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Use Functions with Chat Models

This flow covers how to use the LLM tool chat API in combination with external functions to extend the capabilities of GPT models.

functions is an optional parameter in the Chat Completion API which can be used to provide function specifications. The purpose of this is to enable models to generate function arguments which adhere to the provided specifications. Note that the API will not actually execute any function calls. It is up to developers to execute function calls using model outputs.

If the functions parameter is provided then by default the model will decide when it is appropriate to use one of the functions. The API can be forced to use a specific function by setting the function_call parameter to {"name": "<insert-function-name>"}. The API can also be forced to not use any function by setting the function_call parameter to "none". If a function is used, the output will contain "finish_reason": "function_call" in the response, as well as a function_call object that has the name of the function and the generated function arguments. You can refer to openai sample for more details.

What you will learn

In this flow, you will learn how to use functions with LLM chat models and how to compose function role message in prompt template.

Tools used in this flow

  • LLM tool
  • Python tool

Prerequisites

Install prompt-flow sdk and other dependencies:

pip install -r requirements.txt

Getting started

1 Create connection for LLM tool to use

Go to "Prompt flow" "Connections" tab. Click on "Create" button, select one of LLM tool supported connection types and fill in the configurations.

Currently, there are two connection types supported by LLM tool: "AzureOpenAI" and "OpenAI". If you want to use "AzureOpenAI" connection type, you need to create an Azure OpenAI service first. Please refer to Azure OpenAI Service for more details. If you want to use "OpenAI" connection type, you need to create an OpenAI account first. Please refer to OpenAI for more details.

# Override keys with --set to avoid yaml file changes
pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base> --name open_ai_connection

Note in flow.dag.yaml we are using connection named open_ai_connection.

# show registered connection 
pf connection show --name open_ai_connection

2 Start chatting

# run chat flow with default question in flow.dag.yaml
pf flow test --flow . 

# run chat flow with new question
pf flow test --flow . --inputs question="How about London next week?"

# start a interactive chat session in CLI
pf flow test --flow . --interactive

# start a interactive chat session in CLI with verbose info
pf flow test --flow . --interactive --verbose

References