Build a RAG Knowledge Assistant Like an Enterprise AI Team
Stand up a working retrieval-augmented assistant over your own documents in about half a day.
Download workflow config (JSON)How the data moves
This is a handoff plan, not an automatic integration. Review outputs before the next step. Unlabeled arrows show order, not a verified data format. Use the capability links to explore tool substitutions.
Step 1
Collect Your Documents and Knowledge Base
Gather every document, wiki page, or file you want the assistant to be able to reference and answer questions from.
Tools
No catalog tool specified
Step 2
Chunk and Embed Into a Vector Database
Use Pinecone to store vector embeddings of your documents so they can be retrieved by semantic similarity.
Step 3
Wire Up a Retrieval Chain
Build the retrieval and prompt logic in LangChain so queries pull the right document chunks before generating an answer.
Step 4
Query It Like an Internal Expert
Start asking the assistant natural-language questions and refine the retrieval logic based on how well it answers.
Tools
No catalog tool specified
Time Required
Half a day, one-time setup
Expected Result
A working assistant you can query in natural language that answers using your own document set as its source of truth.
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