Build an OpenAI RAG system with document upload, semantic search and caching

## Overview This workflow implements a complete Retrieval-Augmented Generation (RAG) system for document ingestion and intelligent querying. It allows users to upload documents, convert them into vector embeddings, and query them using natural language. The system retrieves relevant document context and generates accurate AI responses while using caching to improve performance and reduce costs. This workflow is ideal for building AI knowledge bases, document assistants, and internal search systems. --- ## How It Works ### 1. Input & Configuration - Receives requests via webhook (`rag-system`) - Supports two actions: - `upload` → process documents - `query` → answer questions - Defines: - Chunk size & overlap - TopK retrieval count - Database table names --- ### Document Upload Flow 2. **Text Extraction** - Extracts text from uploaded PDF documents 3. **Text Chunking** - Splits text into overlapping chunks for better retrieval accuracy 4. **Document Structuring** - Converts chunks into structured documents 5. **Embedding Generation** - Generates vector embeddings using OpenAI 6. **Vector Storage** - Stores embeddings in PGVector (Postgres) 7. **Upload Logging** - Logs document metadata (user, filename, timestamp) 8. **Response** - Returns success message via webhook --- ### Query Flow 9. **Cache Check** - Checks if query result exists in cache (last 1 hour) 10. **Cache Routing** - If cached → return cached response - If not → proceed to retrieval --- ### Cache Hit Flow 11. **Format Cached Response** - Standardizes cached output format 12. **Respond to User** - Returns cached answer with `cached: true` --- ### Cache Miss Flow 13. **Vector Retrieval** - Retrieves top relevant document chunks from PGVector 14. **AI Answer Generation** - Uses LLM with retrieved context - Generates accurate, context-based answer 15. **Cache Storage** - Saves query + response in database for reuse

33 nodeswebhook trigger0 views0 copiesProductivity
WebhookExtractFromFileTextSplitterRecursiveCharacterTextSplitterDocumentDefaultDataLoaderEmbeddingsOpenAiVectorStorePGVectorPostgresRespondToWebhook

