Mcprag
What is Mcprag
mcpRAG is a Retrieval-Augmented Generation (RAG) system that utilizes Ollama for embeddings, Gemini as the Large Language Model (LLM), and MCP server for agentic use.
Use cases
Use cases for mcpRAG include creating intelligent chatbots that provide accurate answers, enhancing search engines with contextual understanding, and developing applications that require dynamic content generation from large text datasets.
How to use
To use mcpRAG, you need to prepare text documents, which are chunked into JSON format containing file name, chunk id, and chunk text. These chunks are converted into embeddings using nomic embeddings, indexed with FAISS, and can be queried to retrieve relevant text for generating responses with the LLM.
Key features
Key features of mcpRAG include the use of open-source embeddings, a vector database (FAISS), and the Gemini LLM for efficient information retrieval and response generation.
Where to use
mcpRAG can be used in various fields such as natural language processing, information retrieval, chatbots, and any application requiring intelligent text generation based on user queries.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Mcprag
mcpRAG is a Retrieval-Augmented Generation (RAG) system that utilizes Ollama for embeddings, Gemini as the Large Language Model (LLM), and MCP server for agentic use.
Use cases
Use cases for mcpRAG include creating intelligent chatbots that provide accurate answers, enhancing search engines with contextual understanding, and developing applications that require dynamic content generation from large text datasets.
How to use
To use mcpRAG, you need to prepare text documents, which are chunked into JSON format containing file name, chunk id, and chunk text. These chunks are converted into embeddings using nomic embeddings, indexed with FAISS, and can be queried to retrieve relevant text for generating responses with the LLM.
Key features
Key features of mcpRAG include the use of open-source embeddings, a vector database (FAISS), and the Gemini LLM for efficient information retrieval and response generation.
Where to use
mcpRAG can be used in various fields such as natural language processing, information retrieval, chatbots, and any application requiring intelligent text generation based on user queries.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
mcpRAG
RAG- using opensource embeddings, opensource vector database and Gemini LLM
In this project i have created RAG using txt documents:
Embeddings : ‘nomic embeddings’ are used with ollama locally
LLM : gemini-2.0-flash
Vector Database : FAISS
All the txt files are chunked with file name, chunk id and chunk text in JSON format and stored locally.
Each chunk is converted into embeddings and collected in a list
This embedding list is indexed using FAISS and stored locally.
when query is embedding using nomic embeddings, these embeddings are searched in FAISS index and relevant indices(location of chunk) is retrieved. These indices are passed to JSON file to get the actual text.
THis text is passed to LLM with the query to formulate the answer.
Additional text is appended to the exiting index and queries are run on the updated index by loading the stored index and embedding file.
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
