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// import OpenAI from "openai";
import { createUserContent, GoogleGenAI } from "@google/genai";
import type {
CategorizationRequest,
CategorizationResponse,
} from "./categorization";
import { CATEGORIZATION_PROMPT } from "./llm-prompts";
export interface LLMRequestOptions {
model?: string;
temperature?: number;
maxTokens?: number;
}
export class LLMService {
private defaultOptions: LLMRequestOptions = {};
api;
model;
constructor(
// baseURL: string,
apiKey: string,
options: LLMRequestOptions = {},
) {
// const client = new OpenAI({ baseURL, apiKey });
const client = new GoogleGenAI({ apiKey });
this.api = client;
this.model = "gemini-2.5-flash";
// this.defaultOptions = {
// model: "gemini-2.5-flash",
// temperature: 0.3,
// maxTokens: 1000,
// ...options,
// ...options
// };
}
async categorizeBookmark(
request: CategorizationRequest,
options?: LLMRequestOptions,
): Promise<CategorizationResponse> {
const mergedOptions = { ...this.defaultOptions, ...options };
const prompt = this.buildCategorizationPrompt(request);
const media = [];
const allPics = request.bookmark.media.pics;
if (request.bookmark.media.video.thumb)
allPics.push(request.bookmark.media.video.thumb);
for (const pic of allPics) {
const imgdata = await fetch(pic);
const imageArrayBuffer = await imgdata.arrayBuffer();
const base64ImageData = Buffer.from(imageArrayBuffer).toString("base64");
const mimeType = imgdata.headers.get("content-type") || "image/jpeg";
const con = { inlineData: { mimeType, data: base64ImageData } };
media.push(con);
}
const contents = media
? createUserContent([
prompt[0],
`**Media**: The bookmark included the following images:`,
...media,
prompt[1],
])
: [prompt[0] + "\n\n" + prompt[1]];
try {
const response = await this.api.models.generateContent({
model: this.model,
contents,
config: { systemInstruction: CATEGORIZATION_PROMPT },
});
console.log("llm res", response);
if (!response.text) {
throw new Error("No response content from LLM");
}
return this.parseCategorizationResponse(response.text);
} catch (error) {
console.error("Error in LLM categorization:", error);
throw error;
}
}
private buildCategorizationPrompt(
request: CategorizationRequest,
): [string, string] {
const { bookmark, userCategories } = request;
const prompt: [string, string] = [
`
Analyze the following bookmark and provide your categorization suggestions.
## Bookmark Details
**Text**: "${bookmark.text}"
**Language**: ${bookmark.language}
**Author**: ${bookmark.author.name} (@${bookmark.author.username})
**Hashtags**: ${bookmark.hashtags.join(", ") || "None"}
**URLs**: ${bookmark.urls.map((u) => u.expandedUrl).join(", ") || "None"}`,
`## User Categories
${userCategories.map((cat) => `- ${cat.name}: ${cat.criteria}`).join("\n")}
Please provide your categorization analysis in the requested JSON format.
`,
];
return prompt;
}
private parseCategorizationResponse(content: string): CategorizationResponse {
try {
// Try to extract JSON from the response
const jsonMatch = content.match(/\{[\s\S]*\}/);
if (!jsonMatch) {
throw new Error("No JSON found in response");
}
const parsed = JSON.parse(jsonMatch[0]);
// Validate response structure
if (
!parsed.suggestedCategories ||
!Array.isArray(parsed.suggestedCategories)
) {
throw new Error(
"Invalid response structure: missing suggestedCategories",
);
}
return {
suggestedCategories: parsed.suggestedCategories.map((s: any) => ({
categories: Array.isArray(s.categories)
? s.categories
: [s.categories],
confidence: typeof s.confidence === "number" ? s.confidence : 0.5,
reasoning: s.reasoning || "No reasoning provided",
})),
newCategories: Array.isArray(parsed.newCategories)
? parsed.newCategories
: [],
summary: parsed.summary || "No summary provided",
keyTopics: Array.isArray(parsed.keyTopics) ? parsed.keyTopics : [],
};
} catch (error) {
console.error("Error parsing LLM response:", error);
// Return fallback response
return {
suggestedCategories: [
{
categories: ["Uncategorized"],
confidence: 0.1,
reasoning: "Failed to parse LLM response",
},
],
newCategories: [],
summary: "Analysis failed",
keyTopics: [],
};
}
}
// Generic LLM request method for future use
// async sendPrompt(
// prompt: string,
// options?: LLMRequestOptions,
// ): Promise<string> {
// const mergedOptions = { ...this.defaultOptions, ...options };
// try {
// const response = await fetch(`${this.baseUrl}/v1/messages`, {
// method: "POST",
// headers: {
// "Content-Type": "application/json",
// Authorization: `Bearer ${this.apiKey}`,
// "anthropic-version": "2023-06-01",
// },
// body: JSON.stringify({
// model: mergedOptions.model,
// max_tokens: mergedOptions.maxTokens,
// temperature: mergedOptions.temperature,
// messages: [
// {
// role: "user",
// content: prompt,
// },
// ],
// }),
// });
// if (!response.ok) {
// throw new Error(
// `LLM API request failed: ${response.status} ${response.statusText}`,
// );
// }
// const data = await response.json();
// return data.content[0]?.text || "";
// } catch (error) {
// console.error("Error in generic LLM request:", error);
// throw error;
// }
// }
}
|