Workflow JSON

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INTERVAL '1 hour'","options":{"queryReplacement":"={{ $('Webhook Trigger').first().json.body.user_id }},={{ $('Webhook Trigger').first().json.body.query }}"},"operation":"executeQuery"},"typeVersion":2.6},{"id":"a6cd4561-8ba5-43a6-a679-d6259f64078c","name":"Cache Hit or Miss","type":"n8n-nodes-base.switch","position":[-112,-64],"parameters":{"rules":{"values":[{"outputKey":"Cache Hit","conditions":{"options":{"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"operator":{"type":"number","operation":"gt"},"leftValue":"={{ $json.length }}","rightValue":0}]},"renameOutput":true},{"outputKey":"Cache Miss","conditions":{"options":{"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"operator":{"type":"number","operation":"equals"},"leftValue":"={{ $json.length }}","rightValue":0}]},"renameOutput":true}]},"options":{}},"typeVersion":3.4},{"id":"1576da8b-432c-42c9-a0e4-487523bf314b","name":"Retrieve Relevant Chunks","type":"@n8n/n8n-nodes-langchain.vectorStorePGVector","position":[432,1088],"parameters":{"options":{"metadata":{"metadataValues":[{"name":"user_id","value":"={{ $('Webhook Trigger').first().json.body.user_id }}"}]}},"tableName":"={{ $('Workflow Configuration').first().json.tableName }}"},"typeVersion":1.3},{"id":"559a79ea-ce0a-4fa1-96e6-f9439799a48a","name":"OpenAI Chat Model","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[224,240],"parameters":{"model":{"__rl":true,"mode":"list","value":"gpt-4.1-mini"},"options":{},"builtInTools":{}},"typeVersion":1.3},{"id":"4952c071-c20b-4e4e-b753-e0fe93413d26","name":"Answer Query with Context","type":"@n8n/n8n-nodes-langchain.agent","position":[288,16],"parameters":{"text":"={{ $('Webhook Trigger').first().json.body.query }}","options":{"systemMessage":"You are a helpful AI assistant that answers questions based on the provided document context.\n\nYour task is to:\n1. Analyze the user's question carefully\n2. Review the relevant document chunks provided from the vector database\n3. Provide a clear, accurate answer based ONLY on the information in the documents\n4. If the answer is not in the provided context, say \"I don't have enough information to answer that question based on the available documents.\"\n5. Always cite which document or section your answer comes from when possible\n\nBe concise but thorough. Maintain a professional and helpful tone."},"promptType":"define"},"typeVersion":3},{"id":"a7bea884-a935-4de3-8770-a52806d1abd4","name":"Save to Query Cache","type":"n8n-nodes-base.postgres","position":[768,16],"parameters":{"query":"INSERT INTO query_cache (user_id, query_hash, query, answer, created_at) VALUES ($1, MD5($2), $3, $4, NOW()) ON CONFLICT (user_id, query_hash) DO UPDATE SET answer = EXCLUDED.answer, created_at = NOW()","options":{"queryReplacement":"={{ $('Webhook Trigger').first().json.body.user_id }},={{ $('Webhook Trigger').first().json.body.query }},={{ $('Webhook Trigger').first().json.body.query }},={{ $json.output }}"},"operation":"executeQuery"},"typeVersion":2.6},{"id":"c380d8d7-769c-4bfe-994e-d54dffcc4c7a","name":"Respond with Answer","type":"n8n-nodes-base.respondToWebhook","position":[1168,16],"parameters":{"options":{},"respondWith":"json","responseBody":"={\n  \"answer\": \"={{ $('Answer Query with Context').first().json.output }}\",\n  \"cached\": false,\n  \"user_id\": \"={{ $('Webhook Trigger').first().json.body.user_id }}\"\n}"},"typeVersion":1.5},{"id":"efcc43e4-7a1d-4174-85dd-f73c55960ba6","name":"Format Cached Response","type":"n8n-nodes-base.set","position":[416,-576],"parameters":{"options":{},"assignments":{"assignments":[{"id":"id-1","name":"answer","type":"string","value":"={{ $json.answer }}"},{"id":"id-2","name":"cached","type":"boolean","value":true},{"id":"id-3","name":"user_id","type":"string","value":"={{ $('Webhook Trigger').first().json.body.user_id }}"}]}},"typeVersion":3.4},{"id":"767873ee-0740-4460-8d1a-79a9b37e1d8b","name":"Respond with Cached Answer","type":"n8n-nodes-base.respondToWebhook","position":[880,-576],"parameters":{"options":{},"respondWith":"json","responseBody":"={\n  \"answer\": \"={{ $json.answer }}\",\n  \"cached\": true,\n  \"user_id\": \"={{ $json.user_id }}\"\n}"},"typeVersion":1.5},{"id":"4f8320ed-a474-4f49-b6d6-baf644f3c937","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[-1472,48],"parameters":{"color":7,"width":592,"height":240,"content":"## Input Layer\nReceive upload or query via webhook and Set chunking, topK, and database tables"},"typeVersion":1},{"id":"0aafeb7b-06f6-45c9-bfe8-322e95550240","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[-800,16],"parameters":{"color":7,"width":304,"height":288,"content":"## Action Routing\nRoute request to upload or query flow"},"typeVersion":1},{"id":"34da2fb8-619f-4e21-b2d1-4b7e0192f2ae","name":"Answer questions with a vector store","type":"@n8n/n8n-nodes-langchain.toolVectorStore","position":[368,240],"parameters":{},"typeVersion":1.1},{"id":"479de263-883a-4197-9ae2-09ed26b05b5c","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[-448,-192],"parameters":{"color":7,"width":470,"height":288,"content":"## Cache Check\nCheck if query result exists in cache"},"typeVersion":1},{"id":"afface0a-2435-4831-aee3-0c5683218a53","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[336,-704],"parameters":{"color":7,"width":272,"height":320,"content":"## Cache Response Formatting\nFormat cached query results into a consistent response structure."