This report details the development of a Retrieval-Augmented Generation (RAG) Assistant utilizing LangChain, OpenAI, FAISS, and Streamlit. The assistant allows users to query a custom corpus of technical documents and receive contextually relevant, LLM-generated answers with source attribution. The system demonstrates the practical application of modern retrieval and generation techniques for domain-specific question answering.
Large Language Models (LLMs) such as those provided by OpenAI have revolutionized natural language processing, but their knowledge is static and limited to their training data. Retrieval-Augmented Generation (RAG) systems address this limitation by integrating LLMs with external knowledge sources, enabling dynamic, up-to-date, and context-aware responses. This project implements a RAG assistant focused on technical documentation for LangChain and LangGraph, providing accurate, source-attributed answers through an interactive web interface.
The RAG assistant is implemented in Python and leverages several key technologies:
The workflow is as follows:
.txt files in the docs/ directory are loaded. Each document is split into overlapping chunks (500 characters with 50-character overlap) using a recursive character splitter to ensure context continuity.To evaluate the assistant, the following steps were performed:
.txt files in the docs/ directory. Example files include langchain_concept_vectorstores.txt, langchain_concept_retrieval.txt, and others.Below is an example of an actual input (user question) and the corresponding output (assistant answer and sources):
Input (User Question):
What is a vector store in LangChain and how is it used?
Output (Assistant Answer):
A vector store in LangChain is a specialized data store that enables indexing and retrieving information based on vector representations. It is used to store and query complex relationships between data points, such as social networks, supply-chain management, fraud detection, and recommendation services. It provides a standard interface for working with vector stores, allowing users to easily switch between different vectorstore implementations. The key methods for working with vector stores are add_documents, delete, and similarity_search.
Sources:
The assistant consistently provided relevant and accurate answers to technical questions, with clear source attribution. The use of FAISS enabled fast retrieval even as the document corpus grew. The Streamlit interface made the system accessible and easy to use for developers.
The Code Repository for this RAG Assistant : RAG Assistant
The RAG assistant effectively augments LLMs with a custom knowledge base, enabling accurate, source-attributed answers to domain-specific questions. The modular design, leveraging LangChain, OpenAI, and FAISS, allows for straightforward extension to other domains or document types. This project demonstrates the practical value of RAG systems for technical support and documentation search, and serves as a template for similar applications in other fields.