clean up

pilot/utils/function_calling.py

@@ -1,12 +1,31 @@
 import json
 import re
-# from local_llm_function_calling import Generator
-# from local_llm_function_calling.model.llama import LlamaModel
-# from local_llm_function_calling.model.huggingface import HuggingfaceModel
-from local_llm_function_calling.prompter import FunctionType, CompletionModelPrompter, InstructModelPrompter
-# from local_llm_function_calling.model.llama import LlamaInstructPrompter
+from typing import Literal, NotRequired, TypedDict, Callable
 
-from typing import Literal, NotRequired, Protocol, TypeVar, TypedDict, Callable
+JsonType = str | int | float | bool | None | list["JsonType"] | dict[str, "JsonType"]
+
+
+class FunctionParameters(TypedDict):
+    """Function parameters"""
+
+    type: Literal["object"]
+    properties: dict[str, JsonType]
+    required: NotRequired[list[str]]
+
+
+class FunctionType(TypedDict):
+    """Function type"""
+
+    name: str
+    description: NotRequired[str]
+    parameters: FunctionParameters
+
+
+class FunctionCall(TypedDict):
+    """Function call"""
+
+    name: str
+    parameters: str
 
 
 class FunctionCallSet(TypedDict):
@@ -29,8 +48,6 @@ def add_function_calls_to_request(gpt_data, function_calls: FunctionCallSet | No
     #         gpt_data['function_call'] = {'name': function_calls['definitions'][0]['name']}
     #     return
 
-    # prompter = CompletionModelPrompter()
-    # prompter = InstructModelPrompter()
     prompter = JsonPrompter(is_llama)
 
     if len(function_calls['definitions']) > 1:
@@ -39,9 +56,6 @@ def add_function_calls_to_request(gpt_data, function_calls: FunctionCallSet | No
         function_call = function_calls['definitions'][0]['name']
 
     role = 'user' if '/' in model else 'system'
-    # role = 'user'
-    # role = 'system'
-    # is_llama = True
 
     gpt_data['messages'].append({
         'role': role,
@@ -186,8 +200,6 @@ class JsonPrompter:
             "Help choose the appropriate function to call to answer the user's question."
             if function_to_call is None
             else f"Define the arguments for {function_to_call} to answer the user's question."
-        # ) + "\nYou must return a JSON object without notes or commentary."
-        # ) + " \nIn your response you must only use JSON output and provide no explanation or commentary."
         ) + " \nThe response should contain only the JSON object, with no additional text or explanation."
 
         data = (

pilot/utils/llm_connection.py

@@ -88,7 +88,7 @@ def create_gpt_chat_completion(messages: List[dict], req_type, min_tokens=MIN_TO
     }
 
     # delete some keys if using "OpenRouter" API
-    if os.getenv('ENDPOINT') == "OPENROUTER":
+    if os.getenv('ENDPOINT') == 'OPENROUTER':
         keys_to_delete = ['n', 'max_tokens', 'temperature', 'top_p', 'presence_penalty', 'frequency_penalty']
         for key in keys_to_delete:
             if key in gpt_data:

pilot/utils/test_llm_connection.py

@@ -1,5 +1,4 @@
 import builtins
-import os
 import pytest
 from dotenv import load_dotenv
 
@@ -21,42 +20,6 @@ class TestLlmConnection:
     def setup_method(self):
         builtins.print, ipc_client_instance = get_custom_print({})
 
