0b18ac6577
* auto model context limit detection for ollama llm provider * auto model context limit detection for lmstudio llm provider * Patch Ollama to function and sync context windows like Foundry * normalize how model context windows are cached from endpoint service todo: move this into global utility class with MODEL_MAP eager load models on boot to pre-cache them add performance model improvements into ollama agent as well as apply n_ctx * remove debug log --------- Co-authored-by: Timothy Carambat <rambat1010@gmail.com>
103 lines
2.4 KiB
JavaScript
103 lines
2.4 KiB
JavaScript
const OpenAI = require("openai");
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const Provider = require("./ai-provider.js");
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const InheritMultiple = require("./helpers/classes.js");
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const UnTooled = require("./helpers/untooled.js");
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const {
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LMStudioLLM,
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parseLMStudioBasePath,
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} = require("../../../AiProviders/lmStudio/index.js");
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/**
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* The agent provider for the LMStudio.
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*/
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class LMStudioProvider extends InheritMultiple([Provider, UnTooled]) {
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model;
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/**
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*
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* @param {{model?: string}} config
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*/
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constructor(config = {}) {
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super();
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const model =
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config?.model || process.env.LMSTUDIO_MODEL_PREF || "Loaded from Chat UI";
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const client = new OpenAI({
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baseURL: parseLMStudioBasePath(process.env.LMSTUDIO_BASE_PATH),
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apiKey: null,
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maxRetries: 3,
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});
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this._client = client;
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this.model = model;
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this.verbose = true;
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}
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get client() {
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return this._client;
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}
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get supportsAgentStreaming() {
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return true;
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}
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async #handleFunctionCallChat({ messages = [] }) {
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await LMStudioLLM.cacheContextWindows();
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return await this.client.chat.completions
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.create({
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model: this.model,
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messages,
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})
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.then((result) => {
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if (!result.hasOwnProperty("choices"))
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throw new Error("LMStudio chat: No results!");
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if (result.choices.length === 0)
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throw new Error("LMStudio chat: No results length!");
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return result.choices[0].message.content;
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})
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.catch((_) => {
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return null;
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});
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}
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async #handleFunctionCallStream({ messages = [] }) {
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await LMStudioLLM.cacheContextWindows();
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return await this.client.chat.completions.create({
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model: this.model,
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stream: true,
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messages,
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});
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}
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async stream(messages, functions = [], eventHandler = null) {
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return await UnTooled.prototype.stream.call(
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this,
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messages,
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functions,
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this.#handleFunctionCallStream.bind(this),
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eventHandler
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);
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}
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async complete(messages, functions = []) {
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return await UnTooled.prototype.complete.call(
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this,
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messages,
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functions,
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this.#handleFunctionCallChat.bind(this)
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);
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}
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/**
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* Get the cost of the completion.
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*
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* @param _usage The completion to get the cost for.
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* @returns The cost of the completion.
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* Stubbed since LMStudio has no cost basis.
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*/
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getCost(_usage) {
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return 0;
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}
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}
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module.exports = LMStudioProvider;
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