This example demonstrates how to use LlamaIndex to create a document index and query it using OpenAI's GPT model via LangDB.

• Python 3.8+
• Access to LangDB.ai API
• LangDB.ai API key

pip install llama-index openai

- Create a data directory in the same folder as main.py and add your documents there.

The main.py script:

1. Initializes LlamaIndex with LangDB.ai configuration
2. Loads documents from the data directory consisting of langdb.ai features
3. Creates a vector store index from the documents
4. Persists the index to disk in a storage directory
5. Creates a query engine to interact with the index
6. Performs a sample query about LangDB.ai features

1. Place your documents in the data directory
2. Run the script:

python main.py

• SimpleDirectoryReader: Loads documents from a directory
• VectorStoreIndex: Creates and manages document embeddings
• OpenAI: LLM configuration for queries
• query_engine: Interface for querying the indexed documents

The script will print the response to the query "what are the features of langdb.ai?" based on the content of your indexed documents.