How do I create an API to interact with LlamaIndex?

To create an API that interacts with LlamaIndex, you’ll need to design endpoints that handle data ingestion, indexing, and querying. Start by setting up a basic web framework like Flask or FastAPI. These tools let you define routes (API endpoints) and integrate LlamaIndex’s core functionality. For example, a /query endpoint could accept natural language questions, pass them to LlamaIndex’s query engine, and return results. Use LlamaIndex’s built-in components, such as VectorStoreIndex or SimpleDirectoryReader, to load and index data from sources like documents or databases. Ensure your API handles authentication, rate limiting, and error responses for production use.

Next, focus on the core logic for integrating LlamaIndex. Suppose your API needs to index documents stored in a directory. You could create a /build-index endpoint that triggers LlamaIndex’s data loading and indexing process. For instance, using SimpleDirectoryReader, you might load PDFs or text files, generate vector embeddings, and store the index in a directory like ./storage. The API could then load this prebuilt index when handling queries, reducing latency. If your use case requires real-time data updates, design an /update-index endpoint to refresh the index when new data is added. Use asynchronous tasks or background workers for time-consuming operations like indexing large datasets to avoid blocking API requests.

Finally, implement the query handling. A /search endpoint might accept a user’s question, such as {"query": "What are the key points from the report?"}, and use LlamaIndex’s QueryEngine to retrieve answers. You can customize the query engine with parameters like response length, summarization, or source citation. For example, configure the engine to include metadata about the source documents in responses. To improve scalability, consider caching frequent queries or using a distributed system like Redis. Test the API with tools like Postman to ensure endpoints work as expected, and document the API using OpenAPI or Swagger so developers can understand how to interact with it.