Create your own QA RAG Chatbot with LangChain.js + Azure OpenAI Service
Demo: Mpesa for Business Setup QA RAG Application In this tutorial we are going to build a Question-Answering RAG Chat Web App. We utilize Node.js and HTML, CSS, JS. We also incorporate Langchain.js + Azure OpenAI + MongoDB Vector Store (MongoDB Search Index). Get a quick look below. Note: Documents and illustrations shared here are for demo purposes only and Microsoft or its products are not part of Mpesa. The content demonstrated here should be used for educational purposes only. Additionally, all views shared here are solely mine. What you will need: An active Azure subscription, get Azure for Student for free or get started with Azure for 12 months free. VS Code Basic knowledge in JavaScript (not a must) Access to Azure OpenAI, click here if you don't have access. Create a MongoDB account (You can also use Azure Cosmos DB vector store) Setting Up the Project In order to build this project, you will have to fork this repository and clone it. GitHub Repository link: https://github.com/tiprock-network/azure-qa-rag-mpesa . Follow the steps highlighted in the README.md to setup the project under Setting Up the Node.js Application. Create Resources that you Need In order to do this, you will need to have Azure CLI or Azure Developer CLI installed in your computer. Go ahead and follow the steps indicated in the README.md to create Azure resources under Azure Resources Set Up with Azure CLI. You might want to use Azure CLI to login in differently use a code. Here's how you can do this. Instead of using az login. You can do az login --use-code-device OR you would prefer using Azure Developer CLI and execute this command instead azd auth login --use-device-code Remember to update the .env file with the values you have used to name Azure OpenAI instance, Azure models and even the API Keys you have obtained while creating your resources. Setting Up MongoDB After accessing you MongoDB account get the URI link to your database and add it to the .env file along with your database name and vector store collection name you specified while creating your indexes for a vector search. Running the Project In order to run this Node.js project you will need to start the project using the following command. npm run dev The Vector Store The vector store used in this project is MongoDB store where the word embeddings were stored in MongoDB. From the embeddings model instance we created on Azure AI Foundry we are able to create embeddings that can be stored in a vector store. The following code below shows our embeddings model instance. //create new embedding model instance const azOpenEmbedding = new AzureOpenAIEmbeddings({ azureADTokenProvider, azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME, azureOpenAIApiEmbeddingsDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_EMBEDDING_NAME, azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION, azureOpenAIBasePath: "https://eastus2.api.cognitive.microsoft.com/openai/deployments" }); The code in uploadDoc.js offers a simple way to do embeddings and store them to MongoDB. In this approach the text from the documents is loaded using the PDFLoader from Langchain community. The following code demonstrates how the embeddings are stored in the vector store. // Call the function and handle the result with await const storeToCosmosVectorStore = async () => { try { const documents = await returnSplittedContent() //create store instance const store = await MongoDBAtlasVectorSearch.fromDocuments( documents, azOpenEmbedding, { collection: vectorCollection, indexName: "myrag_index", textKey: "text", embeddingKey: "embedding", } ) if(!store){ console.log('Something wrong happened while creating store or getting store!') return false } console.log('Done creating/getting and uploading to store.') return true } catch (e) { console.log(`This error occurred: ${e}`) return false } } In this setup, Question Answering (QA) is achieved by integrating Azure OpenAI’s GPT-4o with MongoDB Vector Search through LangChain.js. The system processes user queries via an LLM (Large Language Model), which retrieves relevant information from a vectorized database, ensuring contextual and accurate responses. Azure OpenAI Embeddings convert text into dense vector representations, enabling semantic search within MongoDB. The LangChain RunnableSequence structures the retrieval and response generation workflow, while the StringOutputParser ensures proper text formatting. The most relevant code snippets to include are: AzureChatOpenAI instantiation, MongoDB connection setup, and the API endpoint handling QA queries using vector search and embeddings. There are some code snippets below to explain major parts of the code. Azure AI Chat Completion Model This is the model used in this implementation of RAG, where we use it as the model for chat completion. Below is a code snippet for it. const llm = new AzureChatOpenAI({ azTokenProvider, azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME, azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME, azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION }) Using a Runnable Sequence to give out Chat Output This shows how a runnable sequence can be used to give out a response given the particular output format/ output parser added on to the chain. //Stream response app.post(`${process.env.BASE_URL}/az-openai/runnable-sequence/stream/chat`, async (req,res) => { //check for human message const { chatMsg } = req.body if(!chatMsg) return res.status(201).json({ message:'Hey, you didn\'t send anything.' }) //put the code in an error-handler try{ //create a prompt template format template const prompt = ChatPromptTemplate.fromMessages( [ ["system", `You are a French-to-English translator that detects if a message isn't in French. If it's not, you respond, "This is not French." Otherwise, you translate it to English.