PGVector Store with OpenAI Embeddings Example

This example demonstrates how to use pgvector, a PostgreSQL extension for vector similarity search, with OpenAI embeddings in a Go application. It showcases the integration of langchain-go, OpenAI's API, and pgvector to create a powerful vector database for similarity searches.

What This Example Does

1. Sets up a PostgreSQL Database with pgvector:

   • Uses Docker to run a PostgreSQL instance with the pgvector extension installed.
   • Automatically creates and enables the vector extension when the container starts.

2. Initializes OpenAI Embeddings:

   • Creates an embeddings client using the OpenAI API.
   • Requires an OpenAI API key to be set as an environment variable.

3. Creates a PGVector Store:

   • Establishes a connection to the PostgreSQL database.
   • Initializes a vector store using pgvector and OpenAI embeddings.

4. Adds Sample Documents:

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

5. Performs Similarity Searches:

   • Demonstrates various types of similarity searches: a. Basic search for documents similar to "japan". b. Search for South American cities with a score threshold. c. Search with both score threshold and metadata filtering.

How to Run the Example

1. Start the PostgreSQL database:

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   docker compose up -d
   

2. Set your OpenAI API key:

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   export OPENAI_API_KEY=<your key>
   

3. Run the Go example:

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   go run pgvector_vectorstore_example.go
   

Key Features

• Integration of pgvector with OpenAI embeddings
• Similarity search with score thresholds
• Metadata filtering in vector searches
• Dockerized PostgreSQL setup for easy deployment

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.