Mihai Farcas This workflow corresponds to n8n.io template #2705 — we link there as the canonical source.
This workflow follows the Agent → Chat Trigger recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "FD0bHNaehP3LzCNN",
"name": "Chat with GitHub OpenAPI Specification using RAG (Pinecone and OpenAI)",
"tags": [],
"nodes": [
{
"id": "362cb773-7540-4753-a401-e585cdf4af8a",
"name": "When clicking \u2018Test workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
0,
0
],
"parameters": {},
"typeVersion": 1
},
{
"id": "45470036-cae6-48d0-ac66-addc8999e776",
"name": "HTTP Request",
"type": "n8n-nodes-base.httpRequest",
"position": [
300,
0
],
"parameters": {
"url": "https://raw.githubusercontent.com/github/rest-api-YOUR_AWS_SECRET_KEY_HERE/api.github.com/api.github.com.json",
"options": {}
},
"typeVersion": 4.2
},
{
"id": "a9e65897-52c9-4941-bf49-e1a659e442ef",
"name": "Pinecone Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
"position": [
520,
0
],
"parameters": {
"mode": "insert",
"options": {},
"pineconeIndex": {
"__rl": true,
"mode": "list",
"value": "n8n-demo",
"cachedResultName": "n8n-demo"
}
},
"credentials": {
"pineconeApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "c2a2354b-5457-4ceb-abfc-9a58e8593b81",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
660,
180
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "7338d9ea-ae8f-46eb-807f-a15dc7639fc9",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
740,
360
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "44fd7a59-f208-4d5d-a22d-e9f8ca9badf1",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-20,
760
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "51d819d6-70ff-428d-aa56-1d7e06490dee",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
320,
760
],
"parameters": {
"options": {
"systemMessage": "You are a helpful assistant providing information about the GitHub API and how to use it based on the OpenAPI V3 specifications."
}
},
"typeVersion": 1.7
},
{
"id": "aed548bf-7083-44ad-a3e0-163dee7423ef",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
220,
980
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "dfe9f356-2225-4f4b-86c7-e56a230b4193",
"name": "Window Buffer Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
420,
1020
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "4cf672ee-13b8-4355-b8e0-c2e7381671bc",
"name": "Vector Store Tool",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"position": [
580,
980
],
"parameters": {
"name": "GitHub_OpenAPI_Specification",
"description": "Use this tool to get information about the GitHub API. This database contains OpenAPI v3 specifications."
},
"typeVersion": 1
},
{
"id": "1df7fb85-9d4a-4db5-9bed-41d28e2e4643",
"name": "OpenAI Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
840,
1160
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "7b52ef7a-5935-451e-8747-efe16ce288af",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-40,
-260
],
"parameters": {
"width": 640,
"height": 200,
"content": "## Indexing content in the vector database\nThis part of the workflow is responsible for extracting content, generating embeddings and sending them to the Pinecone vector store.\n\nIt requests the OpenAPI specifications from GitHub using a HTTP request. Then, it splits the file in chunks, generating embeddings for each chunk using OpenAI, and saving them in Pinecone vector DB."
},
"typeVersion": 1
},
{
"id": "3508d602-56d4-4818-84eb-ca75cdeec1d0",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-20,
560
],
"parameters": {
"width": 580,
"content": "## Querying and response generation \n\nThis part of the workflow is responsible for the chat interface, querying the vector store and generating relevant responses.\n\nIt uses OpenAI GPT 4o-mini to generate responses."
},
"typeVersion": 1
},
{
"id": "5a9808ef-4edd-4ec9-ba01-2fe50b2dbf4b",
"name": "Generate User Query Embedding",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
480,
1400
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "f703dc8e-9d4b-45e3-8994-789b3dfe8631",
"name": "Pinecone Vector Store (Querying)",
"type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
"position": [
440,
1220
],
"parameters": {
"options": {},
"pineconeIndex": {
"__rl": true,
"mode": "list",
"value": "n8n-demo",
"cachedResultName": "n8n-demo"
}
},
"credentials": {
"pineconeApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "ea64a7a5-1fa5-4938-83a9-271929733a8e",
"name": "Generate Embeddings",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
480,
220
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "65cbd4e3-91f6-441a-9ef1-528c3019e238",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-820,
-260
],
"parameters": {
"width": 620,
"height": 320,
"content": "## RAG workflow in n8n\n\nThis is an example of how to use RAG techniques to create a chatbot with n8n. It is an API documentation chatbot that can answer questions about the GitHub API. It uses OpenAI for generating embeddings, the gpt-4o-mini LLM for generating responses and Pinecone as a vector database.\n\n### Before using this template\n* create OpenAI and Pinecone accounts\n* obtain API keys OpenAI and Pinecone \n* configure credentials in n8n for both\n* ensure you have a Pinecone index named \"n8n-demo\" or adjust the workflow accordingly."
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "2908105f-c20c-4183-bb9d-26e3559b9911",
"connections": {
"HTTP Request": {
"main": [
[
{
"node": "Pinecone Vector Store",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Vector Store Tool": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"OpenAI Chat Model1": {
"ai_languageModel": [
[
{
"node": "Vector Store Tool",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Pinecone Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Generate Embeddings": {
"ai_embedding": [
[
{
"node": "Pinecone Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Window Buffer Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Generate User Query Embedding": {
"ai_embedding": [
[
{
"node": "Pinecone Vector Store (Querying)",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Pinecone Vector Store (Querying)": {
"ai_vectorStore": [
[
{
"node": "Vector Store Tool",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"When clicking \u2018Test workflow\u2019": {
"main": [
[
{
"node": "HTTP Request",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
openAiApipineconeApi
Pro
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About this workflow
This workflow demonstrates a Retrieval Augmented Generation (RAG) chatbot that lets you chat with the GitHub API Specification (documentation) using natural language. Built with n8n, OpenAI's LLMs and the Pinecone vector database, it provides accurate and context-aware responses…
Source: https://n8n.io/workflows/2705/ — original creator credit. Request a take-down →
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