Coordinated Agentic RAG

"""
Coordinated Agentic RAG
=======================

Demonstrates coordinated team search, analysis, and synthesis over shared knowledge.
"""

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.openai import OpenAIResponses
from agno.team import Team
from agno.vectordb.lancedb import LanceDb, SearchType

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
knowledge = Knowledge(
    vector_db=LanceDb(
        uri="tmp/lancedb",
        table_name="agno_docs_team",
        search_type=SearchType.hybrid,
        embedder=CohereEmbedder(id="embed-v4.0"),
        reranker=CohereReranker(model="rerank-v3.5"),
    ),
)

knowledge.insert_many(urls=["https://docs.agno.com/agents/overview.md"])

# ---------------------------------------------------------------------------
# Create Members
# ---------------------------------------------------------------------------
knowledge_searcher = Agent(
    name="Knowledge Searcher",
    model=OpenAIResponses(id="gpt-5.2"),
    role="Search and retrieve relevant information from the knowledge base",
    knowledge=knowledge,
    search_knowledge=True,
    instructions=[
        "You are responsible for searching the knowledge base thoroughly.",
        "Find all relevant information for the user's query.",
        "Provide detailed search results with context and sources.",
        "Focus on comprehensive information retrieval.",
    ],
    markdown=True,
)

content_analyzer = Agent(
    name="Content Analyzer",
    model=OpenAIResponses(id="gpt-5.2"),
    role="Analyze and synthesize retrieved content",
    instructions=[
        "Analyze the content provided by the Knowledge Searcher.",
        "Extract key concepts, relationships, and important details.",
        "Identify gaps or areas needing additional clarification.",
        "Organize information logically for synthesis.",
    ],
    markdown=True,
)

response_synthesizer = Agent(
    name="Response Synthesizer",
    model=OpenAIResponses(id="gpt-5.2"),
    role="Create final comprehensive response with proper citations",
    instructions=[
        "Synthesize information from team members into a comprehensive response.",
        "Include proper source citations and references.",
        "Ensure accuracy and completeness of the final answer.",
        "Structure the response clearly with appropriate formatting.",
    ],
    markdown=True,
)

# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
coordinated_rag_team = Team(
    name="Coordinated RAG Team",
    model=OpenAIResponses(id="gpt-5.2"),
    members=[knowledge_searcher, content_analyzer, response_synthesizer],
    instructions=[
        "Work together to provide comprehensive responses using the knowledge base.",
        "Knowledge Searcher: First search for relevant information thoroughly.",
        "Content Analyzer: Then analyze and organize the retrieved content.",
        "Response Synthesizer: Finally create a well-structured response with sources.",
        "Ensure all responses include proper citations and are factually accurate.",
    ],
    show_members_responses=True,
    markdown=True,
)


# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
def main() -> None:
    print("Coordinated Agentic RAG Team Demo")
    print("=" * 50)

    query = "What are Agents and how do they work with tools and knowledge?"
    coordinated_rag_team.print_response(query, stream=True)


if __name__ == "__main__":
    main()