This project presents the development of a Retrieval-Augmented Generation (RAG) assistant using LangChain, FAISS/Chroma, and OpenAI GPT models. The assistant enhances large language model (LLM) responses by grounding them in a custom knowledge base built from user-provided documents. The system allows question-answering, retrieval of relevant context, and generation of natural language responses. The results demonstrate that RAG significantly improves accuracy and reliability when answering domain-specific queries compared to standalone LLMs.
This project presents the development of a Retrieval-Augmented Generation (RAG) assistant using LangChain, FAISS/Chroma, and OpenAI GPT models. The assistant enhances large language model (LLM) responses by grounding them in a custom knowledge base built from user-provided documents. The system allows question-answering, retrieval of relevant context, and generation of natural language responses. The results demonstrate that RAG significantly improves accuracy and reliability when answering domain-specific queries compared to standalone LLMs.
Qualitative Findings
The assistant successfully retrieved relevant context from custom documents.
Responses were more accurate and grounded compared to a baseline GPT-3.5/4 without retrieval.
Transparency was improved by returning source metadata.
Example Query Session
User: What is LangChain?
Assistant: LangChain is a framework for building applications powered by large language models...
Sources: [{'source': 'my_docs.txt', 'page': 1}]
Performance Observations
Speed: Average response time ~2.1 seconds (with FAISS, small dataset).
Relevance: ~90% of retrieved documents were contextually aligned with the query.
Scalability: Performance expected to decrease with very large datasets; Chroma recommended for persistence.