docs: add comments and badges

README.md

@@ -6,11 +6,12 @@
 # Serverless ChatGPT with RAG using LangChain.js
 
 [![Open project in GitHub Codespaces](https://img.shields.io/badge/Codespaces-Open-blue?style=flat-square&logo=github)](https://codespaces.new/Azure-Samples/serverless-chat-langchainjs?hide_repo_select=true&ref=main)
-![Node version](https://img.shields.io/badge/Node.js->=20-grass?style=flat-square)
+[![Build Status](https://img.shields.io/github/actions/workflow/status/Azure-Samples/serverless-chat-langchainjs/build-test.yaml?style=flat-square&label=Build)](https://github.com/Azure-Samples/serverless-chat-langchainjs/actions)
+![Node version](https://img.shields.io/badge/Node.js->=20-3c873a?style=flat-square)
+[![Ollama + Mistral](https://img.shields.io/badge/Ollama-Mistral-ff7000?style=flat-square)](https://ollama.com/library/mistral)
 [![TypeScript](https://img.shields.io/badge/TypeScript-blue?style=flat-square&logo=typescript&logoColor=white)](https://www.typescriptlang.org)
-[![License](https://img.shields.io/badge/License-MIT-orange?style=flat-square)](LICENSE)
+[![License](https://img.shields.io/badge/License-MIT-yellow?style=flat-square)](LICENSE)
 
-<!-- [![Build Status](https://img.shields.io/github/actions/workflow/status/Azure-Samples/serverless-chat-langchainjs/build?style=flat-square)](https://github.com/Azure-Samples/serverless-chat-langchainjs/actions) -->
 <!-- [![Watch how to use this sample on YouTube](https://img.shields.io/badge/YouTube-Watch-d95652.svg?style=flat-square&logo=youtube)]() -->
 
 :star: If you like this sample, star it on GitHub — it helps a lot!

packages/api/src/functions/chat-post.ts

@@ -55,6 +55,7 @@ export async function postChat(request: HttpRequest, context: InvocationContext)
     let store: VectorStore;
 
     if (azureOpenAiEndpoint) {
+      // Initialize models and vector database
       embeddings = new AzureOpenAIEmbeddings();
       model = new AzureChatOpenAI();
       store = new AzureCosmosDBVectorStore(embeddings, {});
@@ -66,6 +67,7 @@ export async function postChat(request: HttpRequest, context: InvocationContext)
       store = await FaissStore.load(faissStoreFolder, embeddings);
     }
 
+    // Create the chain that combines the prompt with the documents
     const combineDocsChain = await createStuffDocumentsChain({
       llm: model,
       prompt: ChatPromptTemplate.fromMessages([
@@ -74,6 +76,8 @@ export async function postChat(request: HttpRequest, context: InvocationContext)
       ]),
       documentPrompt: PromptTemplate.fromTemplate('{filename}: {page_content}\n'),
     });
+
+    // Create the chain to retrieve the documents from the database
     const chain = await createRetrievalChain({
       retriever: store.asRetriever(),
       combineDocsChain,
@@ -96,6 +100,7 @@ export async function postChat(request: HttpRequest, context: InvocationContext)
   }
 }
 
+// Transform the response chunks into a JSON stream
 function createStream(chunks: AsyncIterable<{ context: Document[]; answer: string }>) {
   const buffer = new Readable({
     read() {},

packages/api/src/functions/documents-get.ts

@@ -16,6 +16,7 @@ async function getDocument(request: HttpRequest, context: InvocationContext): Pr
     let fileData: Uint8Array;
 
     if (connectionString && containerName) {
+      // Retrieve the file from Azure Blob Storage
       context.log(`Reading blob from: "${containerName}/${fileName}"`);
       const blobServiceClient = BlobServiceClient.fromConnectionString(connectionString);
       const containerClient = blobServiceClient.getContainerClient(containerName);

packages/api/src/functions/documents-post.ts

@@ -25,18 +25,21 @@ export async function postDocuments(request: HttpRequest, context: InvocationCon
     const file = parsedForm.get('file') as File;
     const filename = file.name;
 
+    // Extract text from the PDF
     const loader = new PDFLoader(file, {
       splitPages: false,
     });
     const rawDocument = await loader.load();
     rawDocument[0].metadata.filename = filename;
 
+    // Split the text into smaller chunks
     const splitter = new RecursiveCharacterTextSplitter({
       chunkSize: 1500,
       chunkOverlap: 100,
     });
     const documents = await splitter.splitDocuments(rawDocument);
 
+    // Generate embeddings and save in database
     if (azureOpenAiEndpoint) {
       const store = await AzureCosmosDBVectorStore.fromDocuments(documents, new AzureOpenAIEmbeddings(), {});
       await store.createIndex();
@@ -50,6 +53,7 @@ export async function postDocuments(request: HttpRequest, context: InvocationCon
     }
 
     if (connectionString && containerName) {
+      // Upload the PDF file to Azure Blob Storage
       context.log(`Uploading file to blob storage: "${containerName}/${filename}"`);
       const blobServiceClient = BlobServiceClient.fromConnectionString(connectionString);
       const containerClient = blobServiceClient.getContainerClient(containerName);

scripts/upload-documents.js

@@ -1,6 +1,13 @@
 import fs from 'node:fs/promises';
 import path from 'node:path';
 
+// This script uploads all PDF files from the 'data' folder to the ingestion API.
+// It does a Node.js equivalent of this bash script:
+// ```
+// for file in data/*.pdf; do
+//   curl -X POST -F "file=@$file" <api_url>/api/documents
+// done
+// ```
 async function uploadDocuments(apiUrl, dataFolder) {
   try {
     const files = await fs.readdir(dataFolder);