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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | import { GoogleGenerativeAI, SchemaType } from "@google/generative-ai";
import type {
AIProvider,
AIAnalysisRequest,
AIProviderResponse,
AIAnalysisResult,
} from "../types";
import { buildAnalysisPrompt } from "../prompts";
import { calculateCost } from "../costCalculator";
export class GoogleProvider implements AIProvider {
private client: GoogleGenerativeAI | null = null;
private modelName: string;
constructor(apiKey?: string, modelName: string = "gemini-2.0-flash") {
if (apiKey) {
this.client = new GoogleGenerativeAI(apiKey);
}
this.modelName = modelName;
}
getName(): string {
return this.modelName;
}
getProvider(): string {
return "google";
}
isAvailable(): boolean {
return this.client !== null;
}
async analyze(request: AIAnalysisRequest): Promise<AIProviderResponse> {
if (!this.client) {
throw new Error("Google AI API key not configured");
}
const prompt = buildAnalysisPrompt(request);
const startTime = Date.now();
try {
const model = this.client.getGenerativeModel({
model: this.modelName,
generationConfig: {
responseMimeType: "application/json",
responseSchema: {
type: SchemaType.OBJECT,
properties: {
insight: {
type: SchemaType.STRING,
description: "A brief, encouraging analysis of their progress (2-3 sentences)",
},
suggestion: {
type: SchemaType.STRING,
description: "One specific, actionable recommendation (1-2 sentences)",
},
sentiment: {
type: SchemaType.STRING,
description: "The overall tone of the check-in: positive, neutral, negative, or mixed",
},
},
required: ["insight", "suggestion", "sentiment"],
},
},
});
const response = await model.generateContent(prompt);
const latencyMs = Date.now() - startTime;
const text = response.response.text();
const result = JSON.parse(text) as AIAnalysisResult;
// Validate required fields
if (!result.insight || !result.sentiment) {
throw new Error("Missing required fields in response");
}
// Google doesn't always provide token counts, estimate based on text length
// Rough estimate: ~4 characters per token
const estimatedPromptTokens = Math.ceil(prompt.length / 4);
const estimatedCompletionTokens = Math.ceil(text.length / 4);
const totalTokens = estimatedPromptTokens + estimatedCompletionTokens;
// Try to get actual token counts if available
const promptTokens = response.response.usageMetadata?.promptTokenCount || estimatedPromptTokens;
const completionTokens = response.response.usageMetadata?.candidatesTokenCount || estimatedCompletionTokens;
const estimatedCost = calculateCost(this.modelName, promptTokens, completionTokens);
return {
result,
metrics: {
promptTokens,
completionTokens,
totalTokens,
latencyMs,
estimatedCost,
},
modelName: this.modelName,
provider: "google",
endpoint: "/v1beta/models/generateContent",
};
} catch (error) {
const latencyMs = Date.now() - startTime;
throw new Error(
`Google AI error after ${latencyMs}ms: ${error instanceof Error ? error.message : String(error)}`
);
}
}
}
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