Use the API server

The LlamaEdge RAG API server provides an API endpoint /create/rag that takes a text file, segments it into small chunks, turns the chunks into embeddings (i.e., vectors), and then stores the embeddings into the Qdrant database. It provides an easy way to quick generate embeddings from a body text into a Qdrant database collection.

Prerequisites

You will need to follow this guide to start a Qdrant database instance and a local llama-api-server.wasm server.

Delete the default collection if it exists.

curl -X DELETE 'http://localhost:6333/collections/default'

Step by step example

In this example, we will use a text document paris.txt, and simply submit it to the LlamaEdge API server.

curl -LO https://huggingface.co/datasets/gaianet/paris/raw/main/paris.txt

curl -X POST http://127.0.0.1:8080/v1/create/rag -F "file=@paris.txt"

Now, the Qdrant database has a vector collection called default which contains embeddings from the Paris guide. You can see the stats of the vector collection as follows.

curl 'http://localhost:6333/collections/default'

Of course, the /create/rag API is rather primitive in chunking documents and creating embeddings. For many use cases, you should create your own embedding vectors.

The /create/rag is a simple combination of several more basic API endpoints provided by the API server. You can learn more about them in the developer guide.

Have fun!