refactor(agent): Refactor & improve `create_chat_completion` (#7082)

* refactor(agent/core): Rearrange and split up `OpenAIProvider.create_chat_completion`
   - Rearrange to reduce complexity, improve separation/abstraction of concerns, and allow multiple points of failure during parsing
   - Move conversion from `ChatMessage` to `openai.types.ChatCompletionMessageParam` to `_get_chat_completion_args`
   - Move token usage and cost tracking boilerplate code to `_create_chat_completion`
   - Move tool call conversion/parsing to `_parse_assistant_tool_calls` (new)

* fix(agent/core): Handle decoding of function call arguments in `create_chat_completion`
   - Amend `model_providers.schema`: change type of `arguments` from `str` to `dict[str, Any]` on `AssistantFunctionCall` and `AssistantFunctionCallDict`
   - Implement robust and transparent parsing in `OpenAIProvider._parse_assistant_tool_calls`
   - Remove now unnecessary `json_loads` calls throughout codebase

* feat(agent/utils): Improve conditions and errors in `json_loads`
   - Include all decoding errors when raising a ValueError on decode failure
   - Use errors returned by `return_errors` instead of an error buffer
   - Fix check for decode failure

autogpts/autogpt/autogpt/agent_factory/profile_generator.py

@@ -15,7 +15,6 @@ from autogpt.core.resource.model_providers.schema import (
     CompletionModelFunction,
 )
 from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.core.utils.json_utils import json_loads
 
 logger = logging.getLogger(__name__)
 
@@ -203,9 +202,7 @@ class AgentProfileGenerator(PromptStrategy):
                     f"LLM did not call {self._create_agent_function.name} function; "
                     "agent profile creation failed"
                 )
-            arguments: object = json_loads(
-                response_content.tool_calls[0].function.arguments
-            )
+            arguments: object = response_content.tool_calls[0].function.arguments
             ai_profile = AIProfile(
                 ai_name=arguments.get("name"),
                 ai_role=arguments.get("description"),

autogpts/autogpt/autogpt/agents/prompt_strategies/one_shot.py

@@ -26,7 +26,7 @@ from autogpt.core.resource.model_providers.schema import (
     CompletionModelFunction,
 )
 from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.core.utils.json_utils import extract_dict_from_json, json_loads
+from autogpt.core.utils.json_utils import extract_dict_from_json
 from autogpt.prompts.utils import format_numbered_list, indent
 
 
@@ -436,7 +436,7 @@ def extract_command(
             raise InvalidAgentResponseError("No 'tool_calls' in assistant reply")
         assistant_reply_json["command"] = {
             "name": assistant_reply.tool_calls[0].function.name,
-            "args": json_loads(assistant_reply.tool_calls[0].function.arguments),
+            "args": assistant_reply.tool_calls[0].function.arguments,
         }
     try:
         if not isinstance(assistant_reply_json, dict):

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

@@ -11,7 +11,6 @@ from autogpt.core.resource.model_providers import (
     CompletionModelFunction,
 )
 from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.core.utils.json_utils import json_loads
 
 logger = logging.getLogger(__name__)
 
@@ -195,9 +194,7 @@ class InitialPlan(PromptStrategy):
                     f"LLM did not call {self._create_plan_function.name} function; "
                     "plan creation failed"
                 )
-            parsed_response: object = json_loads(
-                response_content.tool_calls[0].function.arguments
-            )
+            parsed_response: object = response_content.tool_calls[0].function.arguments
             parsed_response["task_list"] = [
                 Task.parse_obj(task) for task in parsed_response["task_list"]
             ]

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

@@ -9,7 +9,6 @@ from autogpt.core.resource.model_providers import (
     CompletionModelFunction,
 )
 from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.core.utils.json_utils import json_loads
 
 logger = logging.getLogger(__name__)
 
@@ -141,9 +140,7 @@ class NameAndGoals(PromptStrategy):
                     f"LLM did not call {self._create_agent_function} function; "
                     "agent profile creation failed"
                 )
-            parsed_response = json_loads(
-                response_content.tool_calls[0].function.arguments
-            )
+            parsed_response = response_content.tool_calls[0].function.arguments
         except KeyError:
             logger.debug(f"Failed to parse this response content: {response_content}")
             raise

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

@@ -11,7 +11,6 @@ from autogpt.core.resource.model_providers import (
     CompletionModelFunction,
 )
 from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.core.utils.json_utils import json_loads
 
 logger = logging.getLogger(__name__)
 
