Code
agentic_rag.py
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"""This cookbook shows how to implement Agentic RAG using Hybrid Search and Reranking.
1. Run: `uv 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_basics/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.ainsert(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
Export your API keys
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export ANTHROPIC_API_KEY=your_anthropic_api_key_here
export CO_API_KEY=your_cohere_api_key_here