kopia lustrzana https://github.com/learn-awesome/learndb
added functionality for fetchContentFromURL(); added some notes for performGPTAnalysis(); modified some other lines to test fetchContentFromURL(); didn't test if anything works yetgit add .!
rodzic
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@ -1,10 +1,22 @@
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// Placeholder function to fetch content from URL using a web scraping service
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import fetch from 'node-fetch'; // Import for webscraping (fetchContentFromURL(url) function
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import OpenAI from "openai"; // Import for performGPTAnalysis(content) function
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// Function to fetch content from URL using a web scraping service
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async function fetchContentFromURL(url) {
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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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try {
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// Return the extracted content
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// Make an HTTP GET request to the provided URL
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// Placeholder code
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const response = await fetch(url);
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const content = "<p>This is a sample content fetched from the URL</p>";
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// Check if the response status is OK (status code 200)
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return content;
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if (!response.ok) {
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throw new Error(`Failed to fetch URL: ${response.statusText}`);
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}
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// Read the response body as text (HTML content)
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const content = await response.text();
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// Return the extracted content
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return content;
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} catch (error) {
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throw new Error(`Error fetching URL: ${error.message}`);
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}
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}
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}
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// Placeholder function to simplify the content for GPT analysis
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// Placeholder function to simplify the content for GPT analysis
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@ -20,6 +32,33 @@ function simplifyContent(content) {
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async function performGPTAnalysis(content) {
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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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// 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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// Send content and receive GPT analysis response
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// Default code from OpenRouter documentation for the Mistral-7b model
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// Retrieved from https://openrouter.ai/models/mistralai/mistral-7b-instruct?tab=api
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// Using OpenAI's client API makes this easily replaceable with other models (ex. GPT-4)
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```
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const openai = new OpenAI({
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baseURL: "https://openrouter.ai/api/v1",
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apiKey: $OPENROUTER_API_KEY,
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defaultHeaders: {
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"HTTP-Referer": $YOUR_SITE_URL, // Optional, for including your app on openrouter.ai rankings.
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"X-Title": $YOUR_SITE_NAME, // Optional. Shows in rankings on openrouter.ai.
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},
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// dangerouslyAllowBrowser: true,
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})
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async function main() {
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const completion = await openai.chat.completions.create({
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model: "mistralai/mistral-7b-instruct",
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messages: [
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{ role: "user", content: "Say this is a test" }
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],
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})
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console.log(completion.choices[0].message)
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}
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main()
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```
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// Placeholder code
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// Placeholder code
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const inferredMediaType = "article";
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const inferredMediaType = "article";
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const extractedTopics = ["topic1", "topic2"];
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const extractedTopics = ["topic1", "topic2"];
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@ -43,7 +82,7 @@ function formatResponse(predefinedMediaType, predefinedTopics) {
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// Placeholder code
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// Placeholder code
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const response = {
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const response = {
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format: predefinedMediaType,
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format: predefinedMediaType,
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topics: predefinedTopics,
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topics: predefinedTopics
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// Other metadata fields if needed
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// Other metadata fields if needed
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};
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};
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return response;
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return response;
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@ -80,7 +119,8 @@ export async function handler(event) {
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// Return the formatted response
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// Return the formatted response
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return {
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return {
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statusCode: 200,
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statusCode: 200,
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body: JSON.stringify(formattedResponse),
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body: JSON.stringify(fetchedContent),
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// body: JSON.stringify(formattedResponse),
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};
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};
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} catch (error) {
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} catch (error) {
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return {
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return {
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