mirror of
https://github.com/hellodigua/ChatLab.git
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feat: AI支持配置Gemini
This commit is contained in:
digua
@@ -0,0 +1,519 @@
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/**
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* Google Gemini LLM Provider
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* 使用 Gemini REST API 格式，支持 Function Calling
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*/
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import type {
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ILLMService,
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LLMProvider,
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ChatMessage,
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ChatOptions,
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ChatResponse,
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ChatStreamChunk,
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ProviderInfo,
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ToolCall,
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ToolDefinition,
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} from './types'
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import { aiLogger } from '../logger'
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const DEFAULT_BASE_URL = 'https://generativelanguage.googleapis.com'
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const MODELS = [
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{ id: 'gemini-3-flash-preview', name: 'Gemini 3 Flash Preview', description: '高速预览版' },
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{ id: 'gemini-3-pro-preview', name: 'Gemini 3 Pro Preview', description: '专业预览版' },
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]
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export const GEMINI_INFO: ProviderInfo = {
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id: 'gemini',
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name: 'Gemini',
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description: 'Google Gemini 大语言模型',
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defaultBaseUrl: DEFAULT_BASE_URL,
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models: MODELS,
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}
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// ==================== Gemini API 类型定义 ====================
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/** Gemini 消息 part（支持多种类型） */
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interface GeminiPart {
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text?: string
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functionCall?: {
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name: string
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args: Record<string, unknown>
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}
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functionResponse?: {
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name: string
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response: unknown
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}
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/** Gemini 3+ 模型的思考签名 */
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thoughtSignature?: string
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}
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/** Gemini 消息内容 */
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interface GeminiContent {
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role: 'user' | 'model'
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parts: GeminiPart[]
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}
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/** Gemini 函数声明（对应 OpenAI 的 ToolDefinition） */
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interface GeminiFunctionDeclaration {
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name: string
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description: string
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parameters: {
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type: string
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properties: Record<string, unknown>
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required?: string[]
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}
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}
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/** Gemini 请求体 */
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interface GeminiRequest {
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contents: GeminiContent[]
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generationConfig?: {
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temperature?: number
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maxOutputTokens?: number
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}
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systemInstruction?: {
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parts: Array<{ text: string }>
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}
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tools?: Array<{
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functionDeclarations: GeminiFunctionDeclaration[]
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}>
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}
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/** Gemini 响应候选项 */
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interface GeminiCandidate {
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content?: {
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parts?: GeminiPart[]
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role?: string
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}
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finishReason?: string
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}
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/** Gemini API 响应 */
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interface GeminiResponse {
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candidates?: GeminiCandidate[]
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usageMetadata?: {
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promptTokenCount?: number
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candidatesTokenCount?: number
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totalTokenCount?: number
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}
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}
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// ==================== GeminiService 类 ====================
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export class GeminiService implements ILLMService {
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private apiKey: string
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private baseUrl: string
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private model: string
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constructor(apiKey: string, model?: string, baseUrl?: string) {
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this.apiKey = apiKey
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this.baseUrl = baseUrl || DEFAULT_BASE_URL
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this.model = model || 'gemini-3-flash-preview'
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}
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getProvider(): LLMProvider {
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return 'gemini'
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}
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getModels(): string[] {
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return MODELS.map((m) => m.id)
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}
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getDefaultModel(): string {
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return 'gemini-3-flash-preview'
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}
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/**
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* 将 OpenAI 格式的 tools 转换为 Gemini 格式
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*/
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private convertTools(tools?: ToolDefinition[]): Array<{ functionDeclarations: GeminiFunctionDeclaration[] }> | undefined {
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if (!tools || tools.length === 0) return undefined
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const functionDeclarations: GeminiFunctionDeclaration[] = tools.map((tool) => ({
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name: tool.function.name,
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description: tool.function.description,
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parameters: tool.function.parameters,
