RAG Chatbot
Build a powerful RAG chatbot that searches through your documents and provides accurate, grounded responses using AI agents with access to your knowledge base.
Last updated
October 25, 2025
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Key Takeaways
- 2 nodes total - a manual trigger and an AI agent with collection search
- Grounded answers - responses come from your actual documents, not general model knowledge
- Multi-step reasoning - the AI agent can iterate up to 3 times to find the best answer
- Customizable - change the model, adjust the system prompt, or add web search for hybrid RAG
What This Workflow Does
This workflow creates a RAG chatbot that answers questions based on your documents. You upload files to a Needle collection, type a question in the trigger node, and the AI agent searches your collection to return a grounded answer.
Use cases:
- Ask questions about product documentation, policies, or research papers
- Build internal knowledge base search for your team
- Create a support chatbot trained on your FAQ and help articles
How It Works
| Step | What Happens |
|---|---|
| 1. Manual trigger | You enter your question |
| 2. AI agent | Searches your Needle collection and returns an answer from your docs |
Setup Instructions
- Click "Use template" on this page
- Create a Needle collection and upload your documents (PDFs, Word docs, markdown, text files)
- Open the AI agent node and select your collection in the
search_collectiontool - Run the workflow with a test question
Customization
| What You Can Change | How |
|---|---|
| AI model | Change the model in the AI agent node |
| System prompt | Edit the system prompt to set the tone (e.g. "concise research assistant") |
| Max steps | Increase max steps for deeper multi-step reasoning |
| Hybrid RAG | Add the search_web tool to the AI agent so it can search both your docs and the web |
| Temperature | Keep at 0 for factual Q&A, raise to 0.3-0.5 for more creative responses |
FAQ
Q: How many documents can I upload? A: There's no hard limit on collections. Start with 10-20 documents and expand from there.
Q: Does it work with images in PDFs? A: Text extraction works well. For complex diagrams, adding captions in the original document helps.
Q: Can I use this for customer-facing support? A: Yes. Replace the manual trigger with a Gmail, Slack, or Telegram trigger to handle incoming questions automatically.
Q: What file types are supported? A: PDFs, Word docs, markdown files, text files, and Google Docs (via export).
Q: Can I search across multiple collections?
A: Yes. Add multiple search_collection tools to the AI agent node, each pointing to a different collection.
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