🚀 Agentic RAG – Smarter AI Retrieval with LLM Agents

Welcome to Agentic RAG, an intelligent retrieval system that doesn’t just fetch data—it thinks before retrieving. Unlike traditional RAG, which passively pulls from a vector database, Agentic RAG dynamically decides whether to:

✅ Retrieve stored knowledge (Qdrant)
✅ Perform a real-time web search (Tavily)

This prevents outdated responses, reduces hallucinations, and improves efficiency for LLM-powered applications.

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📌 Features

• 🔍 Hybrid Retrieval – Chooses between stored embeddings & live web search dynamically.
• 🧠 Decision-Making AI Agent – Knows when to use Qdrant vs. search the web.
• ⚡ Faster & Smarter – Reduces unnecessary API calls, optimizing response time & accuracy.
• 🛠️ Prevents AI Hallucinations – Avoids guessing by fetching fresh data when needed.
• 📓 Hands-on Colab Notebook – Try it out without setup hassles.

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🔧 Installation

1️⃣ Install Dependencies

pip install --upgrade langchain langchain_community pypdf langchain-huggingface google-generativeai qdrant-client

2️⃣ Clone This Repo

git clone https://github.com/devjothish/AgenticRAG.git
cd AgenticRAG

3️⃣ Set Up API Keys

To run Agentic RAG, you need API keys for Qdrant (vector search) and Tavily (web search).

import os
os.environ["QDRANT_API_KEY"] = "your-qdrant-api-key"
os.environ["TAVILY_API_KEY"] = "your-tavily-api-key"

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🚀 How It Works

1️⃣ Store Knowledge in Qdrant (Vector DB)

Qdrant stores documents in vector format for fast similarity-based retrieval.

from qdrant_client import QdrantClient

qdrant = QdrantClient(
    url="https://your-qdrant-instance.com",
    api_key=os.getenv("QDRANT_API_KEY")
)

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2️⃣ Define Retrieval Tools

The agent chooses between vector search (Qdrant) and live web search (Tavily).

from langchain.tools import Tool

# Qdrant Vector Search Tool
vector_search_tool = Tool(
    name="VectorStore",
    func=lambda query: vectorstore.similarity_search(query, k=3),
    description="Use this tool when the query is related to stored documents."
)

# Tavily Web Search Tool
web_search_tool = Tool(
    name="WebSearch",
    func=web_search,
    description="Use this tool when the query requires fresh or real-time information."
)

tools = [vector_search_tool, web_search_tool]

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3️⃣ Create the AI Agent

The agent decides dynamically whether to retrieve from Qdrant or use Tavily for fresh information.

from langchain.agents import AgentExecutor

agent_executor = AgentExecutor(
    agent=chain,   # The LLM-powered decision-maker
    tools=tools,   # Vector search & web search tools
    handle_parsing_errors=True,  
    verbose=True   
)

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4️⃣ Run Queries & See It in Action

query1 = "What is Agentic RAG?"
response1 = agent_executor.invoke({"input": query1})
print(response1)

query2 = "Who won the latest NBA game?"
response2 = agent_executor.invoke({"input": query2})
print(response2)

🔹 If the query matches stored knowledge → The agent retrieves from Qdrant.
🔹 If the query requires real-time data → The agent searches the web using Tavily.

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🎯 Live Demo

🟢 Try the hands-on Colab Notebook → Click Here

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📞 Contact

👨‍💻 Jothiswaran Arumugam
🔗 GitHub: github.com/devjothish
💬 Twitter: @devjothish

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Happy Building! 🚀