kinola-IQ/Retrieval-Augmented-Generation-chat-system
A Python-based retrieval-augmented generation chatbot project that includes an API server, RAG pipeline, embeddings/retrieval/vector store logic, data ingestion scripts, DVC model response evaluation pipeline with openeval, CI/CD containerization and code quality check pipeline with docker and pylint, and tests for API, generation, and retrieval.
SUMMARY AI summary by gpt-5-mini
This repository implements a Retrieval-Augmented Generation (RAG) chatbot backend: a FastAPI service that ingests PDFs, chops and embeds text (LangChain/Hugging Face), stores vectors in Pinecone, retrieves relevant chunks for a query, and generates grounded answers via Hugging Face models. It’s intended for developers, ML engineers, and data engineers building QA/chat systems or evaluating retrieval+generation pipelines. Key features: ingestion pipeline (PDF loading, chunking, embedding, Pinecone upsert); retriever (query embedding + similarity search); RAG pipeline (prompt construction, HF model wrapper, answer + source packaging); FastAPI endpoints (/v1/chat, health); ingestion and benchmark scripts; CI linting; Docker image and publish workflows; tests. Configuration is environment-variable driven. Core flow is implemented; some components are minimal and Pinecone/index compatibility is required.
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Dates
| Created on GitHub | 2026-04-19 |
| Last push | 2026-05-09 |
| First seen here | 2026-05-09 |
| Last fetched | 2026-05-09 16:11 |
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