OpenAI GPT-4 + FAISS DB + Paste Docs (#3492)

* chore: checkpoint ai nextjs api route and faiss db

* chore: refine api endpoint

* chore: remove unused dep

packages/paste-website/package.json

@@ -141,6 +141,7 @@
     "globby-esm": "npm:globby@^13.1.3",
     "highcharts": "^9.3.3",
     "highcharts-react-official": "^3.1.0",
+    "langchain": "^0.0.151",
     "lodash": "4.17.21",
     "lottie-web": "^5.7.4",
     "markdown-to-jsx": "^7.3.2",

packages/paste-website/src/pages/api/ai.ts

@@ -0,0 +1,80 @@
+/**
+ * API endpoint for querying our doc site with ChatGPT4
+ *
+ * Requires two environment variables:
+ * - OPENAI_API_KEY: Your OpenAI API key
+ * - OPENAI_API_SECRET: Custom secret to block unauthorized requests
+ *
+ * Please set these in your .env file and on your deployment boxes configuration.
+ */
+import {fileURLToPath} from 'url';
+import path from 'path';
+
+import {loadQAStuffChain} from 'langchain/chains';
+import {OpenAIEmbeddings} from 'langchain/embeddings/openai';
+import {ChatOpenAI} from 'langchain/chat_models/openai';
+import {FaissStore} from 'langchain/vectorstores/faiss';
+import type {NextApiRequest, NextApiResponse} from 'next';
+
+export default async function handler(req: NextApiRequest, res: NextApiResponse): Promise<void> {
+  const {question, secret} = req.body;
+  // Exit early if the required params aren't provided
+  if (!question || secret !== process.env.OPENAI_API_SECRET) {
+    res.status(200).send({answer: 'Please provide a question'});
+    return;
+  }
+
+  /*
+   * Get the FAISS DB
+   */
+  const __filename = fileURLToPath(import.meta.url);
+  const __dirname = path.dirname(__filename);
+  const directory = path.join(__dirname, '../../../indexes/faiss_index');
+  const loadedVectorStore = await FaissStore.loadFromPython(directory, new OpenAIEmbeddings());
+
+  /*
+   * Create the OpenAI model
+   */
+  const model = new ChatOpenAI({
+    modelName: 'gpt-4',
+    temperature: 0,
+  });
+
+  /*
+   * Create the QA stuff chain from our GPT-4 model
+   */
+  const stuffChain = loadQAStuffChain(model);
+
+  try {
+    /*
+     * We cannot provide GPT-4 with our entire doc site. For this reason we use the FAISS DB.
+     * This DB is a compressed vector db that allows us to search for similar "documents"
+     * based on a provided question string. We take the top 4 results and provide them to
+     * GPT-4 for further processing.
+     */
+    const inputDocuments = await loadedVectorStore.similaritySearch(question, 4);
+
+    /*
+     * Ask GPT-4 the question and provide the input documents
+     */
+    const answer = await stuffChain.call({
+      // eslint-disable-next-line camelcase
+      input_documents: inputDocuments,
+      question,
+    });
+
+    res.status(200).send({answer});
+  } catch (error) {
+    res.status(500).send({error});
+  }
+}
+
+export const config = {
+  api: {
+    bodyParser: {
+      sizeLimit: '10kb',
+    },
+  },
+  // Specifies the maximum allowed duration for this function to execute (in seconds)
+  maxDuration: 15,
+};