Press n or j to go to the next uncovered block, b, p or k for the previous block.
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | 1x 4x 4x 4x 4x 4x 4x 4x 4x | import { AIProvider, AIResponse, ChatOptions, Message } from '../core/interfaces';
export class LMStudioProvider implements AIProvider {
readonly name = 'LM Studio (Local)';
readonly type = 'lmstudio' as const;
readonly modelName = 'local-model';
constructor(
private readonly endpoint: string,
private readonly timeout: number
) {}
async isAvailable(): Promise<boolean> {
try {
const resp = await fetch(`${this.endpoint}/v1/models`, {
signal: AbortSignal.timeout(2000),
});
return resp.ok;
} catch {
return false;
}
}
async chat(messages: Message[], options: ChatOptions = {}): Promise<AIResponse> {
const body = {
messages: options.systemPrompt
? [{ role: 'system', content: options.systemPrompt }, ...messages]
: messages,
max_tokens: options.maxTokens ?? 4096,
temperature: options.temperature ?? 0.1,
};
const resp = await fetch(`${this.endpoint}/v1/chat/completions`, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(body),
signal: AbortSignal.timeout(this.timeout),
});
Iif (!resp.ok) {
const err = await resp.text();
throw new Error(`LM Studio ${resp.status}: ${err}`);
}
const data = await resp.json() as {
choices: Array<{ message: { content: string } }>;
model?: string;
usage?: { prompt_tokens: number; completion_tokens: number };
};
return {
content: data.choices[0]?.message?.content ?? '',
model: data.model ?? 'local',
usage: {
promptTokens: data.usage?.prompt_tokens ?? 0,
completionTokens: data.usage?.completion_tokens ?? 0,
},
};
}
async complete(prompt: string, options?: ChatOptions): Promise<string> {
const resp = await this.chat([{ role: 'user', content: prompt }], options);
return resp.content;
}
}
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