This example passes
url= to insert_many() and ainsert_many(). Agno v2.7.2 reads the urls argument, so these calls insert no documents. The code fence remains source-exact. Apply the replacement below before running.distributed_rag_with_reranking.py
"""
Distributed RAG With Reranking
==============================
Demonstrates distributed RAG with hybrid retrieval and Cohere reranking.
"""
import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reranker.cohere import CohereReranker
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.utils.print_response.team import aprint_response, print_response
from agno.vectordb.lancedb import LanceDb, SearchType
# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
reranked_knowledge = Knowledge(
vector_db=LanceDb(
table_name="recipes_reranked",
uri="tmp/lancedb",
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
reranker=CohereReranker(model="rerank-v3.5"),
),
)
validation_knowledge = Knowledge(
vector_db=LanceDb(
table_name="recipes_validation",
uri="tmp/lancedb",
search_type=SearchType.vector,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
)
# ---------------------------------------------------------------------------
# Create Members
# ---------------------------------------------------------------------------
initial_retriever = Agent(
name="Initial Retriever",
model=OpenAIResponses(id="gpt-5-mini"),
role="Perform broad initial retrieval to gather candidate information",
knowledge=reranked_knowledge,
search_knowledge=True,
instructions=[
"Perform comprehensive initial retrieval from the knowledge base.",
"Cast a wide net to gather all potentially relevant information.",
"Focus on recall rather than precision in this initial phase.",
"Retrieve diverse content that might be relevant to the query.",
],
markdown=True,
)
reranking_specialist = Agent(
name="Reranking Specialist",
model=OpenAIResponses(id="gpt-5-mini"),
role="Apply advanced reranking to optimize retrieval results",
knowledge=reranked_knowledge,
search_knowledge=True,
instructions=[
"Apply advanced reranking techniques to optimize result relevance.",
"Focus on precision and ranking quality over quantity.",
"Use the Cohere reranker to identify the most relevant content.",
"Prioritize results that best match the user's specific needs.",
],
markdown=True,
)
context_analyzer = Agent(
name="Context Analyzer",
model=OpenAIResponses(id="gpt-5-mini"),
role="Analyze context and relevance of reranked results",
knowledge=validation_knowledge,
search_knowledge=True,
instructions=[
"Analyze the context and relevance of reranked results.",
"Cross-validate information against the validation knowledge base.",
"Assess the quality and accuracy of retrieved content.",
"Identify the most contextually appropriate information.",
],
markdown=True,
)
final_synthesizer = Agent(
name="Final Synthesizer",
model=OpenAIResponses(id="gpt-5-mini"),
role="Synthesize reranked results into optimal comprehensive responses",
instructions=[
"Synthesize information from all team members into optimal responses.",
"Leverage the reranked and analyzed results for maximum quality.",
"Create responses that demonstrate the benefits of advanced reranking.",
"Ensure optimal information organization and presentation.",
"Include confidence levels and source quality indicators.",
],
markdown=True,
)
# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
distributed_reranking_team = Team(
name="Distributed Reranking RAG Team",
model=OpenAIResponses(id="gpt-5-mini"),
members=[
initial_retriever,
reranking_specialist,
context_analyzer,
final_synthesizer,
],
instructions=[
"Work together to provide optimal RAG responses using advanced reranking.",
"Initial Retriever: First perform broad comprehensive retrieval.",
"Reranking Specialist: Apply advanced reranking for result optimization.",
"Context Analyzer: Analyze and validate the reranked results.",
"Final Synthesizer: Create optimal responses from reranked information.",
"Leverage advanced reranking for superior result quality.",
"Demonstrate the benefits of specialized reranking in team coordination.",
],
show_members_responses=True,
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
async def async_reranking_rag_demo() -> None:
"""Demonstrate async distributed reranking RAG processing."""
print("Async Distributed Reranking RAG Demo")
print("=" * 45)
query = "What's the best way to prepare authentic Tom Kha Gai? I want traditional methods and modern variations."
await reranked_knowledge.ainsert_many(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
await validation_knowledge.ainsert_many(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
await aprint_response(input=query, team=distributed_reranking_team)
def sync_reranking_rag_demo() -> None:
"""Demonstrate sync distributed reranking RAG processing."""
print("Distributed Reranking RAG Demo")
print("=" * 35)
query = "What's the best way to prepare authentic Tom Kha Gai? I want traditional methods and modern variations."
reranked_knowledge.insert_many(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
validation_knowledge.insert_many(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
print_response(distributed_reranking_team, query)
def advanced_culinary_demo() -> None:
"""Demonstrate advanced reranking for complex culinary queries."""
print("Advanced Culinary Analysis with Reranking RAG")
print("=" * 55)
query = """I want to understand the science behind Thai curry pastes. Can you explain:
- Traditional preparation methods vs modern techniques
- How different ingredients affect flavor profiles
- Regional variations and their historical origins
- Best practices for storage and usage
- How to adapt recipes for different dietary needs"""
reranked_knowledge.insert_many(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
validation_knowledge.insert_many(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
print_response(distributed_reranking_team, query)
if __name__ == "__main__":
# Choose which demo to run
asyncio.run(async_reranking_rag_demo())
# advanced_culinary_demo()
# sync_reranking_rag_demo()
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno cohere lancedb openai pyarrow pypdf
3
Export your API keys
export CO_API_KEY="your_co_api_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:CO_API_KEY="your_co_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Fix the batch insert arguments
Replace all six
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf" arguments in the saved file with urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"].5
Run the example
Save the code above as
distributed_rag_with_reranking.py, then run:python distributed_rag_with_reranking.py