MongoDB Vector Database

Use MongoDB as a vector database for your Knowledge Base.

Setup

Follow the instructions in the MongoDB Setup Guide to get connection string

Install MongoDB packages

uv pip install "pymongo[srv]"

Example

agent_with_knowledge.py

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

# MongoDB Atlas connection string
"""
Example connection strings:
"mongodb+srv://<username>:<password>@cluster0.mongodb.net/?retryWrites=true&w=majority"
"mongodb://localhost/?directConnection=true"
"""
mdb_connection_string = ""

knowledge_base = Knowledge(
    vector_db=MongoVectorDb(
        collection_name="recipes",
        db_url=mdb_connection_string,
        wait_until_index_ready_in_seconds=60,
        wait_after_insert_in_seconds=300
    ),
)  # adjust wait_after_insert_in_seconds and wait_until_index_ready_in_seconds to your needs

if __name__ == "__main__":
    knowledge_base.insert(
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
    )

    agent = Agent(knowledge=knowledge_base)
    agent.print_response("How to make Thai curry?", markdown=True)

Async Support ⚡

MongoDB also supports asynchronous operations, enabling concurrency and leading to better performance.

async_mongodb.py

import asyncio

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

# MongoDB Atlas connection string
"""
Example connection strings:
"mongodb+srv://<username>:<password>@cluster0.mongodb.net/?retryWrites=true&w=majority"
"mongodb://localhost:27017/agno?authSource=admin"
"""
mdb_connection_string = "mongodb+srv://<username>:<password>@cluster0.mongodb.net/?retryWrites=true&w=majority"

knowledge_base = Knowledge(
    vector_db=MongoVectorDb(
        collection_name="recipes",
        db_url=mdb_connection_string,
    ),
)

# Create and use the agent
agent = Agent(knowledge=knowledge_base)

if __name__ == "__main__":
    # Load knowledge base asynchronously
    asyncio.run(knowledge_base.ainsert(
            url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
        )
    )

    # Create and use the agent asynchronously
    asyncio.run(agent.aprint_response("How to make Thai curry?", markdown=True))

Use aload() and aprint_response() methods with asyncio.run() for non-blocking operations in high-throughput applications.

MongoDB 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