anything-llm/server/utils/agents/aibitat/providers/lmstudio.js
Sean Hatfield 8422f92542
Agent support for LLMs with no function calling (#1295)
* add LMStudio agent support (generic) support
"work" with non-tool callable LLMs, highly dependent on system specs

* add comments

* enable few-shot prompting per function for OSS models

* Add Agent support for Ollama models

* azure, groq, koboldcpp agent support complete + WIP togetherai

* WIP gemini agent support

* WIP gemini blocked and will not fix for now

* azure fix

* merge fix

* add localai agent support

* azure untooled agent support

* merge fix

* refactor implementation of several agent provideers

* update bad merge comment

---------

Co-authored-by: timothycarambat <rambat1010@gmail.com>
2024-05-08 15:17:54 -07:00

114 lines
2.9 KiB
JavaScript

const OpenAI = require("openai");
const Provider = require("./ai-provider.js");
const InheritMultiple = require("./helpers/classes.js");
const UnTooled = require("./helpers/untooled.js");
/**
* The provider for the LMStudio provider.
*/
class LMStudioProvider extends InheritMultiple([Provider, UnTooled]) {
model;
constructor(_config = {}) {
super();
const model = process.env.LMSTUDIO_MODEL_PREF || "Loaded from Chat UI";
const client = new OpenAI({
baseURL: process.env.LMSTUDIO_BASE_PATH?.replace(/\/+$/, ""), // here is the URL to your LMStudio instance
apiKey: null,
maxRetries: 3,
});
this._client = client;
this.model = model;
this.verbose = true;
}
get client() {
return this._client;
}
async #handleFunctionCallChat({ messages = [] }) {
return await this.client.chat.completions
.create({
model: this.model,
temperature: 0,
messages,
})
.then((result) => {
if (!result.hasOwnProperty("choices"))
throw new Error("LMStudio chat: No results!");
if (result.choices.length === 0)
throw new Error("LMStudio chat: No results length!");
return result.choices[0].message.content;
})
.catch((_) => {
return null;
});
}
/**
* Create a completion based on the received messages.
*
* @param messages A list of messages to send to the API.
* @param functions
* @returns The completion.
*/
async complete(messages, functions = null) {
try {
let completion;
if (functions.length > 0) {
const { toolCall, text } = await this.functionCall(
messages,
functions,
this.#handleFunctionCallChat.bind(this)
);
if (toolCall !== null) {
this.providerLog(`Valid tool call found - running ${toolCall.name}.`);
this.deduplicator.trackRun(toolCall.name, toolCall.arguments);
return {
result: null,
functionCall: {
name: toolCall.name,
arguments: toolCall.arguments,
},
cost: 0,
};
}
completion = { content: text };
}
if (!completion?.content) {
this.providerLog(
"Will assume chat completion without tool call inputs."
);
const response = await this.client.chat.completions.create({
model: this.model,
messages: this.cleanMsgs(messages),
});
completion = response.choices[0].message;
}
return {
result: completion.content,
cost: 0,
};
} catch (error) {
throw error;
}
}
/**
* Get the cost of the completion.
*
* @param _usage The completion to get the cost for.
* @returns The cost of the completion.
* Stubbed since LMStudio has no cost basis.
*/
getCost(_usage) {
return 0;
}
}
module.exports = LMStudioProvider;