Agentic RAG with Hybrid Search and Reranking

This example demonstrates how to implement Agentic RAG using Hybrid Search and Reranking with LanceDB, Cohere embeddings, and Cohere reranking for enhanced document retrieval and response generation.

Code

agentic_rag.py

"""This cookbook shows how to implement Agentic RAG using Hybrid Search and Reranking.
1. Run: `pip install agno anthropic cohere lancedb tantivy sqlalchemy` to install the dependencies
2. Export your ANTHROPIC_API_KEY and CO_API_KEY
3. Run: `python cookbook/agent_concepts/agentic_search/agentic_rag.py` to run the agent
"""

import asyncio

from agno.agent import Agent
from agno.knowledge.embedder.cohere import CohereEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reranker.cohere import CohereReranker
from agno.models.anthropic import Claude
from agno.vectordb.lancedb import LanceDb, SearchType

knowledge = Knowledge(
    # Use LanceDB as the vector database, store embeddings in the `agno_docs` table
    vector_db=LanceDb(
        uri="tmp/lancedb",
        table_name="agno_docs",
        search_type=SearchType.hybrid,
        embedder=CohereEmbedder(id="embed-v4.0"),
        reranker=CohereReranker(model="rerank-v3.5"),
    ),
)

asyncio.run(
    knowledge.add_content_async(url="https://docs.agno.com/introduction/agents.md")
)

agent = Agent(
    model=Claude(id="claude-3-7-sonnet-latest"),
    # Agentic RAG is enabled by default when `knowledge` is provided to the Agent.
    knowledge=knowledge,
    # search_knowledge=True gives the Agent the ability to search on demand
    # search_knowledge is True by default
    search_knowledge=True,
    instructions=[
        "Include sources in your response.",
        "Always search your knowledge before answering the question.",
    ],
    markdown=True,
)

if __name__ == "__main__":
    agent.print_response("What are Agents?", stream=True)

Usage

1. Create a virtual environment

   Open the Terminal and create a python virtual environment.

   Mac

   python3 -m venv .venv
   source .venv/bin/activate
   
2. Install libraries

   pip install -U agno anthropic cohere lancedb tantivy sqlalchemy
   
3. Export your ANTHROPIC API key

   Mac/Linux

     export ANTHROPIC_API_KEY="your_anthropic_api_key_here"
   
4. Create a Python file

   Create a Python file and add the above code.

   touch agentic_rag.py
   
5. Run Agent

   Mac

   python agentic_rag.py
   
6. Find All Cookbooks

   Explore all the available cookbooks in the Agno repository. Click the link below to view the code on GitHub:

   Agno Cookbooks on GitHub