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Update generate-llmstxt-service.ts
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@ -53,6 +53,7 @@ export async function performGenerateLlmsTxt(options: GenerateLLMsTextServiceOpt
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showFullText: showFullText,
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showFullText: showFullText,
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});
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});
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return {
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return {
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success: true,
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success: true,
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data: {
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data: {
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@ -86,62 +87,72 @@ export async function performGenerateLlmsTxt(options: GenerateLLMsTextServiceOpt
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let llmsFulltxt = `# ${url} llms-full.txt\n\n`;
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let llmsFulltxt = `# ${url} llms-full.txt\n\n`;
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// Scrape each URL
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// Process URLs in batches of 10
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for (const url of urls) {
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for (let i = 0; i < urls.length; i += 10) {
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_logger.debug(`Scraping URL: ${url}`);
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const batch = urls.slice(i, i + 10);
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const document = await scrapeDocument(
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{
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url,
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teamId,
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plan,
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origin: url,
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timeout: 30000,
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isSingleUrl: true,
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},
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[],
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logger,
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{ onlyMainContent: true }
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);
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if (!document) {
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const batchResults = await Promise.all(batch.map(async (url) => {
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logger.error(`Failed to scrape URL ${url}`);
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_logger.debug(`Scraping URL: ${url}`);
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continue;
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try {
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}
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const document = await scrapeDocument(
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{
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url,
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teamId,
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plan,
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origin: url,
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timeout: 30000,
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isSingleUrl: true,
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},
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[],
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logger,
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{ onlyMainContent: true }
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);
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// Process scraped result
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if (!document || !document.markdown) {
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if (!document.markdown) continue;
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logger.error(`Failed to scrape URL ${url}`);
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return null;
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_logger.debug(`Generating description for ${document.metadata?.url}`);
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const completion = await openai.beta.chat.completions.parse({
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model: "gpt-4o-mini",
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messages: [
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{
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role: "user",
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content: `Generate a 9-10 word description and a 3-4 word title of the entire page based on ALL the content one will find on the page for this url: ${document.metadata?.url}. This will help in a user finding the page for its intended purpose. Here is the content: ${document.markdown}`
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}
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}
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],
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response_format: zodResponseFormat(DescriptionSchema, "description")
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});
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try {
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_logger.debug(`Generating description for ${document.metadata?.url}`);
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const parsedResponse = completion.choices[0].message.parsed;
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const description = parsedResponse!.description;
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const title = parsedResponse!.title;
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llmstxt += `- [${title}](${document.metadata?.url}): ${description}\n`;
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const completion = await openai.beta.chat.completions.parse({
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llmsFulltxt += `## ${title}\n${document.markdown}\n\n`;
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model: "gpt-4o-mini",
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messages: [
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{
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role: "user",
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content: `Generate a 9-10 word description and a 3-4 word title of the entire page based on ALL the content one will find on the page for this url: ${document.metadata?.url}. This will help in a user finding the page for its intended purpose. Here is the content: ${document.markdown}`
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}
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],
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response_format: zodResponseFormat(DescriptionSchema, "description")
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});
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// Update progress with both generated text and full text
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const parsedResponse = completion.choices[0].message.parsed;
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await updateGeneratedLlmsTxt(generationId, {
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return {
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status: "processing",
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title: parsedResponse!.title,
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generatedText: llmstxt,
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description: parsedResponse!.description,
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fullText: llmsFulltxt,
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url: document.metadata?.url,
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});
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markdown: document.markdown
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} catch (error) {
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};
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logger.error(`Failed to parse LLM response for ${document.metadata?.url}`, { error });
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} catch (error) {
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continue;
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logger.error(`Failed to process URL ${url}`, { error });
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return null;
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}
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}));
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// Process successful results from batch
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for (const result of batchResults) {
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if (!result) continue;
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llmstxt += `- [${result.title}](${result.url}): ${result.description}\n`;
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llmsFulltxt += `## ${result.title}\n${result.markdown}\n\n`;
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}
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}
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// Update progress after each batch
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await updateGeneratedLlmsTxt(generationId, {
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status: "processing",
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generatedText: llmstxt,
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fullText: llmsFulltxt,
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});
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}
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}
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// After successful generation, save to cache
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// After successful generation, save to cache
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