kopia lustrzana https://github.com/learn-awesome/learndb
commit
126cad3307
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[build]
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functions = "netlify/functions/"
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// Placeholder function to fetch content from URL using a web scraping service
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async function fetchContentFromURL(url) {
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// Implement logic to fetch content from the URL using a web scraping service
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// Return the extracted content
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// Placeholder code
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const content = "<p>This is a sample content fetched from the URL</p>";
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return content;
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}
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// Placeholder function to simplify the content for GPT analysis
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function simplifyContent(content) {
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// Implement logic to simplify the content for GPT analysis
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// Remove unnecessary elements, clean HTML tags, format content, etc.
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// Placeholder code
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const simplifiedContent = "Simplified content suitable for GPT analysis";
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return simplifiedContent;
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}
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// Placeholder function to perform GPT analysis for media type and topics using Mistral-7b via OpenRouter
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async function performGPTAnalysis(content) {
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// Implement logic to send content to Mistral-7b via OpenRouter for GPT analysis
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// Send content and receive GPT analysis response
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// Placeholder code
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const inferredMediaType = "article";
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const extractedTopics = ["topic1", "topic2"];
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return { inferredMediaType, extractedTopics };
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}
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// Placeholder function to map inferred values to predefined formats and topics
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function mapInferredValues(mediaType, topics) {
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// Implement logic to map inferred media type and topics to predefined formats and topics
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// Match inferred values with predefined taxonomy
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// Placeholder code
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const predefinedMediaType = "Article";
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const predefinedTopics = ["Topic 1", "Topic 2"];
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return { predefinedMediaType, predefinedTopics };
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}
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// Placeholder function to format the response
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function formatResponse(predefinedMediaType, predefinedTopics) {
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// Implement logic to format the extracted metadata into the desired response structure
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// Construct the response object
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// Placeholder code
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const response = {
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mediaType: predefinedMediaType,
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topics: predefinedTopics,
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// Other metadata fields if needed
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};
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return response;
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}
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export async function handler(event) {
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try {
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// Extract URL and API Key from the request body
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const { url, apiKey } = JSON.parse(event.body);
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// Validate if URL and API Key are present
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if (!url || !apiKey) {
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return {
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statusCode: 400,
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body: JSON.stringify({ error: 'URL and API Key are required' }),
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};
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}
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// Step 1: Fetch content from the URL using a web scraping service
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const fetchedContent = await fetchContentFromURL(url);
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// Step 2: Simplify the fetched content for GPT analysis
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const simplifiedContent = simplifyContent(fetchedContent);
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// Step 3: Perform GPT analysis for media type and topics
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const { inferredMediaType, extractedTopics } = await performGPTAnalysis(simplifiedContent);
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// Step 4: Map inferred values to predefined formats and topics
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const { predefinedMediaType, predefinedTopics } = mapInferredValues(inferredMediaType, extractedTopics);
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// Step 5: Format the response
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const formattedResponse = formatResponse(predefinedMediaType, predefinedTopics);
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// Return the formatted response
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return {
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statusCode: 200,
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body: JSON.stringify(formattedResponse),
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};
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} catch (error) {
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return {
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statusCode: 500,
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body: JSON.stringify({ error: 'Something went wrong' }),
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};
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}
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}
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