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}))
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return [{ functionDeclarations }]
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}
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/**
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* 将 OpenAI 格式消息转换为 Gemini 格式
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*/
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private convertMessages(messages: ChatMessage[]): {
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contents: GeminiContent[]
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systemInstruction?: { parts: Array<{ text: string }> }
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} {
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const contents: GeminiContent[] = []
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let systemInstruction: { parts: Array<{ text: string }> } | undefined
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for (const msg of messages) {
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if (msg.role === 'system') {
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// Gemini 使用 systemInstruction 处理系统提示
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systemInstruction = {
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parts: [{ text: msg.content }],
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}
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} else if (msg.role === 'user') {
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contents.push({
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role: 'user',
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parts: [{ text: msg.content }],
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})
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} else if (msg.role === 'assistant') {
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// 处理 assistant 消息（可能包含 tool_calls）
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if (msg.tool_calls && msg.tool_calls.length > 0) {
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// 有工具调用的 assistant 消息
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const parts: GeminiPart[] = []
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if (msg.content) {
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parts.push({ text: msg.content })
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}
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for (const tc of msg.tool_calls) {
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const part: GeminiPart = {
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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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// Gemini 3+ 需要包含 thoughtSignature
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if (tc.thoughtSignature) {
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part.thoughtSignature = tc.thoughtSignature
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}
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parts.push(part)
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}
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contents.push({ role: 'model', parts })
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} else {
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// 普通文本消息
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contents.push({
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role: 'model',
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parts: [{ text: msg.content }],
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})
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}
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} else if (msg.role === 'tool') {
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// 工具结果消息 - 在 Gemini 中作为 user 角色的 functionResponse
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// 注意：需要从消息内容解析工具名称和结果
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// tool_call_id 格式通常是 "call_xxx"，我们需要从上下文获取工具名
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// 这里简化处理：假设内容是 JSON 格式的结果
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try {
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const result = JSON.parse(msg.content)
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// 尝试从上一条 assistant 消息中找到对应的 tool_call
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// 由于 Gemini 需要 name，我们从 tool_call_id 推断或使用默认值
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contents.push({
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role: 'user',
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parts: [
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{
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functionResponse: {
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name: msg.tool_call_id?.replace('call_', '') || 'unknown',
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response: result,
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},
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},
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],
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})
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} catch {
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// 如果不是 JSON，直接作为文本结果
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contents.push({
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role: 'user',
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parts: [
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{
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functionResponse: {
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name: msg.tool_call_id?.replace('call_', '') || 'unknown',
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response: { result: msg.content },
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},
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},
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],
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})
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}
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}
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}
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return { contents, systemInstruction }
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}
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/**
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* 构建 API URL
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*/
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private buildUrl(stream: boolean): string {
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const action = stream ? 'streamGenerateContent' : 'generateContent'
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const base = this.baseUrl.replace(/\/$/, '')
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return `${base}/v1beta/models/${this.model}:${action}?key=${this.apiKey}`
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}
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/**
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* 从 Gemini parts 中提取工具调用
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*/
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private extractToolCalls(parts?: GeminiPart[]): ToolCall[] | undefined {
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if (!parts) return undefined
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const toolCalls: ToolCall[] = []
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for (const part of parts) {
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if (part.functionCall) {
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toolCalls.push({
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id: `call_${part.functionCall.name}_${Date.now()}`,
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type: 'function',
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function: {
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name: part.functionCall.name,
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arguments: JSON.stringify(part.functionCall.args),
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},
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// 保存 Gemini 3+ 的思考签名
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thoughtSignature: part.thoughtSignature,
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})
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}
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}
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return toolCalls.length > 0 ? toolCalls : undefined
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}
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/**
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* 从 Gemini parts 中提取文本内容
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*/
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private extractText(parts?: GeminiPart[]): string {
