> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# MongoDB Vector DB

> - Go to https://www.mongodb.com/cloud/atlas/register.

1. Create a MongoDB Atlas Account: - Go to [https://www.mongodb.com/cloud/atlas/register](https://www.mongodb.com/cloud/atlas/register) - Sign up for a free account

```python theme={null}
"""
MongoDB Vector DB
=================

1. Create a MongoDB Atlas Account:
   - Go to https://www.mongodb.com/cloud/atlas/register
   - Sign up for a free account

2. Create a New Cluster:
   - Click "Build a Database"
   - Choose the FREE tier (M0)
   - Select your preferred cloud provider and region
   - Click "Create Cluster"

3. Set Up Database Access:
   - Follow the instructions in the MongoDB Atlas UI
   - Create a username and password
   - Click "Add New Database User"

5. Get Connection String:
   - Select "Drivers" as connection method
   - Select "Python" as driver
   - Copy the connection string

7. Test Connection:
   - Use the connection string in your code
   - Ensure pymongo is installed: uv pip install "pymongo[srv]"
   - Test with a simple query to verify connectivity

Alternatively to test locally, you can run a docker container

docker run -p 27017:27017 -d --name mongodb-container --rm -v ./tmp/mongo-data:/data/db mongodb/mongodb-atlas-local:8.0.3
"""

import asyncio

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.mongodb import MongoVectorDb

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
mdb_connection_string = "mongodb+srv://<username>:<password>@cluster0.mongodb.net/?retryWrites=true&w=majority"


# ---------------------------------------------------------------------------
# Create Knowledge Base
# ---------------------------------------------------------------------------
def create_sync_knowledge() -> Knowledge:
    return Knowledge(
        vector_db=MongoVectorDb(
            collection_name="recipes",
            db_url=mdb_connection_string,
            search_index_name="recipes",
        )
    )


def create_async_knowledge(enable_batch: bool = False) -> Knowledge:
    if enable_batch:
        return Knowledge(
            vector_db=MongoVectorDb(
                collection_name="documents",
                db_url=mdb_connection_string,
                database="agno",
                embedder=OpenAIEmbedder(enable_batch=True),
            )
        )
    return Knowledge(
        vector_db=MongoVectorDb(
            collection_name="recipes",
            db_url=mdb_connection_string,
        )
    )


# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
def create_sync_agent(knowledge: Knowledge) -> Agent:
    return Agent(knowledge=knowledge)


def create_async_agent(knowledge: Knowledge, enable_batch: bool = False) -> Agent:
    if enable_batch:
        return Agent(
            model=OpenAIChat(id="gpt-5.2"),
            knowledge=knowledge,
            search_knowledge=True,
            read_chat_history=True,
        )
    return Agent(knowledge=knowledge)


# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
def run_sync() -> None:
    knowledge = create_sync_knowledge()
    knowledge.insert(
        name="Recipes",
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
        metadata={"doc_type": "recipe_book"},
    )

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


async def run_async(enable_batch: bool = False) -> None:
    knowledge = create_async_knowledge(enable_batch=enable_batch)
    agent = create_async_agent(knowledge, enable_batch=enable_batch)

    if enable_batch:
        await knowledge.ainsert(path="cookbook/07_knowledge/testing_resources/cv_1.pdf")
        await agent.aprint_response(
            "What can you tell me about the candidate and what are his skills?",
            markdown=True,
        )
    else:
        await knowledge.ainsert(
            url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
        )
        await agent.aprint_response("How to make Thai curry?", markdown=True)


if __name__ == "__main__":
    run_sync()
    asyncio.run(run_async(enable_batch=False))
    asyncio.run(run_async(enable_batch=True))
```

## Run the Example

```bash theme={null}
# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/07_knowledge/vector_db/mongo_db

# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate

python mongo_db.py
```