{ "name": "Reduce LLM Costs with Semantic Caching using Redis Vector Store and HuggingFace", "nodes": [ { "id": "44708aff-a859-4d15-923f-b33f38000db7", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ -1744, -400 ], "parameters": { "width": 580, "height": 1216, "content": "## Try it out!\n\n### This workflow implements an intelligent semantic caching system that reduces LLM costs and improves response times by caching similar queries.\n\n### How It Works\n\n**Chat Trigger → V" } }, { "id": "7c408b00-c33a-4953-94c6-007cde96a416", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [ -1024, 672 ], "parameters": { "width": 736, "height": 136, "content": "### Tuning the Cache\nAdjust the `distanceThreshold` in the `Analyze results from store` node to control cache sensitivity:\n- **Lower threshold** (e.g., 0.2): More strict matching, fewer false positive" } }, { "id": "485fe302-f0d6-4889-812e-549244cf5ffe", "name": "OpenAI Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ -112, 496 ] }, { "id": "08cc2596-a105-45ce-9645-158607387623", "name": "Redis Chat Memory", "type": "@n8n/n8n-nodes-langchain.memoryRedisChat", "position": [ 64, 496 ] }, { "id": "50407adf-e53c-4a6c-a32f-0bd2a62425b3", "name": "When chat message received", "type": "@n8n/n8n-nodes-langchain.chatTrigger", "position": [ -1088, 288 ] }, { "id": "acc2d01d-4694-47fc-88f5-f79c2ca47d36", "name": "Analyze results from store", "type": "n8n-nodes-base.code", "position": [ -528, 288 ] }, { "id": "9a49690b-fab4-4759-a633-9f7dfcef2be7", "name": "Check for similar prompts", "type": "@n8n/n8n-nodes-langchain.vectorStoreRedis", "position": [ -864, 288 ] }, { "id": "70b60272-9e7f-4d06-9d3c-2da8d5c83ee5", "name": "Respond to Chat (from semantic cache)", "type": "@n8n/n8n-nodes-langchain.chat", "position": [ -32, -32 ] }, { "id": "00e5c3d4-eada-4caf-bf5e-a3b49d73196e", "name": "Respond to Chat (from LLM)", "type": "@n8n/n8n-nodes-langchain.chat", "position": [ 768, 288 ] }, { "id": "f7919340-de57-41ca-a2b6-d41528ed6c34", "name": "LLM Agent", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ -80, 288 ] }, { "id": "ad7dfa5e-4687-4cce-be6f-694779b7ff83", "name": "Store entry in cache", "type": "@n8n/n8n-nodes-langchain.vectorStoreRedis", "position": [ 304, 288 ] }, { "id": "e60f4189-4d99-4f95-98d5-2485a80509a0", "name": "Add response as metadata", "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader", "position": [ 400, 496 ] }, { "id": "2546c015-da8f-4481-bb61-87113f82e206", "name": "Recursive Character Text Splitter", "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter", "position": [ 496, 672 ] }, { "id": "16887dcb-ccba-44a7-a64e-eb99db4256bc", "name": "Sticky Note3", "type": "n8n-nodes-base.stickyNote", "position": [ 288, -32 ], "parameters": { "width": 576, "height": 152, "content": "### Embedding model\nObviously using your own model to calculate the embeddings would not only increase performance but may also drastically reduce the costs.