from local_llm_function_calling.prompter import CompletionModelPrompter, InstructModelPrompter from const.function_calls import ARCHITECTURE, DEV_STEPS from .function_calling import parse_agent_response, LlamaInstructPrompter class TestFunctionCalling: def test_parse_agent_response_text(self): # Given response = {'text': 'Hello world!'} # When response = parse_agent_response(response, None) # Then assert response == 'Hello world!' def test_parse_agent_response_json(self): # Given response = {'text': '{"greeting": "Hello world!"}'} function_calls = {'definitions': [], 'functions': {}} # When response = parse_agent_response(response, function_calls) # Then assert response == 'Hello world!' def test_parse_agent_response_json_markdown(self): # Given response = {'text': '```json\n{"greeting": "Hello world!"}\n```'} function_calls = {'definitions': [], 'functions': {}} # When response = parse_agent_response(response, function_calls) # Then assert response == 'Hello world!' def test_parse_agent_response_markdown(self): # Given response = {'text': '```\n{"greeting": "Hello world!"}\n```'} function_calls = {'definitions': [], 'functions': {}} # When response = parse_agent_response(response, function_calls) # Then assert response == 'Hello world!' def test_parse_agent_response_multiple_args(self): # Given response = {'text': '{"greeting": "Hello", "name": "John"}'} function_calls = {'definitions': [], 'functions': {}} # When greeting, name = parse_agent_response(response, function_calls) # Then assert greeting == 'Hello' assert name == 'John' def test_completion_function_prompt(): # Given prompter = CompletionModelPrompter() # When prompt = prompter.prompt('Create a web-based chat app', ARCHITECTURE['definitions']) # , 'process_technologies') # Then assert prompt == '''Create a web-based chat app Available functions: process_technologies - Print the list of technologies that are created. ```jsonschema { "technologies": { "type": "array", "description": "List of technologies that are created in a list.", "items": { "type": "string", "description": "technology" } } } ``` Function call: Function call: ''' def test_instruct_function_prompter(): # Given prompter = InstructModelPrompter() # When prompt = prompter.prompt('Create a web-based chat app', ARCHITECTURE['definitions']) # , 'process_technologies') # Then assert prompt == '''Your task is to call a function when needed. You will be provided with a list of functions. Available functions: process_technologies - Print the list of technologies that are created. ```jsonschema { "technologies": { "type": "array", "description": "List of technologies that are created in a list.", "items": { "type": "string", "description": "technology" } } } ``` Create a web-based chat app Function call: ''' # def test_json_prompter(): # # Given # prompter = JsonPrompter() # # # When # prompt = prompter.prompt('Create a web-based chat app', ARCHITECTURE['definitions']) # , 'process_technologies') # # # Then # assert prompt == '''[INST] <> # Help choose the appropriate function to call to answer the user's question. # In your response you must only use JSON output and provide no notes or commentary. # # Available functions: # - process_technologies - Print the list of technologies that are created. # <> # # Create a web-based chat app [/INST]''' def test_llama_instruct_function_prompter_named(): # Given prompter = LlamaInstructPrompter() # When prompt = prompter.prompt('Create a web-based chat app', ARCHITECTURE['definitions'], 'process_technologies') # Then assert prompt == '''[INST] <> Define the arguments for process_technologies to answer the user's question. In your response you must only use JSON output and provide no notes or commentary. Function description: Print the list of technologies that are created. Function parameters should follow this schema: ```jsonschema { "technologies": { "type": "array", "description": "List of technologies that are created in a list.", "items": { "type": "string", "description": "technology" } } } ``` <> Create a web-based chat app [/INST]'''