AI / Retrieval
RAG Chatbot
A local document intelligence system for ingestion, semantic retrieval, summarization, and grounded answers.
Overview
RAG Chatbot is an enterprise-ready document intelligence platform that extracts actionable knowledge from complex unstructured PDFs locally.
By keeping model weights and vectors entirely localized, the platform guarantees zero data leakage while offering fast, context-aware information retrieval.
Tech Stack
API
FastAPI
Uvicorn
Orchestration
LangChain
Ollama Execution Framework
Embeddings
Sentence Transformers (Hugging Face)
Storage
ChromaDB Vector Store
Features
+ Hierarchical PDF parsing and validation
+ Dense semantic vector search matching
+ Context-grounded query answering engine
+ Token-by-token server-sent response streaming
+ Verifiable multi-document source citation mappings
+ Isolated offline LLM inference workflows
Architecture
Document Document Ingestion Pipeline
->Recursive Text Splitter Strategy
->Vector Embedding Engine
->ChromaDB Storage Matrix
->Context-Aware Document Retriever
->Local Ollama Model Runner
->Client Output Aggregator
Challenges
Dense, multi-page technical documents produced irrelevant context extractions when using arbitrary fixed chunk sizes.
Systematically re-engineered the parsing chunk strategy to balance semantic token overlap and document structure.
Constructed strict prompting patterns and citation layers to eliminate model hallucination risks.
Lessons Learned
- Architectural mechanics of advanced retrieval-augmented generation patterns
- Mathematical and structural trade-offs in vector token distribution
- Orchestration strategies and resource limits for local LLM runtimes