Building an AI Agent: RAG, Hybrid Search, Semantic Cache, and Memory
Most RAG demos stop at the happy path: split a PDF, embed the chunks, retrieve the nearest neighbors, put them in a prompt, call a model. That is the right starting point, but it leaves out the parts that make the system feel like an actual assistant instead of a stateless search box. I built a small agent in Python to explore those missing pieces. It is a FastAPI service with two main endpoints: /ingest for uploading documentation and /ask for asking questions against it. Under the hood it uses Redis Stack via redisvl for vector and full-text search, OpenAI for embeddings and answer generation, and Redis again for sessions, semantic cache entries, long-term memory, and metrics. ...