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Setup

pip install pymilvus

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Initialize Milvus

Set the uri and token for your Milvus server.

  • If you only need a local vector database for small scale data or prototyping, setting the uri as a local file, e.g../milvus.db, is the most convenient method, as it automatically utilizes Milvus Lite to store all data in this file.
  • If you have large scale data, say more than a million vectors, you can set up a more performant Milvus server on Docker or Kubernetes. In this setup, please use the server address and port as your uri, e.g.http://localhost:19530. If you enable the authentication feature on Milvus, use your_username:your_password as the token, otherwise don’t set the token.
  • If you use Zilliz Cloud, the fully managed cloud service for Milvus, adjust the uri and token, which correspond to the Public Endpoint and API key in Zilliz Cloud.

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Example

agent_with_knowledge.py

from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.milvus import Milvus

vector_db = Milvus(
    collection="recipes",
    uri="./milvus.db",
)
# Create knowledge base
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=vector_db,
)

knowledge_base.load(recreate=False)  # Comment out after first run

# Create and use the agent
agent = Agent(knowledge_base=knowledge_base, use_tools=True, show_tool_calls=True)
agent.print_response("How to make Tom Kha Gai", markdown=True)
agent.print_response("What was my last question?", stream=True)

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Developer Resources

LanceDBPgVector