Game Changer RAG AI Agent for Your Sales Teams - No-Code (FREE n8n Template)

Key Concepts

• RAG (Retrieval-Augmented Generation) AI Agent: An AI agent that combines information retrieval with text generation to answer questions based on a specific knowledge base.
• No-Code Tools: Platforms like Naden and Vectoriz.io that allow users to build complex workflows and AI agents without writing code.
• API Documentation: Technical documentation that describes how to use and integrate with an Application Programming Interface (API).
• Vector Database: A database that stores data as vector embeddings, allowing for efficient similarity searches and retrieval of relevant information.
• Vector Embeddings: Numerical representations of text or other data that capture their semantic meaning, enabling AI models to understand and compare them.
• Naden: A no-code platform for building automated workflows and integrating different applications.
• Vectoriz.io: A platform for building RAG pipelines and managing vector databases.
• Slack Integration: Connecting the AI agent to Slack for easy access and communication.
• Prompt Engineering: Designing effective prompts to guide the AI model in generating accurate and relevant responses.

Building an Intelligent RAG AI Agent for Technical Support

Problem Statement

• Sales and support teams often struggle to answer technical questions related to API documentation and integrations.
• Salespeople get stuck in meetings due to API-related inquiries.
• Support agents waste time searching through documentation for simple answers.

Solution Overview

• Build an intelligent RAG AI agent using no-code tools (Naden and Vectoriz.io).
• Connect the agent to API documentation for automated updates.
• Enable instant answers to technical questions via Slack.

Step-by-Step Process

1. Download the Naden Template:
   • Join the free AI Workshop Light community.
   • Navigate to the classroom section and YouTube resources.
   • Download the Naden blueprint and import it into your Naden workflow.
2. Set up Vectoriz.io Account and RAG Pipeline:
   • Create a free account on Vectoriz.io.
   • Go to RAG pipelines and create a new pipeline.
   • Name the pipeline (e.g., "Slack Naden Agent").
3. Configure the Data Source:
   • Select "Web Crawler" as the source.
   • Add a new container and name it (e.g., "Vectoriz Test").
   • Enter the URL of the API documentation (e.g., docs.vectoriz.io).
4. Configure Extraction and Embedding:
   • Leave the extraction strategy as "Fast" (simple and fast extractor).
   • Keep the chunking strategy as "Default."
   • Select the built-in Vectoriz.io embedding model (OpenAI V3 small).
   • Use the built-in Vectoriz.io vector database.
5. Deploy the RAG Pipeline:
   • Click "Deploy RAG Pipeline" in the top right corner.
   • The system will crawl the URL, grab all pages, and deploy the pipeline.
6. Configure the Naden Workflow:
   • Slack Trigger:
     • Add your Slack account to Naden.
     • Set the trigger to "Bot or App Mentions."
     • Select the appropriate Slack channel.
   • HTTP Request Node:
     • Use a POST request to reach the Vectoriz.io retrieval endpoint (found in Vectoriz.io under "Connect").
     • Set the headers:
       • Authorization: Your Vectoriz.io API token (generate a token in Vectoriz.io).
     • Send a JSON body with the following parameters:
       • question: The question from Slack (drag the text from the Slack trigger).
       • num_results: (Leave as is)
       • re-rank: (Leave as is)
   • AI Agent (Chat Model):
     • Use a chat model like GPT-4.
     • Create a prompt that includes:
       • The user's question from Slack.
       • The helpful content retrieved from Vectoriz.io (stringified JSON document).
       • Instructions for generating a concise and Slack-friendly response.
     • Set the simple memory to event timestamp.
   • Slack Send Message Node:
     • Add your Slack account.
     • Set the resource to "Message" and the operation to "Send."
     • Select the channel (Naden bot channel).
     • Set the message type to "Simple Text Message."
     • Set the text to the AI agent's output.
     • Enable "Reply to Message" and set the timestamp to the Slack event timestamp (event_ts).
7. Test the Integration:
   • Execute the Naden workflow.
   • In Slack, mention the bot (e.g., @NadenBot what is a RAG pipeline?).
   • The bot should respond with an answer and a link to the source documentation.

Vectoriz.io RAG Pipeline Details

• Web Crawler: Used to extract data from API documentation websites.
• Extraction Strategy: "Fast" is suitable for simple text extraction.
• Chunking Strategy: "Default" is generally sufficient.
• Embedding Model: OpenAI V3 small (used by the built-in Vectoriz.io embedding).
• Vector Database: Built-in Vectoriz.io vector database.
• Automatic Updates: The pro version of Vectoriz.io allows scheduling automatic updates to the RAG pipeline (e.g., every 12 hours, daily).

Naden Workflow Details

• Slack Trigger: Listens for bot mentions in a specific Slack channel.
• HTTP Request Node: Sends a request to the Vectoriz.io API to retrieve relevant information.
• AI Agent (Chat Model): Generates a human-readable response based on the retrieved information.
• Slack Send Message Node: Sends the response back to the Slack channel as a reply to the original question.

Benefits and Applications

• Improved Efficiency: Provides instant answers to technical questions, saving time for sales and support teams.
• Increased Accuracy: Ensures that answers are based on up-to-date API documentation.
• Enhanced Customer Satisfaction: Delivers faster and more accurate support to customers.
• Monetization Opportunity: Can be sold as a service to companies that need to improve their technical support capabilities.

Notable Quotes

• "Sales people get stuck in meetings all the time when clients ask them questions about API details."
• "Support agents often waste a lot of time digging through documentation just to answer a simple question."

Conclusion

The video demonstrates how to build a practical and valuable AI agent using no-code tools to address a common problem in sales and support teams. By connecting to API documentation and integrating with Slack, the agent provides instant and accurate answers to technical questions, improving efficiency and customer satisfaction. The presenter also highlights the potential for monetizing this solution by selling it to companies that need to enhance their technical support capabilities.