AutoGPT/v2: First pass with small fixes

* Typing fixes & improvements

* Improved console output formatting

* Added support for all OpenAI GPT-3.5-turbo and GPT-4 model versions

* Added token counting functions to ModelProviders

autogpts/autogpt/autogpt/core/ability/simple.py

@@ -48,7 +48,7 @@ def __init__(
         self._memory = memory
         self._workspace = workspace
         self._model_providers = model_providers
-        self._abilities = []
+        self._abilities: list[Ability] = []
         for (
             ability_name,
             ability_configuration,

autogpts/autogpt/autogpt/core/planning/__init__.py

@@ -2,7 +2,6 @@
 from autogpt.core.planning.schema import (
     LanguageModelClassification,
     LanguageModelPrompt,
-    LanguageModelResponse,
     Task,
     TaskStatus,
     TaskType,

autogpts/autogpt/autogpt/core/planning/schema.py

@@ -1,12 +1,12 @@
 import enum
+from typing import Optional
 
 from pydantic import BaseModel, Field
 
 from autogpt.core.ability.schema import AbilityResult
 from autogpt.core.resource.model_providers.schema import (
     LanguageModelFunction,
     LanguageModelMessage,
-    LanguageModelProviderModelResponse,
 )
 
 
@@ -19,43 +19,42 @@ class LanguageModelClassification(str, enum.Enum):
 
     """
 
-    FAST_MODEL: str = "fast_model"
-    SMART_MODEL: str = "smart_model"
+    FAST_MODEL = "fast_model"
+    SMART_MODEL = "smart_model"
 
 
 class LanguageModelPrompt(BaseModel):
     messages: list[LanguageModelMessage]
     functions: list[LanguageModelFunction] = Field(default_factory=list)
 
     def __str__(self):
-        return "\n\n".join([f"{m.role.value}: {m.content}" for m in self.messages])
-
-
-class LanguageModelResponse(LanguageModelProviderModelResponse):
-    """Standard response struct for a response from a language model."""
+        return "\n\n".join(
+            f"{m.role.value.upper()}: {m.content}"
+            for m in self.messages
+        )
 
 
 class TaskType(str, enum.Enum):
-    RESEARCH: str = "research"
-    WRITE: str = "write"
-    EDIT: str = "edit"
-    CODE: str = "code"
-    DESIGN: str = "design"
-    TEST: str = "test"
-    PLAN: str = "plan"
+    RESEARCH = "research"
+    WRITE = "write"
+    EDIT = "edit"
+    CODE = "code"
+    DESIGN = "design"
+    TEST = "test"
+    PLAN = "plan"
 
 
 class TaskStatus(str, enum.Enum):
-    BACKLOG: str = "backlog"
-    READY: str = "ready"
-    IN_PROGRESS: str = "in_progress"
-    DONE: str = "done"
+    BACKLOG = "backlog"
+    READY = "ready"
+    IN_PROGRESS = "in_progress"
+    DONE = "done"
 
 
 class TaskContext(BaseModel):
     cycle_count: int = 0
     status: TaskStatus = TaskStatus.BACKLOG
-    parent: "Task" = None
+    parent: Optional["Task"] = None
     prior_actions: list[AbilityResult] = Field(default_factory=list)
     memories: list = Field(default_factory=list)
     user_input: list[str] = Field(default_factory=list)

autogpts/autogpt/autogpt/core/planning/simple.py

@@ -14,14 +14,15 @@
 from autogpt.core.planning.base import PromptStrategy
 from autogpt.core.planning.schema import (
     LanguageModelClassification,
-    LanguageModelResponse,
     Task,
 )
 from autogpt.core.resource.model_providers import (
     LanguageModelProvider,
+    LanguageModelResponse,
     ModelProviderName,
     OpenAIModelName,
 )
+from autogpt.core.runner.client_lib.logging.helpers import dump_prompt
 from autogpt.core.workspace import Workspace
 
 
@@ -153,7 +154,7 @@ async def chat_with_model(
         template_kwargs.update(kwargs)
         prompt = prompt_strategy.build_prompt(**template_kwargs)
 
-        self._logger.debug(f"Using prompt:\n{prompt}\n\n")
+        self._logger.debug(f"Using prompt:\n{dump_prompt(prompt)}\n")
         response = await provider.create_language_completion(
             model_prompt=prompt.messages,
             functions=prompt.functions,

autogpts/autogpt/autogpt/core/planning/strategies/initial_plan.py

@@ -1,3 +1,5 @@
+import logging
+
 from autogpt.core.configuration import SystemConfiguration, UserConfigurable
 from autogpt.core.planning.base import PromptStrategy
 from autogpt.core.planning.schema import (
@@ -13,6 +15,8 @@
     MessageRole,
 )
 
+logger = logging.getLogger(__name__)
+
 
 class InitialPlanConfiguration(SystemConfiguration):
     model_classification: LanguageModelClassification = UserConfigurable()
@@ -98,7 +102,7 @@ class InitialPlan(PromptStrategy):
         },
     }
 
-    default_configuration = InitialPlanConfiguration(
+    default_configuration: InitialPlanConfiguration = InitialPlanConfiguration(
         model_classification=LanguageModelClassification.SMART_MODEL,
         system_prompt_template=DEFAULT_SYSTEM_PROMPT_TEMPLATE,
         system_info=DEFAULT_SYSTEM_INFO,
@@ -183,8 +187,12 @@ def parse_response_content(
             The parsed response.
 
