Unlock Intelligent Chatbots with Your Knowledge Base
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Empower your chatbots with your own data! This workflow leverages n8n and Langchain to create intelligent conversational agents that can access and understand your specific knowledge base. Turn static documents into dynamic, conversational AI experiences.
About This Workflow
This n8n workflow revolutionizes how you build AI-powered chatbots by enabling them to tap into your proprietary data. It seamlessly integrates with OpenAI's powerful embedding models to vectorize your content and a chat model (like GPT-4o-mini) to understand and respond to user queries. The workflow processes incoming chat messages, retrieves relevant information from your knowledge base by generating embeddings and performing similarity searches, and uses this context to generate accurate and personalized responses. It also includes chat memory functionality, powered by PostgreSQL, to maintain conversational context and provide a more natural user experience. This allows you to build sophisticated chatbots for customer support, internal knowledge sharing, or any application requiring intelligent data retrieval and interaction.
Key Features
- Custom Knowledge Integration: Easily connect your existing documents and data sources to your chatbot.
- AI-Powered Understanding: Utilizes advanced OpenAI embedding and chat models for intelligent query processing.
- Contextual Conversations: Maintains chat history with PostgreSQL memory for more natural interactions.
- Flexible Data Handling: Supports various content types and extracts key metadata for better data organization.
- Automated Workflow: Triggered by chat messages, automating the process of knowledge retrieval and response generation.
How To Use
- Configure Data Ingestion: Use the
Default Data LoaderandEmbeddings OpenAInodes to process and vectorize your knowledge base content. Ensure metadata liketitle,url, andpublication_dateare correctly mapped. - Implement Text Splitting: Utilize the
Token Splitternode to break down large documents into manageable chunks for effective embedding. - Set Up Chat Interaction: Connect a trigger (e.g.,
When chat message received- assumed from theSet fieldsnode's input) to capture user input and session IDs. - Process User Queries: Use the
Set fieldsnode to format the retrieved documents and prepare them for the LLM, including extracting thechatInputandsessionId. - Generate Responses: Employ the
OpenAI Chat Modelto generate intelligent responses based on the user's query and the vectorized knowledge base. - Maintain Conversation History: Integrate the
Postgres Chat Memorynode to store and retrieve past interactions, enabling contextual conversations. - Respond to User: Configure the
Respond to Webhooknode to send the generated response back to the user or application.
Apps Used
Workflow JSON
{
"id": "ac41deea-355a-4da9-be38-9a2816167458",
"name": "Unlock Intelligent Chatbots with Your Knowledge Base",
"nodes": 29,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: ac41deea-355a...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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