page_type	languages	products	urlFragment	name	description
sample	azdeveloper python bicep	azure azure-functions azure-openai	function-python-ai-langchain	Azure Functions - LangChain with Azure OpenAI and ChatGPT (Python v2 Function)	Using human prompt with Python as HTTP Get or Post input, calculates the completions using chains of human input and templates.

Azure Functions

LangChain with Azure OpenAI and ChatGPT (Python v2 Function)

This sample shows how to take a human prompt as HTTP Get or Post input, calculates the completions using chains of human input and templates. This is a starting point that can be used for more sophisticated chains.

Run on your local environment

Pre-reqs

1. Python 3.8+
2. Azure Functions Core Tools
3. Azure OpenAPI API key, endpoint, and deployment
4. Add this local.settings.json file to this folder to simplify local development and include Key from step 3

./local.settings.json

{
  "IsEncrypted": false,
  "Values": {
    "FUNCTIONS_WORKER_RUNTIME": "python",
    "AzureWebJobsFeatureFlags": "EnableWorkerIndexing",
    "AzureWebJobsStorage": "",
    "AZURE_OPENAI_KEY": "...",
    "AZURE_OPENAI_ENDPOINT": "https://<service_name>.openai.azure.com/",
    "AZURE_OPENAI_SERVICE": "...",
    "AZURE_OPENAI_CHATGPT_DEPLOYMENT": "...",
    "OPENAI_API_VERSION": "2023-05-15",
    "USE_LANGCHAIN": "True"
  }
}

Using Functions CLI

1. Open a new terminal and do the following:

pip3 install -r requirements.txt
func start

2. Using your favorite REST client, e.g. RestClient in VS Code, PostMan, curl, make a post. test.http has been provided to run this quickly.

Terminal:

curl -i -X POST http://localhost:7071/api/ask/ \
  -H "Content-Type: text/json" \
  --data-binary "@testdata.json"

testdata.json

{
    "prompt": "What is a good feature of Azure Functions?"
}

test.http

POST http://localhost:7071/api/ask HTTP/1.1
content-type: application/json

{
    "prompt": "What is a good feature of Azure Functions?"
}

Using Visual Studio Code

1. Open this repo in VS Code:

code .

2. Follow the prompts to load Function. It is recommended to Initialize the Functions Project for VS Code, and also to enable a virtual environment for your chosen version of Python.

3. Run and Debug F5 the app

4. Test using same REST client steps above

Source Code

The key code that makes this work is as follows in function_app.py. You can customize this or learn more snippets using the LangChain Quickstart Guide.

llm = AzureOpenAI(deployment_name=AZURE_OPENAI_CHATGPT_DEPLOYMENT, temperature=0.3, openai_api_key=AZURE_OPENAI_KEY)

llm_prompt = PromptTemplate(
    input_variables=["human_prompt"],
    template="The following is a conversation with an AI assistant. The assistant is helpful.\n\nAI: I am an AI created by OpenAI. How can I help you today?\nHuman: {human_prompt}?",
)

from langchain.chains import LLMChain
chain = LLMChain(llm=llm, prompt=llm_prompt)

return chain.run(prompt) # prompt is human input from request body

Deploy to Azure

The easiest way to deploy this app is using the Azure Dev CLI. If you open this repo in GitHub CodeSpaces the AZD tooling is already preinstalled.

To provision and deploy:

azd up