RAG Pipeline - Document Q&A System by Emmanuel NwangumaRAG Pipeline - Document Q&A System by Emmanuel Nwanguma
RAG Pipeline - Document Q&A SystemEmmanuel Nwanguma
Cover image for RAG Pipeline - Document Q&A System
I'll build a production-ready Retrieval-Augmented Generation (RAG) pipeline that lets your application answer questions from your documents accurately and with source citations.
What's included:
Document ingestion and semantic chunking
Vector database setup (FAISS or ChromaDB)
Hybrid retrieval (semantic + keyword)
LLM integration for answer generation
FastAPI backend with sub-500ms latency
Source citation in responses
Basic observability (token usage, latency tracking)
Built with LangChain or LlamaIndex depending on your stack. Deployable on Vercel, Render, or your own server.
Starting at$250
Duration1 week
Tags
FastAPI
LangChain
Python
RAG
AI
LLM
NLP
Vector Database
Service provided by
Emmanuel Nwanguma Lagos, Nigeria
1
Followers
RAG Pipeline - Document Q&A SystemEmmanuel Nwanguma
Starting at$250
Duration1 week
Tags
FastAPI
LangChain
Python
RAG
AI
LLM
NLP
Vector Database
Cover image for RAG Pipeline - Document Q&A System
I'll build a production-ready Retrieval-Augmented Generation (RAG) pipeline that lets your application answer questions from your documents accurately and with source citations.
What's included:
Document ingestion and semantic chunking
Vector database setup (FAISS or ChromaDB)
Hybrid retrieval (semantic + keyword)
LLM integration for answer generation
FastAPI backend with sub-500ms latency
Source citation in responses
Basic observability (token usage, latency tracking)
Built with LangChain or LlamaIndex depending on your stack. Deployable on Vercel, Render, or your own server.
$250