RAG Knowledge Base: Orchestrating Enterprise Intelligence
A sophisticated retrieval-augmented generation pipeline designed to bridge the gap between static enterprise data and dynamic LLM reasoning.
Timeframe
14 Weeks
My Role
Lead AI Architect
Key Tech
LangChain, Pinecone, GPT-4
The Challenge
Enterprise knowledge is often trapped in fragmented silos—PDFs, internal wikis, and legacy databases. Standard LLMs, while powerful, suffer from hallucinations and lack the context of proprietary data.
The goal was to build a system that could ingest 50,000+ technical documents and provide real-time, verifiable answers with strict adherence to source citations, reducing the information-seeking time for engineers by over 50%.
Architecture Overview
Ingestion & Chunking
Recursive character splitting and semantic chunking using LangChain to ensure context preservation across dense technical specifications.
Vector Embedding
Utilizing OpenAI's text-embedding-3-small model to transform chunks into high-dimensional vectors stored in Pinecone.
Hybrid Retrieval
Combining dense vector search with sparse BM25 keyword matching to handle specific technical terminology and product IDs.
LLM Orchestration
# RAG Pipeline Logic chain = ( {"context": retriever, "question": RunnablePassthrough()} | prompt | model | StrOutputParser() ) # Ensuring strict citation format system_prompt = "You must quote specific source IDs..."
Performance Fine-Tuning
Implemented a re-ranking stage using Cohere Rerank to increase accuracy from top-10 retrievals to top-3 final context injections.
Response Time Reduction
Accuracy in Citations
Average Query Latency
Tokens Processed Daily
User Interface: Natural Language Query Portal
System Architecture: RAG Pipeline Dataflow
The Engine Room — Technologies Used
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