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if (!parts) return ''
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return parts
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.filter((p) => p.text)
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.map((p) => p.text)
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.join('')
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}
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async chat(messages: ChatMessage[], options?: ChatOptions): Promise<ChatResponse> {
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const { contents, systemInstruction } = this.convertMessages(messages)
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const requestBody: GeminiRequest = {
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contents,
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generationConfig: {
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temperature: options?.temperature ?? 0.7,
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maxOutputTokens: options?.maxTokens ?? 2048,
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},
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}
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if (systemInstruction) {
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requestBody.systemInstruction = systemInstruction
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}
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// 添加工具定义
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const geminiTools = this.convertTools(options?.tools)
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if (geminiTools) {
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requestBody.tools = geminiTools
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}
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const response = await fetch(this.buildUrl(false), {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify(requestBody),
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signal: options?.abortSignal,
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})
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if (!response.ok) {
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const error = await response.text()
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throw new Error(`Gemini API error: ${response.status} - ${error}`)
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}
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const data: GeminiResponse = await response.json()
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const candidate = data.candidates?.[0]
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const parts = candidate?.content?.parts
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const content = this.extractText(parts)
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const toolCalls = this.extractToolCalls(parts)
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// 解析 finish_reason
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let finishReason: ChatResponse['finishReason'] = 'error'
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const reason = candidate?.finishReason
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if (reason === 'STOP') {
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finishReason = toolCalls ? 'tool_calls' : 'stop'
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} else if (reason === 'MAX_TOKENS') {
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finishReason = 'length'
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}
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return {
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content,
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finishReason,
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tool_calls: toolCalls,
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usage: data.usageMetadata
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? {
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promptTokens: data.usageMetadata.promptTokenCount || 0,
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completionTokens: data.usageMetadata.candidatesTokenCount || 0,
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totalTokens: data.usageMetadata.totalTokenCount || 0,
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}
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: undefined,
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}
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}
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async *chatStream(messages: ChatMessage[], options?: ChatOptions): AsyncGenerator<ChatStreamChunk> {
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const { contents, systemInstruction } = this.convertMessages(messages)
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const requestBody: GeminiRequest = {
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contents,
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generationConfig: {
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temperature: options?.temperature ?? 0.7,
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maxOutputTokens: options?.maxTokens ?? 2048,
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},
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}
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if (systemInstruction) {
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requestBody.systemInstruction = systemInstruction
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}
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// 添加工具定义
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const geminiTools = this.convertTools(options?.tools)
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if (geminiTools) {
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requestBody.tools = geminiTools
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}
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// Gemini 流式需要添加 alt=sse 参数
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const url = this.buildUrl(true) + '&alt=sse'
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aiLogger.info('Gemini', '开始流式请求', {
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url: url.replace(/key=[^&]+/, 'key=***'),
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model: this.model,
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messagesCount: contents.length,
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hasSystemInstruction: !!systemInstruction,
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hasTools: !!geminiTools,
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})
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const response = await fetch(url, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify(requestBody),
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signal: options?.abortSignal,
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})
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if (!response.ok) {
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const error = await response.text()
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aiLogger.error('Gemini', 'API 请求失败', { status: response.status, error: error.slice(0, 500) })
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throw new Error(`Gemini API error: ${response.status} - ${error}`)
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}
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aiLogger.info('Gemini', 'API 响应成功，开始读取流')
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const reader = response.body?.getReader()
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if (!reader) {
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throw new Error('Failed to get response reader')
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}
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const decoder = new TextDecoder()
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let buffer = ''
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// 用于累积工具调用（可能跨多个 chunk）
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const toolCallsAccumulator: ToolCall[] = []
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try {
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while (true) {
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// 检查是否已中止
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if (options?.abortSignal?.aborted) {
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yield { content: '', isFinished: true, finishReason: 'stop' }
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return
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}
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const { done, value } = await reader.read()
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if (done) break
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buffer += decoder.decode(value, { stream: true })
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const lines = buffer.split('\n')
