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Documentation Index

Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt

Use this file to discover all available pages before exploring further.

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Setup

Follow the instructions in the Azure Cosmos DB Setup Guide to get the connection string. Install MongoDB packages: 
pip install "pymongo[srv]"

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Example

agent_with_knowledge.py
import urllib.parse
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.mongodb import MongoDb

# Azure Cosmos DB MongoDB connection string
"""
Example connection strings:
"mongodb+srv://<username>:<encoded_password>@cluster0.mongocluster.cosmos.azure.com/?tls=true&authMechanism=SCRAM-SHA-256&retrywrites=false&maxIdleTimeMS=120000"
"""
mdb_connection_string = f"mongodb+srv://<username>:<encoded_password>@cluster0.mongocluster.cosmos.azure.com/?tls=true&authMechanism=SCRAM-SHA-256&retrywrites=false&maxIdleTimeMS=120000"

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=MongoDb(
        collection_name="recipes",
        db_url=mdb_connection_string,
        search_index_name="recipes",
        cosmos_compatibility=True,
    ),
)

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

# Create and use the agent
agent = Agent(knowledge=knowledge_base, show_tool_calls=True)
agent.print_response("How to make Thai curry?", markdown=True)

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MongoDB Params

  • collection_name: The name of the collection in the database.
  • db_url: The connection string for the MongoDB database.
  • search_index_name: The name of the search index to use.
  • cosmos_compatibility: Set to True for Azure Cosmos DB compatibility.

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Developer Resources