Système de réponse aux questions de documentation intelligente basé sur Webhook, Pinecone + OpenAI + n8n

• •Point de terminaison HTTP Webhook (généré automatiquement par n8n)
• •Informations d'identification Google Drive API
• •Clé API OpenAI
• •Clé API Pinecone

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        "content": "## Document Loading \n1. Connect to Google Drive folder to access Contract Agreement Documents\n2. Download and Vectorize the Data using Vector Embedding \n3. Store in Pinecone Database"
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        "content": "## Query Document via Chat (for testing)"
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          "systemMessage": "*Role*\nYou are a highly experienced contracting, commercial and legal adviser who thoroughly understands the contract related to shipping, clearing and forwarding agreements and advise and reply to chat queries looking into the pinecone vector database and respond accordingly. \n\n**Instructions**\nyou will receive chat query to which you have to reply back in chat\nyou will only look for information in the pinecone vector databse\nyou will not create your own reply if you don't get the answer from the database\n\nNote:\nbe polite and professional in your response\ncan use emojis where it is appropriate\n"
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        "content": "## Query document from a user interface connectied via Webhook\n"
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      "parameters": {
        "text": "=the query: {{ $json.body.query }}",
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          "systemMessage": "*Role*\nYou are a highly experienced contracting, commercial and legal adviser who thoroughly understands the contract related to shipping, clearing and forwarding agreements and advise and reply to chat queries looking into the pinecone vector database and respond accordingly. \n\n**Instructions**\nyou will receive chat query to which you have to reply back in chat\nyou will only look for information in the pinecone vector databse\nyou will not create your own reply if you don't get the answer from the database\n\nNote:\nbe polite and professional in your response\ncan use emojis where it is appropriate\n"
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      "type": "@n8n/n8n-nodes-langchain.toolVectorStore",
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      "parameters": {
        "description": "When ever there is a query from chat, use this pinecone vector database to analyse and construct the response. "
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          "responseKey": "={{ $json.output }}"
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        "content": "This project demonstrates how to build a Retrieval-Augmented Generation (RAG) system using n8n, which:\n🧾 Downloads any pdf file format documents from Google Drive\n📚 Converts them into vector embeddings using OpenAI\n🔍 Stores and searches them in Pinecone Vector DB\n💬 Allows natural language querying of contracts using AI Agents\n\n## Document Loading & RAG Setup\nThis flow automates:\nReading documents from a Google Drive folder\nVectorizing using text-embedding-3-small\nUploading vectors into Pinecone for later semantic search\n\n### 🧱 Workflow Structure\nA [Manual Trigger] --> B[Google Drive Search]\nB --> C[Google Drive Download]\nC --> D[Pinecone Vector Store]\nD --> E[Default Data Loader]\nE --> F[Recursive Character Text Splitter]\nE --> G[OpenAI Embedding]\n\n### 🪜 Steps\nManual Trigger: Kickstarts the workflow on demand for loading new documents.\nGoogle Drive Search & Download\nNode: Google Drive (Search: file/folder), Credentials required to access google drive folders and files\nDownloads PDF documents from the google drive\n\n#### Recursive Text Splitter to Break long documents into overlapping chunks\nSettings:\nChunk Size: 1000\nChunk Overlap: 100\n\n#### OpenAI Embedding\nModel: text-embedding-3-small\nUsed for creating document vectors\n\n#### Pinecone Vector Store\nIndex: package1536\nBatch Size: 200\nSettings:\nType: Dense\nRegion: us-east-1\nMode: Insert Documents\n\n\n"
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      "type": "n8n-nodes-base.stickyNote",
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        "content": "## Quyerying the Documetn via Chat \nThis flow enables chat-style querying of stored documents using OpenAI-powered agents with vector memory.\n\n### 🧱 Workflow Diagram\n  A[Webhook (chat message)] --> B[AI Agent]\n  B --> C[OpenAI Chat Model]\n  B --> D[Simple Memory]\n  B --> E[Answer with Vector Store]\n  E --> F[Pinecone Vector Store]\n  F --> G[Embeddings OpenAI]\n### 🪜 Components\nChat Trigger\nAI Agent Node\n\nHandles query flow using:\nChat Model: OpenAI GPT\nMemory: Simple Memory\nTool: Question Answer with Vector Store\nPinecone Vector Store\nConnected via same embedding index as Flow 1 Embeddings\nEnsures document chunks are retrievable using vector similarity\nResponse Node\nReturns final AI response to user via chat response\n\n"
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        "content": "## 🌐 Flow 3: UI-Based Query with webhook connecting to Lovable\nThis flow uses a web UI built using Lovable to query contracts directly from a form interface.\n\n### 📥 Webhook Setup for Lovable\nWebhook Node\nMethod: POST\nURL: your webhook url\nResponse: Using 'Respond to Webhook' Node\n\n### 🧱 Workflow Logic\n  A[Webhook (Lovable Form)] --> B[AI Agent]\n  B --> C[OpenAI Chat Model]\n  B --> D[Simple Memory]\n  B --> E[Answer with Vector Store]\n  E --> F[Pinecone Vector Store]\n  F --> G[Embeddings OpenAI]\n  B --> H[Respond to Webhook]\n\n### 💡 Lovable UI\nUsers can submit:\nFull Name\nEmail\nDepartment\nFreeform Query\n\nData is sent via webhook to n8n and responded with the answer from contract content.\n\n### 🔍 Use Cases\nContract Querying for Legal/HR teams\nProcurement & Vendor Agreement QA\nCustomer Support Automation (based on terms)\nRAG Systems for private document knowledge\n\n⚙️ Tools & Tech Stack\nComponent\tTool Used\nAI Embedding\tOpenAI text-embedding-3-small\nVector DB\tPinecone\nChunking\tRecursive Text Splitter\nAI Agent\tOpenAI GPT Chat\nAutomation\tn8n\nUI Integration\tLovable (form-based)\n\n\n\n"
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