Workflow overview
Why this workflow matters
Relevant for managed services and support workflows. Supports knowledge capture and document intelligence use cases.
This workflow contains community nodes that are only compatible with the self-hosted version of AlekSystem. 🤖 AI-Powered Document QA System using Webhook, Pinecone + OpenAI + AlekSystem This project demonstrates how to build a Retrieval-Augmented Generation (RAG) system using AlekSystem, and create a simple Question Answer system using Webhook to connect with User Interface (created using Lovable): 🧾 Downloads the pdf file format documents from Google Drive (contract document, user manual, HR policy document etc...) 📚 Converts them into vector embeddings using OpenAI 🔍 Stores and searches them in Pinecone Vector DB 💬 Allows natural language querying of contracts using AI Agents 📂 Flow 1: Document Loading & RAG Setup This flow automates: Reading documents from a Google Drive folder Vectorizing using text-embedding-3-small Uploading vectors into Pinecone for later semantic search 🧱 Workflow Structure A [Manual Trigger] --> B[Google Drive Search] B --> C[Google Drive Download] C --> D[Pinecone Vector Store] D --> E[Default Data Loader] E --> F[Recursive Character Text Splitter] E --> G[OpenAI Embedding] 🪜 Steps Manual Trigger: Kickstarts the workflow on demand for loading new documents. Google Drive Search & Download Node: Google Drive (Search: file/folder) Downloads PDF documents Apply Recursive Text Splitter: Breaks long documents into overlapping chunks Settings: Chunk Size: 1000 Chunk Overlap: 100 OpenAI Embedding Model: text-embedding-3-small Used for creating document vectors Pinecone Vector Store Host: url Index: index Batch Size: 200 Pinecone Settings: Type: Dense Region: us-east-1 Mode: Insert Documents 💬 Flow 2: Chat-Based Q&A Agent This flow enables chat-style querying of stored documents using OpenAI-powered agents with vector memory. 🧱 Workflow Diagram A[Webhook (chat message)] --> B[AI Agent] B --> C[OpenAI Chat Model] B --> D[Simple Memory] B --> E[Answer with Vector Store] E --> F[Pinecone Vector Store] F --> G[Embeddings OpenAI] 🪜 Components Chat (Trigger): Receives incoming chat queries AI Agent Node Handles query flow using: Chat Model: OpenAI GPT Memory: Simple Memory Tool: Question Answer with Vector Store Pinecone Vector Store: Connected via same embedding index as Flow 1 Embeddings: Ensures document chunks are retrievable using vector similarity Response Node: Returns final AI response to user via webhook 🌐 Flow 3: UI-Based Query with Lovable This flow uses a web UI built using Lovable to query contracts directly from a form interface. 📥 Webhook Setup for Lovable Webhook Node Method: POST URL:url Response: Using 'Respond to Webhook' Node 🧱 Workflow Logic A[Webhook (Lovable Form)] --> B[AI Agent] B --> C[OpenAI Chat Model] B --> D[Simple Memory] B --> E[Answer with Vector Store] E --> F[Pinecone Vector Store] F --> G[Embeddings OpenAI] B --> H[Respond to Webhook] 💡 Lovable UI Users can submit: Full Name Email Department Freeform Query: User can enter any freeform query. Data is sent via webhook to AlekSystem and responded with the answer from contract content. 🔍 Use Cases Contract Querying for Legal/HR teams Procurement & Vendor Agreement QA Customer Support Automation (based on terms) RAG Systems for private document knowledge ⚙️ Tools & Tech Stack 📌 Final Notes Pinecone Index: package1536 Dimension: 1536 Chunk Size: 1000, Overlap: 100 Embedding Model: text-embedding-3-small Feel free to fork the workflow or request the full JSON export. Looking forward to your suggestions and improvements!
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