End-to-End RAG (Retrieval-Augmented Generation) Application Development

Project Overview:

We need an experienced AI/ML developer to build a robust, dynamic Retrieval-Augmented Generation (RAG) system for querying unstructured internal documents (PDFs, DOCX, TXT) with high factual accuracy and low latency.

Key Technical Requirements:

Document Ingestion & Chunking: Dynamic text extraction, cleaning, and semantic chunking. Avoid rigid hardcoded templates so it works across various document types.

Vector Database Integration: Embedding generation and storage using solutions such as PostgreSQL (pgvector), ChromaDB, Pinecone, or Qdrant.

Orchestration & Retrieval: Implemented via LangChain or LangGraph to handle multi-step reasoning, query expansion, and similarity search.

LLM Integration: Connect with local/cloud LLMs (e.g., Groq API, Ollama, OpenAI) with proper guardrails against hallucinations.

Interface / API: A clean REST API (FastAPI) or an interactive prototype (Streamlit / Next.js) for testing query-response flows.

Deliverables:

Clean, modular, and well-documented source code (Python).

Containerized setup (Dockerfile & docker-compose.yml).

Brief documentation explaining setup, chunking strategy, and vector retrieval flow.

Preferred Skills:

Python, LangChain, LangGraph, pgvector / Vector DBs, FastAPI, LLM API Integration, Docker.