-    # def test_break_down_development_task(self):
-    #     # Given
-    #     agent = Developer(project)
-    #     convo = AgentConvo(agent)
-    #     # convo.construct_and_add_message_from_prompt('architecture/technologies.prompt',
-    #     #                                             {
-    #     #                                                 'name': 'Test App',
-    #     #                                                 'prompt': '''
-    #
-    #     messages = convo.messages
-    #     function_calls = DEV_STEPS
-    #
-    #     # When
-    #     # response = create_gpt_chat_completion(messages, '', function_calls=function_calls)
-    #     response = {'function_calls': {
-    #         'name': 'break_down_development_task',
-    #         'arguments': {'tasks': [{'type': 'command', 'description': 'Run the app'}]}
-    #     }}
-    #     response = convo.postprocess_response(response, function_calls)
-    #
-    #     # Then
-    #     # assert len(convo.messages) == 2
-    #     assert response == ([{'type': 'command', 'description': 'Run the app'}], 'more_tasks')
-
-    # @pytest.fixture(params=[
-    #     {"endpoint": "OPENAI", "model": "gpt-4"},
-    #     {"endpoint": "OPENROUTER", "model": "openai/gpt-3.5-turbo"},
-    #     {"endpoint": "OPENROUTER", "model": "meta-llama/codellama-34b-instruct"},
-    #     {"endpoint": "OPENROUTER", "model": "anthropic/claude-2"},
-    #     {"endpoint": "OPENROUTER", "model": "google/palm-2-codechat-bison"},
-    #     {"endpoint": "OPENROUTER", "model": "google/palm-2-chat-bison"},
-    # ])
-    # def params(self, request):
-    #     return request.param
-
-    @pytest.mark.slow
     @pytest.mark.uses_tokens
     @pytest.mark.parametrize("endpoint, model", [
         ("OPENAI", "gpt-4"),                                 # role: system
@@ -70,6 +33,9 @@ class TestLlmConnection:
     ])
     def test_chat_completion_Architect(self, endpoint, model, monkeypatch):
         # Given
+        monkeypatch.setenv('ENDPOINT', endpoint)
+        monkeypatch.setenv('MODEL_NAME', model)
+
         agent = Architect(project)
         convo = AgentConvo(agent)
         convo.construct_and_add_message_from_prompt('architecture/technologies.prompt',
@@ -99,22 +65,10 @@ solution-oriented decision-making in areas where precise instructions were not p
                                                             ]
                                                         })
 
-        # endpoint = 'OPENROUTER'
-        # monkeypatch.setattr('utils.llm_connection.endpoint', endpoint)
-        monkeypatch.setenv('ENDPOINT', endpoint)
-        monkeypatch.setenv('MODEL_NAME', model)
-        # monkeypatch.setenv('MODEL_NAME', 'meta-llama/codellama-34b-instruct')
-        # monkeypatch.setenv('MODEL_NAME', 'openai/gpt-3.5-turbo-16k-0613')
-        # monkeypatch.setenv('MODEL_NAME', 'anthropic/claude-2')  # TODO: remove ```json\n ... ```
-        # monkeypatch.setenv('MODEL_NAME', 'google/palm-2-codechat-bison')  # TODO: not JSON
-        # monkeypatch.setenv('MODEL_NAME', 'google/palm-2-chat-bison')        # TODO: not JSON
-
-        messages = convo.messages
         function_calls = ARCHITECTURE
 
-        # with patch('.llm_connection.endpoint', endpoint):
         # When
-        response = create_gpt_chat_completion(messages, '', function_calls=function_calls)
+        response = create_gpt_chat_completion(convo.messages, '', function_calls=function_calls)
 
         # Then
         assert convo.messages[0]['content'].startswith('You are an experienced software architect')
@@ -122,9 +76,30 @@ solution-oriented decision-making in areas where precise instructions were not p
 
         assert response is not None
         response = parse_agent_response(response, function_calls)
-        # response = response['function_calls']['arguments']['technologies']
         assert 'Node.js' in response
 
+    # def test_break_down_development_task(self):
+    #     # Given
+    #     agent = Developer(project)
+    #     convo = AgentConvo(agent)
+    #     # convo.construct_and_add_message_from_prompt('architecture/technologies.prompt',
+    #     #                                             {
+    #     #                                                 'name': 'Test App',
+    #     #                                                 'prompt': '''
+    #
+    #     function_calls = DEV_STEPS
+    #
+    #     # When
+    #     response = create_gpt_chat_completion(convo.messages, '', function_calls=function_calls)
+    #     # response = {'function_calls': {
+    #     #     'name': 'break_down_development_task',
+    #     #     'arguments': {'tasks': [{'type': 'command', 'description': 'Run the app'}]}
+    #     # }}
+    #     response = parse_agent_response(response, function_calls)
+    #
+    #     # Then
+    #     # assert len(convo.messages) == 2
+    #     assert response == ([{'type': 'command', 'description': 'Run the app'}], 'more_tasks')
 
     def _create_convo(self, agent):
-        convo = AgentConvo(agent)
+        convo = AgentConvo(agent)