Build Your Own RAG Pipeline for Intelligent Chatbots
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Automate the creation of your custom Retrieval Augmented Generation (RAG) pipeline. Ingest documents, embed them, and build an intelligent chatbot capable of answering questions based on your own data.
About This Workflow
This workflow empowers you to construct a powerful Retrieval Augmented Generation (RAG) pipeline for your AI applications. It streamlines the process of ingesting documents, transforming them into queryable data, and then leveraging an AI agent to answer questions based on that data. The workflow begins with a simple form submission to upload files, which are then processed using a default data loader and a recursive text splitter. These text chunks are embedded using Ollama and stored in a Qdrant vector store. Simultaneously, a chat trigger initiates an AI agent, powered by an Ollama chat model and enhanced with memory, that can query the vector store to retrieve relevant information and formulate intelligent responses.
Key Features
- Automated Data Ingestion: Easily upload documents (PDFs) to fuel your RAG system.
- Intelligent Text Splitting: Optimized chunking for effective semantic search.
- Vector Database Integration: Seamlessly store and retrieve data using Qdrant.
- AI-Powered Chatbot: Build a conversational agent that answers questions based on your data.
- Local LLM Support: Leverages Ollama for local embedding and chat models.
How To Use
- Configure Data Ingestion: Use the 'On form submission' node to define how users will upload their data (e.g., PDF files).
- Set up Vector Store: Configure the 'Qdrant Vector Store' nodes to connect to your Qdrant instance and define your collection name (e.g., 'rag_collection').
- Integrate Embeddings: Set up the 'Embeddings Ollama' nodes to connect to your local Ollama service and select your preferred embedding model (e.g., 'mxbai-embed-large:latest').
- Configure Chatbot Trigger: Set up the 'When chat message received' node to define how users will interact with your chatbot.
- Define AI Agent: Configure the 'AI Agent' node with a system message to guide its behavior. Ensure the 'Ollama Chat Model' and 'Simple Memory' nodes are correctly connected to the AI Agent for conversational capabilities.
- Enable Retrieval Tool: Configure the second 'Qdrant Vector Store' node as a retrieval tool for the AI Agent.
Apps Used
Workflow JSON
{
"id": "29c7ed99-f9a1-4c70-90ee-8bb205bb6432",
"name": "Build Your Own RAG Pipeline for Intelligent Chatbots",
"nodes": 8,
"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: 29c7ed99-f9a1...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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