I already run a private AI assistant in-house and now need its answers to come directly from our own material. I have chosen Pinecone as the vector store and will start by loading several Excel spreadsheets that contain operating procedures, KPIs and reference tables. The single most important outcome is lightning-fast information retrieval when staff query the bot; analytics, reports, and other add-ons can wait until phase two. Here’s what I need you to do: • Build a clean ingestion pipeline that takes our Excel files, splits them sensibly (sheet, row or custom logic), converts the text to embeddings, and writes everything to a Pinecone index. • Wire up a basic retrieval function (Python / LangChain or similar) that the chatbot can call, returning the most relevant context snippets in milliseconds. • Provide concise documentation and a short demo notebook or script so my team can extend or swap in other file types later (PDF, Word, etc.). Acceptance criteria • All supplied spreadsheets are searchable through Pinecone with <1 s average query time on a modest test set. • Source code runs from the command line with a README covering environment variables, API keys and deployment steps. • Returned passages align with the original cells 95 % of the time in a spot-check. If you have proven Pinecone experience and can show similar retrieval demos, let’s talk and get this running quickly.