SUMMARY AI summary by gpt-5-mini
A Retrieval-Augmented Generation (RAG) "Knowledge Agent" implemented in Python. It uses LangChain to orchestrate LLM workflows, OpenAI for embeddings and generation, ChromaDB as the vector store, and FastAPI to expose endpoints. Intended for developers and researchers who need to add document-backed QA, contextual retrieval, or conversational agents to applications. Key features: - Document ingestion and embedding pipeline - Vector storage and similarity search with ChromaDB - LangChain chains/agents to combine retrieval and generation - OpenAI model integration for embeddings and responses - FastAPI-based API for serving queries and integrating into apps
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Owner
Associate Software Engineer focused on Generative AI, RAG systems, backend engineering, and scalable AI-powered applications. Building with LangChain, OpenAI AP
Dates
| Created on GitHub | 2026-05-15 |
| Last push | 2026-05-16 |
| First seen here | 2026-05-15 |
| Last fetched | 2026-05-16 18:17 |
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