MongoDB Vector Database

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`

​Setup

​Example

Async Support ⚡

​MongoDB Params

Setup

Example

MongoDB Params