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Models: Choosing from different LLMs and embedding models

Currently, many different LLMs are emerging. Edgechain offers integrations to a wide range of models and a streamlined interface to all of them. Edgechain differentiates between three types of models that differ in their inputs and outputs:

-LLMs take a string as an input (prompt) and output a string (completion).

public ArkResponse chat(ArkRequest arkRequest) {

OpenAiEndpoint gpt4Endpoint =
new OpenAiEndpoint(
new ExponentialDelay(3, 5, 2, TimeUnit.SECONDS));

String prompt = "Arakoo has a cat. What animal is Arakoo;s pet";

EdgeChain<ChatCompletionResponse> chatChain =
new EdgeChain<>(gpt4Endpoint.chatCompletion(prompt, "ChatChain", arkRequest));

return chatChain.getArkResponse();

-Chat models are similar to LLMs. They take a list of chat messages as input and return a chat message.

-Text embedding models take text input and return a list of floats (embeddings), which are the numerical representation of the input text. Embeddings help extract information from a text. This information can then be later used, e.g., for calculating similarities between texts (e.g., movie summaries).

public ArkResponse embedding(ArkRequest arkRequest) {

OpenAiEndpoint ada002Embeddings =
new OpenAiEndpoint(
new ExponentialDelay(3, 3, 2, TimeUnit.SECONDS));;

String input = "Alice has a parrot. What animal is Alice;s pet";

EdgeChain<ChatCompletionResponse> embeddingChain =
new EdgeChain<>(gpt4Endpoint.embeddings(input, "EmbeddingChianChain", arkRequest));

return embeddingChain.getArkResponse();