Qdrant Vector Store Example with LangChain Go

Welcome to this cheerful example of using Qdrant vector store with LangChain Go! 🎉

This example demonstrates how to use the Qdrant vector store to store and search for similar documents using embeddings. It's a great way to get started with vector databases and semantic search in your Go applications!

What This Example Does

1. Sets up OpenAI Embeddings:

   • Creates an embeddings client using the OpenAI API.
   • Make sure you have your OPENAI_API_KEY environment variable set!

2. Creates a Qdrant Vector Store:

   • Connects to your Qdrant instance.
   • Don't forget to replace YOUR_QDRANT_URL and YOUR_COLLECTION_NAME with your actual Qdrant details!

3. Adds Documents:

   • Adds a variety of documents about different locations to the vector store.
   • Each document has some content and metadata (like area).

4. Performs Similarity Searches:

   • Searches for documents similar to "england".
   • Searches for "american places" with a score threshold.
   • Searches for "cities in south america" with both a score threshold and metadata filter.

Cool Features Demonstrated

• Similarity Search: Find documents that are semantically similar to a query.
• Score Threshold: Filter results based on a minimum similarity score.
• Metadata Filtering: Use additional metadata to refine your search results.

How to Run

1. Make sure you have Go installed and your OPENAI_API_KEY set.
2. Replace YOUR_QDRANT_URL and YOUR_COLLECTION_NAME with your Qdrant details.
3. Run the example with go run qdrant_vectorstore_example.go.

Have fun exploring the world of vector databases and semantic search with LangChain Go and Qdrant! 🚀🔍