MongoDB Vector Search Tool

MongoDBVectorSearchTool

Description

Perform vector similarity queries on MongoDB Atlas collections. Supports index creation helpers and bulk insert of embedded texts.

MongoDB Atlas supports native vector search. Learn more: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/

Installation

Install with the MongoDB extra:

pip install crewai-tools[mongodb]

or

uv add crewai-tools --extra mongodb

Parameters

Initialization

• connection_string (str, required)
• database_name (str, required)
• collection_name (str, required)
• vector_index_name (str, default vector_index)
• text_key (str, default text)
• embedding_key (str, default embedding)
• dimensions (int, default 1536)

Run Parameters

• query (str, required): Natural language query to embed and search.

Quick start

from crewai_tools import MongoDBVectorSearchTool

tool = MongoDBVectorSearchTool(
  connection_string="mongodb+srv://...",
  database_name="mydb",
  collection_name="docs",
)

print(tool.run(query="how to create vector index"))

Index creation helpers

Use create_vector_search_index(...) to provision an Atlas Vector Search index with the correct dimensions and similarity.

Common issues

• Authentication failures: ensure your Atlas IP Access List allows your runner and the connection string includes credentials.
• Index not found: create the vector index first; name must match vector_index_name.
• Dimensions mismatch: align embedding model dimensions with dimensions.

More examples

Basic initialization

from crewai_tools import MongoDBVectorSearchTool

tool = MongoDBVectorSearchTool(
    database_name="example_database",
    collection_name="example_collection",
    connection_string="<your_mongodb_connection_string>",
)

Custom query configuration

from crewai_tools import MongoDBVectorSearchConfig, MongoDBVectorSearchTool

query_config = MongoDBVectorSearchConfig(limit=10, oversampling_factor=2)
tool = MongoDBVectorSearchTool(
    database_name="example_database",
    collection_name="example_collection",
    connection_string="<your_mongodb_connection_string>",
    query_config=query_config,
    vector_index_name="my_vector_index",
)

rag_agent = Agent(
    name="rag_agent",
    role="You are a helpful assistant that can answer questions with the help of the MongoDBVectorSearchTool.",
    goal="...",
    backstory="...",
    tools=[tool],
)

Preloading the database and creating the index

import os
from crewai_tools import MongoDBVectorSearchTool

tool = MongoDBVectorSearchTool(
    database_name="example_database",
    collection_name="example_collection",
    connection_string="<your_mongodb_connection_string>",
)

# Load text content from a local folder and add to MongoDB
texts = []
for fname in os.listdir("knowledge"):
    path = os.path.join("knowledge", fname)
    if os.path.isfile(path):
        with open(path, "r", encoding="utf-8") as f:
            texts.append(f.read())

tool.add_texts(texts)

# Create the Atlas Vector Search index (e.g., 3072 dims for text-embedding-3-large)
tool.create_vector_search_index(dimensions=3072)

Example

from crewai import Agent, Task, Crew
from crewai_tools import MongoDBVectorSearchTool

tool = MongoDBVectorSearchTool(
    connection_string="mongodb+srv://...",
    database_name="mydb",
    collection_name="docs",
)

agent = Agent(
    role="RAG Agent",
    goal="Answer using MongoDB vector search",
    backstory="Knowledge retrieval specialist",
    tools=[tool],
    verbose=True,
)

task = Task(
    description="Find relevant content for 'indexing guidance'",
    expected_output="A concise answer citing the most relevant matches",
    agent=agent,
)

crew = Crew(
    agents=[agent],
    tasks=[task],
    verbose=True,
)

result = crew.kickoff()