Pinecone

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.

Recap: On Day 7, LangBot learned to stream — token-by-token responses that feel like a real chat app. It remembers the conversation within a session (Day 2 memory), it has a personality (Day 3 prompt templates), it returns typed data (Day 5 structured output), it handles lists (Day 6 output parsers), and it streams those replies in real time (Day 7). But ask it anything outside its training data and it either guesses, hallucinates, or apologizes. Today we fix that by giving LangBot the ability to search external documents — the retrieval half of RAG.