✨🚀 Enroll now for "Live Building Agentic AI & Generative AI Applications" starting Jan 25—Contact us for details! 🚀✨Contact Us

Building an End-to-End Agentic AI RAG System with Langflow and DataStax Vector DB

Hello everyone! This is Krish, and today we're going to explore something really exciting - building an end-to-end Agentic AI application using Langflow, a powerful low-code framework, integrated with DataStax Vector DB for RAG (Retrieval Augmented Generation) capabilities.

Introduction to Langflow

Langflow is a revolutionary low-code tool for developers that simplifies the process of building powerful AI agents and workflows. Key features include:

  • Support for multiple API models and databases
  • Drag-and-drop interface for building complex workflows
  • Integration with various LLM providers
  • Built-in vector database support
  • Agent creation and management capabilities

Project Overview: Recipe Generator with RAG

In this tutorial, we'll build a recipe generator that combines:

  • Vector database retrieval for existing recipes
  • Web search capabilities for unknown recipes
  • Multi-agent communication for comprehensive responses

Setting Up the Workflow

1. Basic Components

  • Chat Input: For user queries
  • Chat Output: For displaying responses
  • Recipe Chef Agent: Main agent handling the workflow

2. Agent Configuration


# Agent Configuration
Agent Name: Recipe Chef
Model Provider: Groq
Instructions: "You are a helpful agent that can use tools to answer questions and perform tasks"
API Integration: Groq API Key (Required)
    

Implementing RAG with Astra DB

Vector Database Setup

  • Database Provider: DataStax Astra DB
  • Embedding Model: Astra Vectorizer/NVIDIA
  • Collection Creation for recipe storage

Document Processing Pipeline


# RAG Pipeline Components
1. File Upload (PDF)
2. Text Splitter
   - Type: Split Text
   - Parameters: Chunk size, overlap
3. Vector Storage
   - Database: Astra DB
   - Embeddings: Astra Vectorizer
4. Data Parser
   - Convert JSON responses to text
    

Creating the Multi-Agent System

Workflow Components

  • Primary Agent (Recipe Chef): Manages overall workflow
  • RAG Integration: Queries vector database
  • Web Search Fallback: Uses DuckDuckGo for missing information

Prompt Template


Your goal is to answer the question asked from the company database.
Use RAG to fetch the details and provide the most relevant output.
Question: {question}
In your response incorporate the recipe points: {results}
    

Implementing Fallback Mechanism

The system follows this logic flow:

  1. Receive user query
  2. Search Vector Database (Astra DB)
  3. If no results found, trigger DuckDuckGo web search
  4. Combine and format results
  5. Return comprehensive response

Testing the System

Example Queries


# Query 1 (RAG Database)
Input: "What is the direction to make Som Tum?"
Result: [Retrieves from Vector DB]

# Query 2 (Web Search Fallback)
Input: "How to prepare chicken biryani?"
Result: [Retrieves from Web Search]
    

Advantages of Using Langflow

  • Visual workflow creation
  • Minimal coding required
  • Easy integration with multiple tools
  • Built-in support for various LLMs
  • Flexible agent creation and management

Best Practices and Tips

  • Always configure proper API keys
  • Test both RAG and fallback mechanisms
  • Monitor system performance
  • Start with simple workflows before complex implementations

Conclusion

Langflow provides an intuitive way to build complex AI applications without extensive coding. By combining RAG capabilities with multi-agent systems, we can create powerful applications that leverage both stored knowledge and real-time web information.

Next Steps

  • Explore additional LLM integrations
  • Implement more complex agent interactions
  • Add custom knowledge bases
  • Optimize response times and accuracy