{"schemaVersion":"1.0","source":"The Workflow Finder","sourceUrl":"https://www.theworkflowfinder.com","license":"Free to use with attribution and a link to the cited page","generated":"2026-08-28","workflow":{"title":"Build a RAG Knowledge Assistant Like an Enterprise AI Team","slug":"build-a-rag-knowledge-assistant-like-an-enterprise-ai-team","url":"https://www.theworkflowfinder.com/workflows/build-a-rag-knowledge-assistant-like-an-enterprise-ai-team","description":"Stand up a working retrieval-augmented assistant over your own documents in about half a day.","difficulty":"Advanced","timeEstimate":"Half a day, one-time setup","schemaDuration":"PT4H","expectedResult":"A working assistant you can query in natural language that answers using your own document set as its source of truth.","tags":["Developer Tools","AI Infrastructure","Knowledge Management"]},"steps":[{"position":1,"title":"Collect Your Documents and Knowledge Base","instructions":"Gather every document, wiki page, or file you want the assistant to be able to reference and answer questions from.","input":"Workflow inputs: the topic, goal, or raw materials the user is starting from.","output":"Handoff artifact consumed by Step 2 (\"Chunk and Embed Into a Vector Database\").","ioProvenance":{"input":"structural","output":"structural"},"toolSlots":[]},{"position":2,"title":"Chunk and Embed Into a Vector Database","instructions":"Use Pinecone to store vector embeddings of your documents so they can be retrieved by semantic similarity.","input":"Output of Step 1 (\"Collect Your Documents and Knowledge Base\").","output":"Handoff artifact consumed by Step 3 (\"Wire Up a Retrieval Chain\").","ioProvenance":{"input":"structural","output":"structural"},"toolSlots":[{"slug":"pinecone","name":"Pinecone","url":"https://www.theworkflowfinder.com/tools/pinecone","category":"Developer Tools","pricing":"freemium","capabilities":["API integration"]}]},{"position":3,"title":"Wire Up a Retrieval Chain","instructions":"Build the retrieval and prompt logic in LangChain so queries pull the right document chunks before generating an answer.","input":"Output of Step 2 (\"Chunk and Embed Into a Vector Database\").","output":"Handoff artifact consumed by Step 4 (\"Query It Like an Internal Expert\").","ioProvenance":{"input":"structural","output":"structural"},"toolSlots":[{"slug":"langchain","name":"LangChain","url":"https://www.theworkflowfinder.com/tools/langchain","category":"Developer Tools","pricing":"free","capabilities":["API integration","Multi-step agent orchestration"]}]},{"position":4,"title":"Query It Like an Internal Expert","instructions":"Start asking the assistant natural-language questions and refine the retrieval logic based on how well it answers.","input":"Output of Step 3 (\"Wire Up a Retrieval Chain\").","output":"Final workflow output: A working assistant you can query in natural language that answers using your own document set as its source of truth.","ioProvenance":{"input":"structural","output":"structural"},"toolSlots":[]}]}