219 lines
8.6 KiB
JavaScript
219 lines
8.6 KiB
JavaScript
"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.GeminiClient = exports.VertexOpenAIClient = void 0;
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exports.createLLM = createLLM;
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exports.toOAITools = toOAITools;
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const google_auth_library_1 = require("google-auth-library");
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const genai_1 = require("@google/genai");
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const uuid_1 = require("uuid");
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// ---------------------------------------------------------------------------
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// Vertex AI OpenAI-compatible client
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// Used for: zai-org/glm-5-maas, anthropic/claude-sonnet-4-6, etc.
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// ---------------------------------------------------------------------------
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let _cachedToken = '';
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let _tokenExpiry = 0;
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// Build GoogleAuth with explicit service account credentials when available.
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// GCP_SA_KEY_BASE64: base64-encoded service account JSON key — safe to pass as
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// an env var since it contains no newlines or special shell characters.
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// Falls back to the GCP metadata server (works on VMs with correct scopes).
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function buildGoogleAuth() {
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const b64Key = process.env.GCP_SA_KEY_BASE64;
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if (b64Key) {
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try {
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const jsonStr = Buffer.from(b64Key, 'base64').toString('utf8');
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const credentials = JSON.parse(jsonStr);
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return new google_auth_library_1.GoogleAuth({ credentials, scopes: ['https://www.googleapis.com/auth/cloud-platform'] });
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}
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catch {
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console.warn('[llm] GCP_SA_KEY_BASE64 is set but failed to decode/parse — falling back to metadata server');
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}
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}
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return new google_auth_library_1.GoogleAuth({ scopes: ['https://www.googleapis.com/auth/cloud-platform'] });
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}
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const _googleAuth = buildGoogleAuth();
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async function getVertexToken() {
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const now = Date.now();
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if (_cachedToken && now < _tokenExpiry)
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return _cachedToken;
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const client = await _googleAuth.getClient();
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const tokenResponse = await client.getAccessToken();
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_cachedToken = tokenResponse.token;
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_tokenExpiry = now + 55 * 60 * 1000; // tokens last 1hr, refresh at 55min
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return _cachedToken;
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}
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class VertexOpenAIClient {
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constructor(modelId, opts) {
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this.modelId = modelId;
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this.projectId = opts?.projectId ?? process.env.GCP_PROJECT_ID ?? 'master-ai-484822';
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this.region = opts?.region ?? 'global';
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this.temperature = opts?.temperature ?? 0.3;
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}
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async chat(messages, tools, maxTokens = 4096) {
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const token = await getVertexToken();
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const base = this.region === 'global'
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? 'https://aiplatform.googleapis.com'
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: `https://${this.region}-aiplatform.googleapis.com`;
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const url = `${base}/v1/projects/${this.projectId}/locations/${this.region}/endpoints/openapi/chat/completions`;
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const body = {
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model: this.modelId,
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messages,
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max_tokens: maxTokens,
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temperature: this.temperature,
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stream: false
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};
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if (tools && tools.length > 0) {
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body.tools = tools;
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body.tool_choice = 'auto';
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}
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const res = await fetch(url, {
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method: 'POST',
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headers: {
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'Authorization': `Bearer ${token}`,
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'Content-Type': 'application/json'
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},
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body: JSON.stringify(body)
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});
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if (!res.ok) {
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const errText = await res.text();
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// Force token refresh on 401
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if (res.status === 401)
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_tokenExpiry = 0;
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throw new Error(`Vertex API ${res.status}: ${errText.slice(0, 400)}`);
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}
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const data = await res.json();
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const choice = data.choices?.[0];
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const message = choice?.message ?? {};
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return {
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content: message.content ?? null,
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reasoning: message.reasoning_content ?? null,
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tool_calls: message.tool_calls ?? [],
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finish_reason: choice?.finish_reason ?? 'stop',
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usage: data.usage
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};
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}
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}
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exports.VertexOpenAIClient = VertexOpenAIClient;
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// ---------------------------------------------------------------------------
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// Gemini client via @google/genai SDK
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// Used for: Tier A (fast/cheap routing, summaries, log parsing)
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// Converts to/from OpenAI message format internally.
