RAG Chatbot

Retrieval-Augmented Generation Chat

GitHub ↗⏱ 2024 · RAG AI pipeline with document grounding

Open-source Python RAG chatbot that ingests documents, generates embeddings, and answers questions with grounded context to reduce hallucinations.

★ Position Gained

Built a document-driven AI pipeline that connects LangChain embeddings and OpenAI chat models to a conversational interface. The project showcases PDF parsing, vector search, and fast retrieval for context-aware question answering.

Pieces in Play

PythonLangChainFastAPIOpenAIPDF ParsingVector Search

⚔ Complications on the Board

Designed a robust PDF ingestion and chunking workflow to preserve document context for retrieval.
Integrated LangChain with OpenAI to serve grounded answers instead of free-form hallucinations.
Balanced document retrieval relevance with prompt construction for reliable chatbot responses.

✦ Post-Game Analysis

Retrieval-augmented generation improves answer accuracy when document embeddings are well structured.
A dedicated document parser is essential for RAG apps to handle diverse PDF content cleanly.
Separating vector search from model prompting helps maintain a stable grounding layer in conversational AI.

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