I will construct a generative artificial intelligence backend utilizing FastAPI, retrieval-augmented generation, vector databases, Neo4j, and directed acyclic graphs

I offer assistance in developing a scalable, production-ready backend utilizing large language models (LLMs) and vector search, encompassing integration with platforms like OpenAI, creation of RAG pipelines using tools such as LangChain, and connection to vector databases including Milvus, Pinecone, or Qdrant. My expertise lies in constructing APIs with frameworks like FastAPI or Flask, incorporating LLMs (OpenAI, Ollama, custom), establishing embedding pipelines, and linking vector stores for semantic search; I also handle graph databases (Neo4j), metadata management, and complete API delivery. The service provides backend logic with LangChain/RAG, setup of vector databases and search APIs, authentication and session management (JWT), and optional deployment support (Docker, AWS, Runpod); the resulting code is clean, modular, and well-documented, suitable for applications such as AI assistants, document chatbots, or retrieval-based systems. If you are uncertain about the best approach for your needs, please contact me for a quick response.