Azure Cosmos DB MongoDB vCore Vector Database

Use Azure Cosmos DB MongoDB vCore as a vector database for your Knowledge Base.

Setup

Follow the instructions in the Azure Cosmos DB Setup Guide to get the connection string.

Install MongoDB packages:

uv pip install "pymongo[srv]"

Example

agent_with_knowledge.py

import urllib.parse
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.mongodb import MongoVectorDb

# 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 = Knowledge(
    vector_db=MongoVectorDb(
        collection_name="recipes",
        db_url=mdb_connection_string,
        search_index_name="recipes",
        cosmos_compatibility=True,
    ),
)

knowledge.insert(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)

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

Azure Cosmos DB MongoDB vCore Params

Parameter	Type	Description	Default
collection_name	str	Name of the MongoDB collection	Required
name	Optional[str]	Name of the vector database	None
description	Optional[str]	Description of the vector database	None
id	Optional[str]	Unique identifier for the vector database	Auto-generated
db_url	Optional[str]	MongoDB connection string	"mongodb://localhost:27017/"
database	str	Database name	"agno"
embedder	Optional[Embedder]	Embedder instance for generating embeddings	OpenAIEmbedder()
distance_metric	str	Distance metric for similarity	Distance.cosine
overwrite	bool	Overwrite existing collection and index if True	False
wait_until_index_ready_in_seconds	Optional[float]	Time in seconds to wait until the index is ready	3
wait_after_insert_in_seconds	Optional[float]	Time in seconds to wait after inserting documents	3
max_pool_size	int	Maximum number of connections in the connection pool	100
retry_writes	bool	Whether to retry write operations	True
client	Optional[MongoClient]	An existing MongoClient instance	None
search_index_name	Optional[str]	Name of the search index	"vector_index_1"
cosmos_compatibility	Optional[bool]	Whether to use Azure Cosmos DB MongoDB vCore compatibility mode	False
search_type	SearchType	The search type to use when searching for documents	SearchType.vector
hybrid_vector_weight	float	Default weight for vector search results in hybrid search	0.5
hybrid_keyword_weight	float	Default weight for keyword search results in hybrid search	0.5
hybrid_rank_constant	int	Default rank constant (k) for Reciprocal Rank Fusion in hybrid search	60