Milvus Database

"""
Milvus Database
===============

Demonstrates Milvus-backed knowledge with sync, async, and async-batching flows.

Set the URI/token for your Milvus deployment. For local development, `/tmp/milvus.db`
uses Milvus Lite.
"""

import asyncio

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.milvus import Milvus


# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
def create_sync_knowledge() -> tuple[Knowledge, Milvus]:
    vector_db = Milvus(collection="recipes", uri="/tmp/milvus.db")
    knowledge = Knowledge(
        name="My Milvus Knowledge Base",
        description="This is a knowledge base that uses a Milvus DB",
        vector_db=vector_db,
    )
    return knowledge, vector_db


def create_async_knowledge(enable_batch: bool = False) -> Knowledge:
    if enable_batch:
        vector_db = Milvus(
            collection="recipe_documents",
            uri="http://localhost:19530",
            embedder=OpenAIEmbedder(enable_batch=True),
        )
    else:
        vector_db = Milvus(
            collection="recipes",
            uri="/tmp/milvus.db",
        )
    return Knowledge(vector_db=vector_db)


# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
def create_sync_agent(knowledge: Knowledge) -> Agent:
    return Agent(knowledge=knowledge)


def create_async_agent(knowledge: Knowledge, enable_batch: bool = False) -> Agent:
    if enable_batch:
        return Agent(
            model=OpenAIChat(id="gpt-4o"),
            knowledge=knowledge,
            search_knowledge=True,
            read_chat_history=True,
        )
    return Agent(knowledge=knowledge)


# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
def run_sync() -> None:
    knowledge, vector_db = create_sync_knowledge()
    knowledge.insert(
        name="Recipes",
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
        metadata={"doc_type": "recipe_book"},
    )

    agent = create_sync_agent(knowledge)
    agent.print_response("How to make Tom Kha Gai", markdown=True)

    vector_db.delete_by_name("Recipes")
    vector_db.delete_by_metadata({"doc_type": "recipe_book"})


async def run_async(enable_batch: bool = False) -> None:
    knowledge = create_async_knowledge(enable_batch=enable_batch)
    agent = create_async_agent(knowledge, enable_batch=enable_batch)

    if enable_batch:
        await knowledge.ainsert(path="cookbook/07_knowledge/testing_resources/cv_1.pdf")
        await agent.aprint_response(
            "What can you tell me about the candidate and what are his skills?",
            markdown=True,
        )
    else:
        await knowledge.ainsert(
            url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
        )
        await agent.aprint_response("How to make Tom Kha Gai", markdown=True)


if __name__ == "__main__":
    run_sync()
    asyncio.run(run_async(enable_batch=False))
    asyncio.run(run_async(enable_batch=True))

# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/07_knowledge/vector_db/milvus_db

# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate

python milvus_db.py