I will a rag pipeline and ai knowledge base system


About this gig
Do you need an AI system that reads your documents and gives accurate, sourced answers?
I build custom RAG (Retrieval-Augmented Generation) systems that transform your PDFs, documents, or knowledge base into an intelligent AI assistant with real retrieval, not hallucinated answers.
What You Get:
- Document ingestion pipeline (PDF, DOCX, TXT, web pages)
- ChromaDB vector database setup and optimization
- Semantic search with Gemini/HuggingFace embeddings
- Multi-query RAG for high accuracy retrieval
- Hybrid search: BM25 keyword + semantic vector search
- CrossEncoder reranking for precision results
- Optional: multimodal support (tables, images from PDFs)
️Tech Stack:
LangGraph | ChromaDB | HuggingFace | BM25 | CrossEncoder | Groq LLMs | Gemini Embeddings
Perfect For:
- Law firms (legal document search)
- Healthcare (medical knowledge base)
- E-commerce (product FAQ assistant)
- SaaS platforms (in-app AI search)
- Research and education tools
- Internal company knowledge systems
Message me with your use case before ordering.
I will recommend the right approach and package for your specific needs.
Get to know Ali raza
Agentic AI Developer
- FromPakistan
- Member sinceMay 2026
Languages
Hindi, Urdu, English
My Portfolio
FAQ
What file types can you process?
I can process PDFs, Word documents (DOCX), plain text files, web pages, and CSV data. Multimodal support includes tables and images embedded in PDFs.
How accurate is the RAG system?
Accuracy depends on document quality. With multi-query retrieval and CrossEncoder reranking, precision is significantly higher than standard RAG. I optimize for your specific use case.
Can this scale to thousands of documents?
Yes. ChromaDB handles large-scale vector storage efficiently. For very large datasets, I can recommend cloud-hosted vector DB solutions.
Will I get the full source code?
Yes. Full source code, setup instructions, and documentation are included in all packages.

