⚡ + 🦜️🔗 azure-functions-langchainjs-demo

Discover the journey of building a generative AI application using LangChain.js and Azure. This demo explore the development process from idea to production, using a RAG-based approach for a Q&A system based on YouTube video transcripts.

The code comes in two versions:

local version: uses FAISS and Ollama with LLaMa2 model for completion and all-minilm-l6-v2 for embeddings
Azure cloud version: uses Azure AI Search and GPT-4 Turbo model for completion and text-embedding-3-large for embeddings

NOTE: This sample using the new HTTP streaming support in Azure Functions that's currently in preview. You can find more information about this feature in the blog post announcement.

Installation

You need Node.js and Ollama installed to run this demo.

npm install
ollama pull llama2
ollama pull all-minilm:l6-v2

Running the local prototype

npm start

Once the server is running, you can test the API in another terminal:

curl http://localhost:7071/api/ask --json '{ "question": "Will GPT-4 Turbo be available on Azure?" }'

Running the Azure version

To run the Azure version, you need to have an Azure account and a subscription enabled for Azure OpenAI usage. If you don't have an Azure account, you can create a free account to get started.

For Azure OpenAI, you can request access with this form.

Create the Azure resources

First you need to create an Azure OpenAI instance. You can deploy a version on Azure Portal following this guide.

In Azure AI Studio, you'll need to deploy these two models:

text-embedding-3-large with a deployment name of text-embedding-3-large
gpt-4 version 0125-preview (aka GPT-4 Turbo) with a deployment name of gpt4-turbo

Note: GPT-4 Turbo is currently in preview and may not be available in all regions, see this table for region availability.

You'll also need to have an Azure AI Search instance running. You can deploy a free version on Azure Portal without any cost, following this guide.

Set up the environment

You need to create a .env file with the following content:

AZURE_AISEARCH_ENDPOINT=https://<your-service-name>.search.windows.net
AZURE_AISEARCH_KEY=<your-aisearch-key>
AZURE_OPENAI_API_KEY=<your-openai-key>
AZURE_OPENAI_API_ENDPOINT=<your-openai-endpoint>
AZURE_OPENAI_API_DEPLOYMENT_NAME="gpt4-turbo"
AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME="text-embedding-3-large"
USE_AZURE=true

Then you can run:

npm run start

And test the API like before.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

sinedied/azure-functions-langchainjs-demo