Pinecone Vector Store Example

Welcome to this exciting example of using Pinecone as a vector store with LangChain in Go! 🚀

What This Example Does

This example demonstrates how to use Pinecone, a powerful vector database, in conjunction with LangChain to create and query a vector store. Here's a breakdown of the main features:

1. Setting up OpenAI Embeddings: The example uses OpenAI's embedding model to convert text into vector representations.

2. Creating a Pinecone Vector Store: It shows how to initialize a Pinecone vector store with custom configurations.

3. Adding Documents: The code adds several documents (cities) to the vector store, each with its own metadata (population and area).

4. Performing Similarity Searches: The example showcases different types of similarity searches:

   • Basic similarity search
   • Search with a score threshold
   • Search with both a score threshold and metadata filters

Key Points

• The example uses the github.com/tmc/langchaingo library for LangChain functionality in Go.
• It demonstrates how to handle errors and set up the necessary clients and stores.
• The code shows how to use metadata filters to refine search results based on specific criteria.

Running the Example

To run this example, make sure you have:

1. Set up your OpenAI API key as an environment variable (OPENAI_API_KEY).
2. Replaced "YOUR_API_KEY" with your actual Pinecone API key.

This example is a great starting point for anyone looking to implement vector search capabilities in their Go applications using Pinecone and LangChain! 🎉

Happy coding! 💻🌟