Intelligent Chatbot for Service & Policy Support
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This n8n workflow orchestrates a powerful AI chatbot capable of understanding user queries and retrieving relevant information from various knowledge bases. It leverages advanced language models and vector databases for efficient and accurate responses.
About This Workflow
This n8n workflow is designed to build a sophisticated AI-powered chatbot experience. It utilizes LangChain nodes to integrate multiple LLM providers and vector stores, allowing for comprehensive knowledge retrieval across different domains like service packages, general services, terms of use, privacy policies, and refund policies. The workflow initiates via a webhook, processes user queries using the OpenRouter chat model with Deepseek, retrieves context from Pinecone vector stores, and maintains conversation history with a Postgres database. The final response is then sent back to the user, providing a seamless and intelligent interaction.
Key Features
- Multi-Vector Store Integration: Connects to multiple Pinecone namespaces for comprehensive data retrieval (Service Packages, Services, Terms of Use, Privacy Policy, Refund Policy).
- Advanced LLM Integration: Utilizes OpenRouter with the Deepseek chat model for sophisticated natural language understanding and generation.
- Persistent Chat Memory: Stores conversation history in a PostgreSQL database for context-aware interactions.
- Flexible Input Trigger: Initiated via a webhook for easy integration into various applications.
- Tool-like Vector Retrieval: Configures Pinecone vector stores to act as tools for efficient information fetching.
How To Use
- Configure Webhook: Set up the n8n webhook node to receive incoming requests (e.g., user queries).
- Initialize Chat Model: Configure the 'OpenRouter Chat Model' node with your desired model and API credentials.
- Set Up Chat Memory: Establish the 'Postgres Chat Memory' node, providing your PostgreSQL connection details and defining a session key based on user identifiers.
- Integrate Vector Stores: For each knowledge domain (Service Packages, Services, Terms of Use, Privacy Policy, Refund Policy):
- Add a 'Pinecone Vector Store' node.
- Set the
modetoretrieve-as-tool. - Specify the correct
pineconeNamespaceand your Pinecone index ID. - Configure the
toolDescriptionto clearly define the purpose of the vector store. - Add a corresponding 'Embeddings Cohere' node to handle embedding generation for retrieval.
- Connect Nodes: Ensure the output of the webhook is passed to the chat model, which then interacts with the vector stores and chat memory.
- Respond to Webhook: Configure the 'Respond to Webhook' node to send the generated AI answer back to the originating request. Ensure the
responseBodycorrectly references the output of the AI processing.
Apps Used
Workflow JSON
{
"id": "703a2873-c1a5-4346-9b3d-ef0809fe9ff2",
"name": "Intelligent Chatbot for Service & Policy Support",
"nodes": 12,
"category": "Operations",
"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: 703a2873-c1a5...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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