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buffer = lines.pop() || ''
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for (const line of lines) {
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const trimmed = line.trim()
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if (!trimmed || !trimmed.startsWith('data: ')) continue
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const data = trimmed.slice(6)
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if (data === '[DONE]') {
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if (toolCallsAccumulator.length > 0) {
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yield { content: '', isFinished: true, finishReason: 'tool_calls', tool_calls: toolCallsAccumulator }
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} else {
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yield { content: '', isFinished: true, finishReason: 'stop' }
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}
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return
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}
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try {
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const parsed: GeminiResponse = JSON.parse(data)
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const candidate = parsed.candidates?.[0]
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const parts = candidate?.content?.parts
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// 处理文本内容
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const text = this.extractText(parts)
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if (text) {
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yield { content: text, isFinished: false }
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}
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// 处理工具调用
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const toolCalls = this.extractToolCalls(parts)
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if (toolCalls) {
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aiLogger.info('Gemini', '检测到工具调用', { toolCalls: toolCalls.map((tc) => tc.function.name) })
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toolCallsAccumulator.push(...toolCalls)
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}
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// 检查是否完成
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const finishReason = candidate?.finishReason
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if (finishReason) {
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aiLogger.info('Gemini', '流式响应完成', { finishReason, toolCallsCount: toolCallsAccumulator.length })
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if (toolCallsAccumulator.length > 0) {
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yield { content: '', isFinished: true, finishReason: 'tool_calls', tool_calls: toolCallsAccumulator }
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} else {
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let reason: ChatStreamChunk['finishReason'] = 'stop'
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if (finishReason === 'MAX_TOKENS') {
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reason = 'length'
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}
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yield { content: '', isFinished: true, finishReason: reason }
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}
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return
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}
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} catch (e) {
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// 记录解析错误
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aiLogger.warn('Gemini', 'SSE 数据解析失败', { data: data.slice(0, 200), error: String(e) })
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}
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}
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}
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// 如果循环正常结束，发送完成信号
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if (toolCallsAccumulator.length > 0) {
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yield { content: '', isFinished: true, finishReason: 'tool_calls', tool_calls: toolCallsAccumulator }
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} else {
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yield { content: '', isFinished: true, finishReason: 'stop' }
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}
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} catch (error) {
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// 如果是中止错误，正常返回
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if (error instanceof Error && error.name === 'AbortError') {
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yield { content: '', isFinished: true, finishReason: 'stop' }
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return
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}
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throw error
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} finally {
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reader.releaseLock()
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||||
}
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}
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||||
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async validateApiKey(): Promise<{ success: boolean; error?: string }> {
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||||
try {
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||||
// 使用 models.list API 验证 API Key
|
||||
const url = `${this.baseUrl.replace(/\/$/, '')}/v1beta/models?key=${this.apiKey}`
|
||||
|
||||
const response = await fetch(url, {
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||||
method: 'GET',
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||||
})
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||||
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||||
if (response.ok) {
|
||||
return { success: true }
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||||
}
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||||
|
||||
// 尝试获取错误详情
|
||||
const errorText = await response.text()
|
||||
let errorMessage = `HTTP ${response.status}`
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||||
try {
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||||
const errorJson = JSON.parse(errorText)
|
||||
errorMessage = errorJson.error?.message || errorJson.message || errorMessage
|
||||
} catch {
|
||||
if (errorText) {
|
||||
errorMessage = errorText.slice(0, 200)
|
||||
}
|
||||
}
|
||||
return { success: false, error: errorMessage }
|
||||
} catch (error) {
|
||||
const errorMessage = error instanceof Error ? error.message : String(error)
|
||||
return { success: false, error: errorMessage }
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -21,6 +21,7 @@ import type {
|
||||
import { MAX_CONFIG_COUNT } from './types'
|
||||
import { DeepSeekService, DEEPSEEK_INFO } from './deepseek'
|
||||
import { QwenService, QWEN_INFO } from './qwen'
|
||||
import { GeminiService, GEMINI_INFO } from './gemini'
|
||||
import { OpenAICompatibleService, OPENAI_COMPATIBLE_INFO } from './openai-compatible'
|
||||
import { aiLogger } from '../logger'
|
||||
|
||||
@@ -72,6 +73,7 @@ const KIMI_INFO: ProviderInfo = {
|
||||
export const PROVIDERS: ProviderInfo[] = [
|
||||
DEEPSEEK_INFO,
|
||||
QWEN_INFO,
|
||||
GEMINI_INFO,
|
||||
MINIMAX_INFO,
|
||||
GLM_INFO,
|
||||
KIMI_INFO,
|
||||
@@ -325,6 +327,8 @@ export function createLLMService(config: ExtendedLLMConfig): ILLMService {
|
||||
return new DeepSeekService(config.apiKey, config.model, config.baseUrl)
|
||||
case 'qwen':
|
||||
return new QwenService(config.apiKey, config.model, config.baseUrl)
|
||||
case 'gemini':
|
||||
return new GeminiService(config.apiKey, config.model, config.baseUrl)
|
||||
// 新增的官方API都使用 OpenAI 兼容格式
|
||||
case 'minimax':
|
||||
case 'glm':
|
||||
|
||||
@@ -5,7 +5,7 @@
|
||||
/**
|
||||
* 支持的 LLM 提供商
|
||||
*/
|
||||
export type LLMProvider = 'deepseek' | 'qwen' | 'minimax' | 'glm' | 'kimi' | 'openai-compatible'
|
||||
export type LLMProvider = 'deepseek' | 'qwen' | 'minimax' | 'glm' | 'kimi' | 'gemini' | 'openai-compatible'
|
||||
|
||||
/**
|
||||
* LLM 配置
|
||||
@@ -105,6 +105,8 @@ export interface ToolCall {
|
||||
name: string
|
||||
arguments: string // JSON 字符串
|
||||
}
|
||||
/** Gemini 3+ 模型需要的思考签名（用于工具调用验证） */
|
||||
thoughtSignature?: string
|
||||
}
|
||||
|
||||
/**
|
||||
|
||||
@@ -42,6 +42,9 @@ export function getAvailableYears(sessionId: string): number[] {
|
||||
* 获取成员活跃度排行
|
||||
*/
|
||||
export function getMemberActivity(sessionId: string, filter?: TimeFilter): any[] {
|
||||
// 先确保数据库有 avatar 字段（兼容旧数据库）
|
||||
ensureAvatarColumn(sessionId)
|
||||
|
||||
const db = openDatabase(sessionId)
|
||||
if (!db) return []
|
||||
|
||||
|
||||
Reference in New Issue
Block a user