RAG), ensures the model's answers are grounded in the provided documents, minimizing hallucinations and increasing factual accuracy. 5. Use Case: Research Paper Analysis The initial and primary application of this platform will be to build a comprehensive knowledge base from academic and scientific research papers. Capturing Knowledge: Researchers can feed new papers into the ingestion pipeline. The system will automatically process the text and add it to the vector database. Advanced Querying: Instead of simple keyword searches, analysts can ask complex questions like, "What are the latest findings on the efficacy of protein folding simulations using AlphaFold?" Analysis and Summarization: The MCP server will find and provide the relevant sections from multiple papers as context, allowing the LLM to synthesize this information into a concise summary, compare different findings, or identify trends in the research. This capability will fundamentally accelerate our research cycle, allowing our teams to stay current with the latest advancements and base their analysis on a broad and deep set of curated information. 6. Future Roadmap Expansion of Data Sources: Integrate with other data sources, including web pages, internal wikis, and other document repositories. UI/UX Development: Create a user-friendly interface for direct interaction with the RAG system. Model Fine-Tuning: Explore fine-tuning the underlying language and embedding models on our specific domain data to further improve performance and relevance. This platform is a strategic investment in our data and AI capabilities, promising to unlock significant value from our unstructured data assets.