Using MongoDB Atlas as a Vector Store with OpenAI Embeddings

This project illustrates how to leverage MongoDB as a vector store for performing similarity searches, utilizing OpenAI embeddings within a Go application. It integrates the LangChainGo library, OpenAI's API, and MongoDB to create an efficient vector database for semantic search.

For more information on getting started with MongoDB Atlas, visit the MongoDB Atlas Getting Started Guide. You can also use the following Docker image to containerize a free (M0) tier: MongoDB Atlas Local.

What This Tutorial Covers

1. MongoDB Setup:

   • Connects to a MongoDB Atlas instance using a specified connection string.
   • Automatically checks for and creates a vector search index on the collection if it is not already present, ensuring compatibility with OpenAI's embedding model.

2. OpenAI Embeddings Initialization:

   • Establishes an embeddings client through the OpenAI API.
   • Requires the OpenAI API key to be set as an environment variable for authentication.

3. Creating the Vector Store:

   • Connects to the MongoDB database and sets up a vector store that utilizes OpenAI embeddings for document representation.

4. Inserting Sample Data:

   • Adds a collection of documents (cities) along with their metadata into the vector store.
   • Each document contains information such as the city name, population, and area.

5. Executing Similarity Searches:

   • Demonstrates various types of similarity searches.

Running the Example

1. Configure your environment by setting the MongoDB URI and OpenAI API key:

   bashCopy

   export MONGODB_URI=<your_mongodb_uri>
   export OPENAI_API_KEY=<your_openai_api_key>
   
   

2. If you want to run this using docker-compose.yml, MONGODB_URI should be mongodb://localhost:27017/?directConnection=true: docker-compose up -d

3. Run the program: go run mongovector_vectorstore_example.go