RAG Pipeline — Private Document AIVlad Ioan
I build RAG (Retrieval-Augmented Generation) pipelines that connect your LLM to your private documents — no data leaves your servers.
What's included:
Vector database setup (Qdrant) on your infrastructure
Embedding pipeline: BGE-M3 with bge-reranker-v2-m3 for hybrid search
Document ingestion (PDF, Word, internal wikis, databases)
Dify integration as chat interface for your team
Chunk size optimization and retrieval testing
Results: your team asks questions in plain language and gets accurate answers grounded in your actual documents — not hallucinations.
Ideal for: legal firms, healthcare, finance, defence contractors, any organization with large internal document repositories.
Deliverable: functional RAG system tested on your documents, with user interface and admin access.
Contact for pricing
Duration1 week
Tags
Docker
Data Engineer
Machine Learning
Artificial Intelligence
Python
Service provided by
Vlad Ioan proBucharest, Romania
RAG Pipeline — Private Document AIVlad Ioan
Contact for pricing
Duration1 week
Tags
Docker
Data Engineer
Machine Learning
Artificial Intelligence
Python
I build RAG (Retrieval-Augmented Generation) pipelines that connect your LLM to your private documents — no data leaves your servers.
What's included:
Vector database setup (Qdrant) on your infrastructure
Embedding pipeline: BGE-M3 with bge-reranker-v2-m3 for hybrid search
Document ingestion (PDF, Word, internal wikis, databases)
Dify integration as chat interface for your team
Chunk size optimization and retrieval testing
Results: your team asks questions in plain language and gets accurate answers grounded in your actual documents — not hallucinations.
Ideal for: legal firms, healthcare, finance, defence contractors, any organization with large internal document repositories.
Deliverable: functional RAG system tested on your documents, with user interface and admin access.
Contact for pricing