Dmitry Ng
29a1abdab3
- Updated go.mod and go.sum files across multiple example projects to reflect the latest dependency versions. - Introduced new example scripts for various use cases, enhancing the repository's functionality and usability. - Cleaned up and optimized existing example configurations for better performance and maintainability.
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:
-
Setting up OpenAI Embeddings: The example uses OpenAI's embedding model to convert text into vector representations.
-
Creating a Pinecone Vector Store: It shows how to initialize a Pinecone vector store with custom configurations.
-
Adding Documents: The code adds several documents (cities) to the vector store, each with its own metadata (population and area).
-
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/vxcontrol/langchaingolibrary 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:
- Set up your OpenAI API key as an environment variable (
OPENAI_API_KEY). - 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! 💻🌟