Bitrix24 Open Channel RAG Chatbot Application Workflow Example with Webhook Integration
This workflow integrates with the Bitrix24 open channel to implement an intelligent chatbot application that features efficient question-and-answer capabilities based on Retrieval-Augmented Generation (RAG) technology. It can automatically register the bot, handle user messages, and provide intelligent responses based on the content of uploaded documents. The documents are stored and retrieved using a vector database, combined with advanced chat models, which enhances the accuracy of answers and contextual understanding, making it suitable for scenarios such as internal knowledge management and customer support within enterprises.
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Workflow Name
Bitrix24 Open Channel RAG Chatbot Application Workflow Example with Webhook Integration
Key Features and Highlights
This workflow implements an intelligent chatbot application integrated with Bitrix24 Open Channels, leveraging Retrieval-Augmented Generation (RAG) technology for enhanced question-answering capabilities. It supports receiving and processing Webhook events, automatic bot registration, dynamic user message responses, and intelligent Q&A based on uploaded document content. Document data is vectorized and stored in the Qdrant vector database for efficient retrieval. The integration of Google Gemini chat model and Ollama text embedding model significantly improves answer accuracy and contextual understanding.
Core Problems Addressed
- Automates bot registration and event handling within Bitrix24 Open Channels, simplifying bot deployment.
- Enables intelligent Q&A based on uploaded documents by combining RAG technology with enterprise knowledge bases, enhancing customer and employee self-service experiences.
- Automates document storage and vectorization to support efficient semantic search and response.
- Provides end-to-end automation for various chat events, including message addition, bot joining chats, app installation, and bot removal.
Application Scenarios
- Internal enterprise knowledge base Q&A assistant to help employees quickly access document information.
- Customer support automation with instant chatbot responses to customer inquiries.
- Demonstration and technical showcase of intelligent chatbot integration within Bitrix24 Open Channels.
- Business scenarios requiring intelligent retrieval and Q&A based on document content.
Main Workflow Steps
- Webhook Event Reception: Receive POST requests from Bitrix24 Open Channels via the “Bitrix24 Handler” node.
- Credential Setup and Verification: Set and verify application tokens to ensure request legitimacy.
- Event Routing: Branch processing based on event types such as message addition, bot joining chat, app installation, and bot deletion.
- Message Processing: Parse user messages and invoke the RAG question-answering chain to generate responses.
- Bot Registration: Automatically call Bitrix24 API to register the bot and bind related events.
- Document Management:
- Retrieve storage lists and folder contents.
- Download relevant documents and use LangChain nodes for PDF loading and text chunking.
- Generate text embeddings via the Ollama model.
- Insert vectors into the Qdrant vector database for document vector storage.
- Q&A Retrieval: Perform vector-based retrieval combined with the Google Gemini model to generate accurate answers based on user queries.
- Response Delivery: Send generated answers back to the chat conversation through the Bitrix24 interface.
- Error and Success Handling: Appropriately respond to Webhook call results to ensure workflow stability.
Involved Systems and Services
- Bitrix24 (Open Channels, Bot API)
- Webhook (Event reception)
- Qdrant (Vector database for document vector storage and retrieval)
- LangChain components (Document loading, text splitting, vector storage, retrieval chain)
- Ollama Embedding Model (Text vectorization)
- Google Gemini Chat Model (Natural language generation)
- HTTP Request Nodes (Calling Bitrix24 REST API)
- n8n Native Nodes (Webhook, function, conditional logic, merging, response handling, etc.)
Target Users and Value
- Enterprise IT and automation developers: Quickly build intelligent chatbots integrated with Bitrix24 to enhance automation levels.
- Customer service teams: Use intelligent bots to automatically answer common questions, reducing manual workload.
- Knowledge managers: Build intelligent Q&A systems based on internal documents using RAG technology.
- Technical demonstrators and learners: Understand and practice RAG chatbot workflow design based on n8n and LangChain.
- Enterprises and developers needing multi-system and multi-model integration for intelligent dialogue and document retrieval.
This workflow example demonstrates how to combine Bitrix24 Open Channel events with advanced RAG chatbot technology to achieve intelligent automated Q&A and customer interaction, significantly improving business response efficiency and user experience. Through flexible n8n workflow design, it supports high customization and scalability, suitable for rapid deployment across diverse enterprise scenarios.
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