         """
-        parsed_response = json_loads(response_content["function_call"]["arguments"])
-        parsed_response["task_list"] = [
-            Task.parse_obj(task) for task in parsed_response["task_list"]
-        ]
+        try:
+            parsed_response = json_loads(response_content["function_call"]["arguments"])
+            parsed_response["task_list"] = [
+                Task.parse_obj(task) for task in parsed_response["task_list"]
+            ]
+        except KeyError:
+            logger.debug(f"Failed to parse this response content: {response_content}")
+            raise
         return parsed_response

autogpts/autogpt/autogpt/core/planning/strategies/name_and_goals.py

@@ -1,3 +1,5 @@
+import logging
+
 from autogpt.core.configuration import SystemConfiguration, UserConfigurable
 from autogpt.core.planning.base import PromptStrategy
 from autogpt.core.planning.schema import (
@@ -11,6 +13,7 @@
     MessageRole,
 )
 
+logger = logging.getLogger(__name__)
 
 class NameAndGoalsConfiguration(SystemConfiguration):
     model_classification: LanguageModelClassification = UserConfigurable()
@@ -21,12 +24,16 @@ class NameAndGoalsConfiguration(SystemConfiguration):
 
 class NameAndGoals(PromptStrategy):
     DEFAULT_SYSTEM_PROMPT = (
-        "Your job is to respond to a user-defined task by invoking the `create_agent` function "
-        "to generate an autonomous agent to complete the task. You should supply a role-based "
-        "name for the agent, an informative description for what the agent does, and 1 to 5 "
-        "goals that are optimally aligned with the successful completion of its assigned task.\n\n"
+        "Your job is to respond to a user-defined task, given in triple quotes, by "
+        "invoking the `create_agent` function to generate an autonomous agent to "
+        "complete the task. "
+        "You should supply a role-based name for the agent, "
+        "an informative description for what the agent does, and "
+        "1 to 5 goals that are optimally aligned with the successful completion of "
+        "its assigned task.\n"
+        "\n"
         "Example Input:\n"
-        "Help me with marketing my business\n\n"
+        '"""Help me with marketing my business"""\n\n'
         "Example Function Call:\n"
         "create_agent(name='CMOGPT', "
         "description='A professional digital marketer AI that assists Solopreneurs in "
@@ -43,7 +50,7 @@ class NameAndGoals(PromptStrategy):
         "remains on track.'])"
     )
 
-    DEFAULT_USER_PROMPT_TEMPLATE = "'{user_objective}'"
+    DEFAULT_USER_PROMPT_TEMPLATE = '"""{user_objective}"""'
 
     DEFAULT_CREATE_AGENT_FUNCTION = {
         "name": "create_agent",
@@ -77,7 +84,7 @@ class NameAndGoals(PromptStrategy):
         },
     }
 
-    default_configuration = NameAndGoalsConfiguration(
+    default_configuration: NameAndGoalsConfiguration = NameAndGoalsConfiguration(
         model_classification=LanguageModelClassification.SMART_MODEL,
         system_prompt=DEFAULT_SYSTEM_PROMPT,
         user_prompt_template=DEFAULT_USER_PROMPT_TEMPLATE,
@@ -135,5 +142,9 @@ def parse_response_content(
             The parsed response.
 
         """
-        parsed_response = json_loads(response_content["function_call"]["arguments"])
+        try:
+            parsed_response = json_loads(response_content["function_call"]["arguments"])
+        except KeyError:
+            logger.debug(f"Failed to parse this response content: {response_content}")
+            raise
         return parsed_response

autogpts/autogpt/autogpt/core/planning/strategies/next_ability.py

@@ -1,3 +1,5 @@
+import logging
+
 from autogpt.core.configuration import SystemConfiguration, UserConfigurable
 from autogpt.core.planning.base import PromptStrategy
 from autogpt.core.planning.schema import (
@@ -12,6 +14,8 @@
     MessageRole,
 )
 
+logger = logging.getLogger(__name__)
+
 
 class NextAbilityConfiguration(SystemConfiguration):
     model_classification: LanguageModelClassification = UserConfigurable()
@@ -61,7 +65,7 @@ class NextAbility(PromptStrategy):
         },
     }
 
-    default_configuration = NextAbilityConfiguration(
+    default_configuration: NextAbilityConfiguration = NextAbilityConfiguration(
         model_classification=LanguageModelClassification.SMART_MODEL,
         system_prompt_template=DEFAULT_SYSTEM_PROMPT_TEMPLATE,
         system_info=DEFAULT_SYSTEM_INFO,
@@ -171,13 +175,17 @@ def parse_response_content(
             The parsed response.
 
         """
-        function_name = response_content["function_call"]["name"]
-        function_arguments = json_loads(response_content["function_call"]["arguments"])
-        parsed_response = {
-            "motivation": function_arguments.pop("motivation"),
-            "self_criticism": function_arguments.pop("self_criticism"),
-            "reasoning": function_arguments.pop("reasoning"),
-            "next_ability": function_name,
-            "ability_arguments": function_arguments,
-        }
+        try:
+            function_name = response_content["function_call"]["name"]
+            function_arguments = json_loads(response_content["function_call"]["arguments"])
+            parsed_response = {
+                "motivation": function_arguments.pop("motivation"),
+                "self_criticism": function_arguments.pop("self_criticism"),
+                "reasoning": function_arguments.pop("reasoning"),
+                "next_ability": function_name,
+                "ability_arguments": function_arguments,
+            }
+        except KeyError:
+            logger.debug(f"Failed to parse this response content: {response_content}")
+            raise
         return parsed_response