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const OpenAI = require("openai");
/**
* @type {import("openai").OpenAI}
*/
const client = new OpenAI({
baseURL: "http://localhost:3001/api/v1/openai",
apiKey: "ENTER_ANYTHINGLLM_API_KEY_HERE",
});
(async () => {
// Models endpoint testing.
console.log("Fetching /models");
const modelList = await client.models.list();
for await (const model of modelList) {
console.log({ model });
}
// Test sync chat completion
console.log("Running synchronous chat message");
const syncCompletion = await client.chat.completions.create({
messages: [
{
role: "system",
content: "You are a helpful assistant who only speaks like a pirate.",
},
{ role: "user", content: "What is AnythingLLM?" },
// {
// role: 'assistant',
// content: "Arrr, matey! AnythingLLM be a fine tool fer sailin' the treacherous sea o' information with a powerful language model at yer helm. It's a potent instrument to handle all manner o' tasks involvin' text, like answerin' questions, generating prose, or even havin' a chat with digital scallywags like meself. Be there any specific treasure ye seek in the realm o' AnythingLLM?"
// },
// { role: "user", content: "Why are you talking like a pirate?" },
],
model: "anythingllm", // must be workspace-slug
});
console.log(syncCompletion.choices[0]);
// Test sync chat streaming completion
console.log("Running asynchronous chat message");
const asyncCompletion = await client.chat.completions.create({
messages: [
{
role: "system",
content: "You are a helpful assistant who only speaks like a pirate.",
},
{ role: "user", content: "What is AnythingLLM?" },
],
model: "anythingllm", // must be workspace-slug
stream: true,
});
let message = "";
for await (const chunk of asyncCompletion) {
message += chunk.choices[0].delta.content;
console.log({ message });
}
// Test embeddings creation
console.log("Creating embeddings");
const embedding = await client.embeddings.create({
model: null, // model is optional for AnythingLLM
input: "This is a test string for embedding",
encoding_format: "float",
});
console.log("Embedding created successfully:");
console.log(`Dimensions: ${embedding.data[0].embedding.length}`);
console.log(
`First few values:`,
embedding.data[0].embedding.slice(0, 5),
`+ ${embedding.data[0].embedding.length - 5} more`
);
// Vector DB functionality
console.log("Fetching /vector_stores");
const vectorDBList = await client.beta.vectorStores.list();
for await (const db of vectorDBList) {
console.log(db);
}
})();
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