},"typeVersion":1},{"id":"2fe09f60-1e2b-42fd-9115-34af03fcfee3","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[768,-736],"parameters":{"color":7,"width":304,"height":320,"content":"## Response Layer\nReturn answer or cached result via webhook"},"typeVersion":1},{"id":"0adeaf21-cd46-4e59-96a7-2285c91d79a9","name":"Sticky Note6","type":"n8n-nodes-base.stickyNote","position":[1056,-64],"parameters":{"color":7,"width":304,"height":240,"content":"## Response Layer\nReturn answer or cached result via webhook"},"typeVersion":1},{"id":"9efa23a4-2f27-4b7b-b729-c9a0c02194ce","name":"Sticky Note7","type":"n8n-nodes-base.stickyNote","position":[656,-64],"parameters":{"color":7,"width":304,"height":272,"content":"## Cache Storage\nStore query and response for reuse"},"typeVersion":1},{"id":"75a4c942-f12b-42ae-96a1-6a36542acf80","name":"Sticky Note8","type":"n8n-nodes-base.stickyNote","position":[176,-80],"parameters":{"color":7,"width":464,"height":448,"content":"## Answer Generation\nGenerate answer using context + AI model"},"typeVersion":1},{"id":"02e378e4-dd93-4800-ae87-1d14490b2727","name":"Sticky Note9","type":"n8n-nodes-base.stickyNote","position":[-512,672],"parameters":{"color":7,"width":304,"height":240,"content":"## Document Processing\nExtract text from uploaded documents"},"typeVersion":1},{"id":"f21786fe-b403-4aa0-b484-caa8bcd67e64","name":"Sticky Note10","type":"n8n-nodes-base.stickyNote","position":[-160,640],"parameters":{"color":7,"width":304,"height":272,"content":"## Chunking Engine\nSplit text into overlapping chunks"},"typeVersion":1},{"id":"6a5abc30-754c-4e9b-8213-f79186b80a35","name":"Sticky Note11","type":"n8n-nodes-base.stickyNote","position":[400,976],"parameters":{"color":7,"width":304,"height":240,"content":"## Embeddings Storage\nGenerate embeddings and store in PGVector"},"typeVersion":1},{"id":"7a1009b0-7395-4041-bde5-06d382318b30","name":"Sticky Note12","type":"n8n-nodes-base.stickyNote","position":[368,624],"parameters":{"color":7,"width":672,"height":272,"content":"## Upload Logging\nTrack document uploads in database"},"typeVersion":1},{"id":"32a6aac8-009a-4cac-829b-ca853c9f2f38","name":"Sticky Note13","type":"n8n-nodes-base.stickyNote","position":[-2224,-256],"parameters":{"width":448,"height":576,"content":"## How it works\nThis workflow implements a complete Retrieval-Augmented Generation (RAG) system for document ingestion and intelligent querying.\n\nUsers can upload documents or send queries via webhook. Uploaded files are processed by extracting text, splitting it into chunks, generating embeddings, and storing them in a vector database (PGVector).\n\nWhen a query is received, the workflow first checks a cache for recent answers. If no cache is found, it retrieves relevant document chunks using vector search and generates a contextual answer using an AI model.\n\nResponses are cached for faster future queries and returned to the user via webhook.\n\n## Setup steps\n- Configure webhook endpoint for upload and query actions\n- Add OpenAI API credentials for embeddings and chat\n- Set up Postgres with PGVector extension\n- Create tables for documents and query cache\n- Adjust chunk size, overlap, and topK settings"},"typeVersion":1}],"pinData":{},"connections":{"Text Splitter":{"ai_textSplitter":[[{"node":"Document Loader","type":"ai_textSplitter","index":0}]]},"Document Loader":{"ai_document":[[{"node":"Store Embeddings in PGVector","type":"ai_document","index":0}]]},"Route by Action":{"main":[[{"node":"Extract Text from Document","type":"main","index":0}],[{"node":"Check Query Cache","type":"main","index":0}]]},"Webhook Trigger":{"main":[[{"node":"Workflow Configuration","type":"main","index":0}]]},"Cache Hit or Miss":{"main":[[{"node":"Format Cached Response","type":"main","index":0}],[{"node":"Answer Query with Context","type":"main","index":0}]]},"Check Query Cache":{"main":[[{"node":"Cache Hit or Miss","type":"main","index":0}]]},"OpenAI Chat Model":{"ai_languageModel":[[{"node":"Answer Query with Context","type":"ai_languageModel","index":0}]]},"OpenAI Embeddings":{"ai_embedding":[[{"node":"Store Embeddings in PGVector","type":"ai_embedding","index":0},{"node":"Retrieve Relevant Chunks","type":"ai_embedding","index":0}]]},"Log Upload to Cache":{"main":[[{"node":"Respond Upload Success","type":"main","index":0}]]},"Save to Query Cache":{"main":[[{"node":"Respond with Answer","type":"main","index":0}]]},"Format Cached Response":{"main":[[{"node":"Respond with Cached Answer","type":"main","index":0}]]},"Workflow Configuration":{"main":[[{"node":"Route by Action","type":"main","index":0}]]},"Retrieve Relevant Chunks":{"ai_vectorStore":[[{"node":"Answer questions with a vector store","type":"ai_vectorStore","index":0}]]},"Answer Query with Context":{"main":[[{"node":"Save to Query Cache","type":"main","index":0}]]},"Extract Text from Document":{"main":[[{"node":"Store Embeddings in PGVector","type":"main","index":0}]]},"Store Embeddings in PGVector":{"main":[[{"node":"Log Upload to Cache","type":"main","index":0}]]},"Answer questions with a vector store":{"ai_tool":[[{"node":"Answer Query with Context","type":"ai_tool","index":0}]]}}}

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