\n\nEven with the existing popular models tho" } }, { "id": "d7cf0fd0-c042-45b5-8a24-29288c92cea4", "name": "Is this a cache hit?", "type": "n8n-nodes-base.if", "position": [ -304, 288 ] }, { "id": "96820700-4963-492f-a91f-b039d6efba03", "name": "Embeddings HuggingFace Inference", "type": "@n8n/n8n-nodes-langchain.embeddingsHuggingFaceInference", "position": [ -864, 480 ] }, { "id": "e5b313d3-ce98-4188-a593-890f5ae09b6b", "name": "Embeddings HuggingFace Inference1", "type": "@n8n/n8n-nodes-langchain.embeddingsHuggingFaceInference", "position": [ 256, 496 ] }, { "id": "56925ea7-b773-486b-91af-c26837844e94", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [ -1104, -400 ], "parameters": { "width": 884, "height": 640, "content": "\n## Initialize the index\n_(manually creating the index can be done using the [langchain library](https://docs.langchain.com/oss/javascript/integrations/vectorstores/redis) )_\n\nIn case your Redis deplo" } }, { "id": "b47e75f4-9b9e-43ca-b100-95a88728dc83", "name": "When clicking ‘Execute workflow’", "type": "n8n-nodes-base.manualTrigger", "position": [ -864, -128 ] }, { "id": "e4595eb8-ad5e-47be-b718-3897fcd8d120", "name": "Initialize Redis store", "type": "@n8n/n8n-nodes-langchain.vectorStoreRedis", "position": [ -512, -272 ] }, { "id": "7988808f-8d22-40c5-afe4-2e26d0d676f5", "name": "Process sample data", "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader", "position": [ -528, -64 ] }, { "id": "66828a6a-00ec-4377-b5bc-c25808050b34", "name": "Use Huggingface for embeddings", "type": "@n8n/n8n-nodes-langchain.embeddingsHuggingFaceInference", "position": [ -720, 80 ] }, { "id": "5e69ae73-70e5-463d-8e90-df327211d47f", "name": "Recursive Character Text Splitter1", "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter", "position": [ -528, 80 ] } ], "connections": { "LLM Agent": { "main": [ [ { "node": "Store entry in cache", "type": "main", "index": 0 } ] ] }, "OpenAI Chat Model": { "ai_languageModel": [ [ { "node": "LLM Agent", "type": "ai_languageModel", "index": 0 } ] ] }, "Redis Chat Memory": { "ai_memory": [ [ { "node": "LLM Agent", "type": "ai_memory", "index": 0 } ] ] }, "Process sample data": { "ai_document": [ [ { "node": "Initialize Redis store", "type": "ai_document", "index": 0 } ] ] }, "Is this a cache hit?": { "main": [ [ { "node": "Respond to Chat (from semantic cache)", "type": "main", "index": 0 } ], [ { "node": "LLM Agent", "type": "main", "index": 0 } ] ] }, "Store entry in cache": { "main": [ [ { "node": "Respond to Chat (from LLM)", "type": "main", "index": 0 } ] ] }, "Add response as metadata": { "ai_document": [ [ { "node": "Store entry in cache", "type": "ai_document", "index": 0 } ] ] }, "Check for similar prompts": { "main": [ [ { "node": "Analyze results from store", "type": "main", "index": 0 } ] ] }, "Analyze results from store": { "main": [ [ { "node": "Is this a cache hit?", "type": "main", "index": 0 } ] ] }, "When chat message received": { "main": [ [ { "node": "Check for similar prompts", "type": "main", "index": 0 } ] ] }, "Use Huggingface for embeddings": { "ai_embedding": [ [ { "node": "Initialize Redis store", "type": "ai_embedding", "index": 0 } ] ] }, "Embeddings HuggingFace Inference": { "ai_embedding": [ [ { "node": "Check for similar prompts", "type": "ai_embedding", "index": 0 } ] ] }, "Embeddings HuggingFace Inference1": { "ai_embedding": [ [ { "node": "Store entry in cache", "type": "ai_embedding", "index": 0 } ] ] }, "Recursive Character Text Splitter": { "ai_textSplitter": [ [ { "node": "Add response as metadata", "type": "ai_textSplitter", "index": 0 } ] ] }, "Recursive Character Text Splitter1": { "ai_textSplitter": [ [ { "node": "Process sample data", "type": "ai_textSplitter", "index": 0 } ] ] }, "When clicking ‘Execute workflow’": { "main": [ [ { "node": "Initialize Redis store", "type": "main", "index": 0 } ] ] } } }