I will build advanced multi model rag pipelines

I build Multi-Model RAG (Retrieval-Augmented Generation) systems that use multiple LLMs, embeddings, and routing logic to give highly accurate responses from your documents, databases, and private data.

My RAG pipelines are engineered for precision, context retention, high recall, and enterprise-grade reliability.

What I Can Build

• Multi-model RAG with LLM routing
• RAG using GPT, LLaMA, Mistral, Qwen
• PDF/document ingestion & processing
• Advanced chunking + embedding optimization
• Vector DB setup (Pinecone, Weaviate, Chroma, FAISS)
• Hybrid search (keyword + semantic)
• Metadata filtering
• Multi-document QA systems
• Private knowledge base chatbots
• Custom APIs + dashboard access

Advanced Features

• Multi-model query routing
• Model-specific relevance scoring
• Intelligent chunk merging
• Citation + source highlighting
• Structured + unstructured data support
• Memory + long-context models
• On-prem or offline RAG

Tech Stack

• Python
• LangChain / LlamaIndex
• GPT-4o, GPT-5, Mistral, LLaMA, Qwen
• Pinecone / Weaviate / Chroma / FAISS
• FastAPI
• Cloud & local deployments

Why Work With Me

• 100% custom-built pipelines
• Clean, optimized, scalable architecture
• High accuracy retrieval
• Multi-model flexibility