Rag

Recap: On Day 9, LangBot became a full RAG pipeline — every chat message retrieves relevant documents and the LLM answers from them. The /search command, list-mode routing, and regular chat all coexist. But there is a problem hiding in plain sight: the routing logic is hand-coded in if/elif blocks. LangBot doesn’t decide anything — it follows a script. Today we replace that script with an agent: an LLM that reasons about which tool to use, calls it, and responds — autonomously.

Recap: On Day 8, LangBot gained a long-term memory — a vector store (Pinecone) filled with document embeddings. You can ask /search home office stipend and get relevant chunks back. But that is half the picture. You still have to type /search, read raw chunks, and synthesize the answer yourself. Today we close the loop: the retriever feeds into the chatbot automatically, so you ask “How much is the home office stipend?” and LangBot answers from your documents — no slash command, no manual lookup.