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// ---------------------------------------------------------------------------
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class GeminiClient {
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constructor(modelId = 'gemini-2.5-flash', opts) {
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this.modelId = modelId;
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this.temperature = opts?.temperature ?? 0.2;
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}
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async chat(messages, tools, maxTokens = 8192) {
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const apiKey = process.env.GOOGLE_API_KEY;
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if (!apiKey)
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throw new Error('GOOGLE_API_KEY not set');
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const genai = new genai_1.GoogleGenAI({ apiKey });
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const systemMsg = messages.find(m => m.role === 'system');
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const nonSystem = messages.filter(m => m.role !== 'system');
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const functionDeclarations = (tools ?? []).map(t => ({
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name: t.function.name,
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description: t.function.description,
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parameters: t.function.parameters
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}));
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const response = await genai.models.generateContent({
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model: this.modelId,
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contents: toGeminiContents(nonSystem),
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config: {
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systemInstruction: systemMsg?.content ?? undefined,
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tools: functionDeclarations.length > 0 ? [{ functionDeclarations }] : undefined,
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temperature: this.temperature,
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maxOutputTokens: maxTokens
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}
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});
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const candidate = response.candidates?.[0];
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if (!candidate)
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throw new Error('No response from Gemini');
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const parts = candidate.content?.parts ?? [];
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const textContent = parts.filter(p => p.text).map(p => p.text).join('') || null;
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const fnCalls = parts.filter(p => p.functionCall);
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const tool_calls = fnCalls.map(p => ({
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id: `call_${(0, uuid_1.v4)().replace(/-/g, '').slice(0, 12)}`,
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type: 'function',
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function: {
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name: p.functionCall.name ?? '',
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arguments: JSON.stringify(p.functionCall.args ?? {})
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}
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}));
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return {
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content: textContent,
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reasoning: null,
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tool_calls,
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finish_reason: fnCalls.length > 0 ? 'tool_calls' : 'stop'
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};
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}
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}
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exports.GeminiClient = GeminiClient;
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/** Convert OpenAI message format → Gemini Content[] format */
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function toGeminiContents(messages) {
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const contents = [];
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for (const msg of messages) {
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if (msg.role === 'assistant') {
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const parts = [];
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if (msg.content)
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parts.push({ text: msg.content });
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for (const tc of msg.tool_calls ?? []) {
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parts.push({
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functionCall: {
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name: tc.function.name,
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args: JSON.parse(tc.function.arguments || '{}')
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}
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});
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}
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contents.push({ role: 'model', parts });
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}
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else if (msg.role === 'tool') {
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// Parse content back — could be JSON or plain text
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let resultValue = msg.content;
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try {
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resultValue = JSON.parse(msg.content ?? 'null');
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}
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catch { /* keep as string */ }
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contents.push({
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role: 'user',
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parts: [{
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functionResponse: {
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name: msg.name ?? 'tool',
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response: { result: resultValue }
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}
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}]
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});
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}
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else {
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contents.push({ role: 'user', parts: [{ text: msg.content ?? '' }] });
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}
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}
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return contents;
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}
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const TIER_MODELS = {
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A: process.env.TIER_A_MODEL ?? 'gemini-2.5-flash',
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B: process.env.TIER_B_MODEL ?? 'zai-org/glm-5-maas',
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C: process.env.TIER_C_MODEL ?? 'zai-org/glm-5-maas'
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};
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function createLLM(modelOrTier, opts) {
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const modelId = (modelOrTier === 'A' || modelOrTier === 'B' || modelOrTier === 'C')
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? TIER_MODELS[modelOrTier]
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: modelOrTier;
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if (modelId.startsWith('gemini-')) {
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return new GeminiClient(modelId, opts);
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}
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return new VertexOpenAIClient(modelId, { temperature: opts?.temperature });
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}
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// ---------------------------------------------------------------------------
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// Helper — convert our ToolDefinition[] → LLMTool[] (OpenAI format)
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// ---------------------------------------------------------------------------
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function toOAITools(tools) {
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return tools.map(t => ({
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type: 'function',
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function: {
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name: t.name,
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description: t.description,
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parameters: t.parameters
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}
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}));
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}
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