VectorOps — Enterprise RAG & Vector Retrieval Platform

Full-stack retrieval-augmented generation system built on LangChain and ChromaDB with advanced semantic chunking strategies and custom embedding pipelines. Designed for enterprise-grade document intelligence and high-accuracy knowledge retrieval at scale.

What It Does VectorOps is a production-ready RAG infrastructure layer that gives applications the ability to retrieve accurate, context-grounded answers from large document collections — without hallucination from stale training data. Built for organizations that need reliable AI responses grounded in their own documents, policies, contracts, or knowledge bases. Tech Stack LayerTechnologyLLMClaude API (Anthropic) / OpenAIRAG FrameworkLangChainVector DatabaseChromaDBEmbeddingsCustom pipeline (semantic chunking)BackendPython / FastAPIDocument ParsingPDF, DOCX, TXT ingestionAPI LayerREST endpoints for query + ingestion Key Features

Semantic Chunking — splits documents by meaning, not arbitrary token limits Custom Embedding Pipeline — tunable embedding strategies for different document types ChromaDB Integration — persistent local vector store with fast similarity search Multi-document Ingestion — batch ingest PDFs, Word docs, and plain text LangChain Orchestration — chain-based retrieval, re-ranking, and response generation REST API Interface — clean endpoints for document upload and query submission Conversation Memory — multi-turn context retention across a session

Results / Impact

High retrieval accuracy through semantic chunking vs. naive fixed-size splitting Reduced LLM hallucination by grounding every response in retrieved source documents Deployed as reusable infrastructure powering multiple downstream AI applications

Project Status Production-deployed. Serves as the retrieval backbone for multiple client AI systems. Contact Built by Averyon Coffey — acaidev.org · coffeyaveryon@gmail.comYou said: okay so just copy and paste that on github?