langchaingo/examples/google-cloudsql-vectorstore-example

Google Cloud SQL Vector Store Example

This example demonstrates how to use Cloud SQL for Postgres for vector similarity search with LangChain in Go.

What This Example Does

1. Creates a Cloud SQL VectorStore:

   • Initializes the cloudsql.PostgresEngine object to establish a connection to the Cloud SQL database.
   • Initializes a new table to store embeddings.
   • Initializes a cloudsql.VectorStore object using a VertexAI model for embeddings.

2. Initializes VertexAI Embeddings:

   • Creates an embeddings client using the VertexAI API.

3. Adds Sample Documents:

   • Inserts several documents (cities) with metadata into the vector store.
   • Each document includes the city name, population, and area.

4. Performs Similarity Searches:

   • Basic search for documents similar to "Japan".
   • Customized search for documents using filters by metadata.

How to Run the Example

1. Set the following environment variables:

   export PROJECT_ID=<your project Id>
   export GOOGLE_CLOUD_LOCATION=<your cloud location>
   export POSTGRES_USERNAME=<your user>
   export POSTGRES_PASSWORD=<your password>
   export POSTGRES_REGION=<your region>
   export POSTGRES_INSTANCE=<your instance>
   export POSTGRES_DATABASE=<your database>
   export POSTGRES_TABLE=<your tablename>
   

2. Run the Go example:

   go run google_cloudsql_vectorstore_example.go
   

Key Features

• This example demonstrates how to use cloudsql.PostgresEngine for connection pooling.
• It shows how to integrate with VertexAI embeddings models.
• Run the code to add documents and perform a similarity search with cloudsql.VectorStore.
• Demonstrates how to filter through the metadata added by using key value pairs.

This example provides a practical demonstration of using vector databases for semantic search and similarity matching, which can be incredibly useful for various AI and machine learning applications.