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

# MongoDB Vector Search

> MongoDB is a cross-platform document-oriented database program.

Langtrace is an [official ecosystem partner of MongoDB](https://cloud.mongodb.com/ecosystem/langtrace-ai).

MongoDB Vector Search is a feature that allows you to store and search for vectors in MongoDB.

## Setup

1. [Register for an Atlas account](https://www.mongodb.com/docs/atlas/tutorial/create-atlas-account/) using your Google Account or an email address.

2. Create and deploy a Free cluster. You can use Atlas Free clusters as a small-scale development environment to host your data. Free clusters never expire, and provide access to a subset of Atlas features.

3. [Connect to your cluster](https://www.mongodb.com/docs/atlas/tutorial/connect-to-your-cluster/) using the mongosh, the Node.js driver, the PyMongo driver, or Compass.

4. [Load the mflix dataset](https://www.mongodb.com/docs/atlas/sample-data/#load-data-into-atlas) into your cluster.

5. Install Langtrace, PyMongo, and OpenAI

```bash theme={null}
pip install langtrace-python-sdk pymongo openai
```

<Note>You'll need API keys from Langtrace and OpenAI. Sign up for [Langtrace](https://langtrace.ai) and [OpenAI](https://platform.openai.com/api-keys) if you haven't done so already.</Note>

## Usage

here's an example of how to use Langtrace with MongoDB:

<CodeGroup>
  ```python Python theme={null}
  from langtrace_python_sdk import langtrace, with_langtrace_root_span
  import pymongo
  import os
  from openai import OpenAI

  langtrace.init(api_key=os.environ["LANGTRACE_API_KEY"])
  openai_client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
  client = pymongo.MongoClient(os.environ["MONGO_URI"])
  MODEL = "text-embedding-ada-002"


  # Define a function to generate embeddings
  def get_embedding(text):
      """Generates vector embeddings for the given text."""
      embedding = (
          openai_client.embeddings.create(input=[text], model=MODEL).data[0].embedding
      )
      return embedding


  @with_langtrace_root_span("mongo-vector-search")
  def vector_query():
      db = client["sample_mflix"]

      embedded_movies_collection = db["embedded_movies"]
      # define pipeline
      pipeline = [
          {
              "$vectorSearch": {
                  "index": "vector_index",
                  "path": "plot_embedding",
                  "queryVector": get_embedding("time travel"),
                  "numCandidates": 150,
                  "limit": 10,
              }
          },
          {
              "$project": {
                  "_id": 0,
                  "plot": 1,
                  "title": 1,
                  "score": {"$meta": "vectorSearchScore"},
              }
          },
      ]

      result = embedded_movies_collection.aggregate(pipeline)
      for doc in result:
          # print(doc)
          pass


  if __name__ == "__main__":
      try:
          vector_query()
      except Exception as e:
          print("error", e)
      finally:
          client.close()

  ```
</CodeGroup>