`], ["human", `${chatMsg}`] ] ) //runnable chain const chain = RunnableSequence.from([prompt, llm, outPutParser]) //chain result let result_stream = await chain.stream() //set response headers res.setHeader('Content-Type','application/json') res.setHeader('Transfer-Encoding','chunked') //create readable stream const readable = Readable.from(result_stream) res.status(201).write(`{"message": "Successful translation.", "response": "`); readable.on('data', (chunk) => { // Convert chunk to string and write it res.write(`${chunk}`); }); readable.on('end', () => { // Close the JSON response properly res.write('" }'); res.end(); }); readable.on('error', (err) => { console.error("Stream error:", err); res.status(500).json({ message: "Translation failed.", error: err.message }); }); }catch(e){ //deliver a 500 error response return res.status(500).json( { message:'Failed to send request.', error:e } ) } }) To run the front end of the code, go to your BASE_URL with the port given. This enables you to run the chatbot above and achieve similar results. The chatbot is basically HTML+CSS+JS. Where JavaScript is mainly used with fetch API to get a response. Thanks for reading. I hope you play around with the code and learn some new things. Additional Reads Introduction to LangChain.js Create an FAQ Bot on Azure Build a basic chat app in Python using Azure AI Foundry SDK
Introduction to Content filtering and Embeddings in Azure Open AI Service
Content filtering and Embeddings in Azure AI Open Service: Abuse Monitoring Content Classification: Classifier models detect harmful language and/or images in user prompts (inputs) and completions (outputs). The system looks for categories of harms as defined in the Content Requirements, and assigns severity levels as described in more detail on the Content Filtering page. Abuse Pattern Capture: Azure OpenAI Service’s abuse monitoring looks at customer usage patterns and employs algorithms and heuristics to detect indicators of potential abuse. Detected patterns consider, for example, the frequency and severity at which harmful content is detected in a customer’s prompts and completions. Human Review and Decision: When prompts and/or completions are flagged through content classification and abuse pattern capture as described above, authorized Microsoft employees may assess the flagged content, and either confirm or correct the classification or determination based on predefined guidelines and policies. Data can be accessed for human review only by authorized Microsoft employees via Secure Access Workstations (SAWs) with Just-In-Time (JIT) request approval granted by team managers. For Azure OpenAI Service resources deployed in the European Economic Area, the authorized Microsoft employees are located in the European Economic Area. Notification and Action: When a threshold of abusive behavior has been confirmed based on the preceding three steps, the customer is informed of the determination by email. Except in cases of severe or recurring abuse, customers typically are given an opportunity to explain or remediate—and implement mechanisms to prevent recurrence of—the abusive behavior. Failure to address the behavior—or recurring or severe abuse—may result in suspension or termination of the customer’s access to Azure OpenAI resources and/or capabilities. Content filtering Azure OpenAI Service includes a content management system that works alongside core models to filter content. If the system identifies harmful content, you'll receive either An error on the API call Content_filter as the finish_reason on the response Mitigate Mitigating harms presented by large language models such as the Azure OpenAI models requires an iterative, layered approach that includes experimentation and continual measurement. Best practices – Content filtering Consider the following best practices Check the finish_reason to see if the generation is filtered Check that there's no error object in the content_filter_result Applications serving multiple end-users should pass the user parameter with each API call. More details: Azure OpenAI Service content filtering - Azure OpenAI | Microsoft Learn Embedding: Cosine similarity Cosine similarity measures the cosine of the angle between two vectors projected in a multi-dimensional space. If two documents are far apart by Euclidean distance because of size, they could still have a smaller angle between them and therefore higher cosine similarity. Azure OpenAI embeddings rely on cosine similarity to compute similarity between documents and a query. More details: Azure OpenAI Service embeddings - Azure OpenAI - embeddings and cosine similarity | Microsoft LearnIntegrating Power Apps with Azure Machine Learning & OpenAI using Power Automate
Learn how to integrate Power Apps with Azure Machine Learning & OpenAI using Power Automate through a Health Expense Planner Application. This app allows the user to predict their Health Expense using a Machine Learning Model and then get a detailed personalized plan to save funds for it.