@@ -188,9 +187,7 @@ class NextAbility(PromptStrategy):
                 raise ValueError("LLM did not call any function")
 
             function_name = response_content.tool_calls[0].function.name
-            function_arguments = json_loads(
-                response_content.tool_calls[0].function.arguments
-            )
+            function_arguments = response_content.tool_calls[0].function.arguments
             parsed_response = {
                 "motivation": function_arguments.pop("motivation"),
                 "self_criticism": function_arguments.pop("self_criticism"),

autogpts/autogpt/autogpt/core/resource/model_providers/openai.py

@@ -3,7 +3,7 @@ import logging
 import math
 import os
 from pathlib import Path
-from typing import Callable, Coroutine, Iterator, Optional, ParamSpec, TypeVar
+from typing import Any, Callable, Coroutine, Iterator, Optional, ParamSpec, TypeVar
 
 import sentry_sdk
 import tenacity
@@ -11,12 +11,17 @@ import tiktoken
 import yaml
 from openai._exceptions import APIStatusError, RateLimitError
 from openai.types import CreateEmbeddingResponse
-from openai.types.chat import ChatCompletion
+from openai.types.chat import (
+    ChatCompletion,
+    ChatCompletionMessage,
+    ChatCompletionMessageParam,
+)
 from pydantic import SecretStr
 
 from autogpt.core.configuration import Configurable, UserConfigurable
 from autogpt.core.resource.model_providers.schema import (
     AssistantChatMessage,
+    AssistantFunctionCall,
     AssistantToolCall,
     AssistantToolCallDict,
     ChatMessage,
@@ -406,83 +411,90 @@ class OpenAIProvider(
     ) -> ChatModelResponse[_T]:
         """Create a completion using the OpenAI API."""
 
-        completion_kwargs = self._get_completion_kwargs(model_name, functions, **kwargs)
-        tool_calls_compat_mode = functions and "tools" not in completion_kwargs
-        if "messages" in completion_kwargs:
-            model_prompt += completion_kwargs["messages"]
-            del completion_kwargs["messages"]
+        openai_messages, completion_kwargs = self._get_chat_completion_args(
+            model_prompt, model_name, functions, **kwargs
+        )
+        tool_calls_compat_mode = bool(functions and "tools" not in completion_kwargs)
 
-        cost = 0.0
+        total_cost = 0.0
         attempts = 0
         while True:
-            _response = await self._create_chat_completion(
-                messages=model_prompt,
+            _response, _cost, t_input, t_output = await self._create_chat_completion(
+                messages=openai_messages,
                 **completion_kwargs,
             )
-
-            _assistant_msg = _response.choices[0].message
-            assistant_msg = AssistantChatMessage(
-                content=_assistant_msg.content,
-                tool_calls=(
-                    [AssistantToolCall(**tc.dict()) for tc in _assistant_msg.tool_calls]
-                    if _assistant_msg.tool_calls
-                    else None
-                ),
-            )
-            response = ChatModelResponse(
-                response=assistant_msg,
-                model_info=OPEN_AI_CHAT_MODELS[model_name],
-                prompt_tokens_used=(
-                    _response.usage.prompt_tokens if _response.usage else 0
-                ),
-                completion_tokens_used=(
-                    _response.usage.completion_tokens if _response.usage else 0
-                ),
-            )
-            cost += self._budget.update_usage_and_cost(response)
-            self._logger.debug(
-                f"Completion usage: {response.prompt_tokens_used} input, "
-                f"{response.completion_tokens_used} output - ${round(cost, 5)}"
-            )
+            total_cost += _cost
 
             # If parsing the response fails, append the error to the prompt, and let the
             # LLM fix its mistake(s).
-            try:
-                attempts += 1
+            attempts += 1
+            parse_errors: list[Exception] = []
 
-                if (
-                    tool_calls_compat_mode
-                    and assistant_msg.content
-                    and not assistant_msg.tool_calls
-                ):
-                    assistant_msg.tool_calls = list(
-                        _tool_calls_compat_extract_calls(assistant_msg.content)
+            _assistant_msg = _response.choices[0].message
+
+            tool_calls, _errors = self._parse_assistant_tool_calls(
+                _assistant_msg, tool_calls_compat_mode
+            )
+            parse_errors += _errors
+
+            assistant_msg = AssistantChatMessage(
+                content=_assistant_msg.content,
+                tool_calls=tool_calls or None,
+            )
+
+            parsed_result: _T = None  # type: ignore
+            if not parse_errors:
+                try:
+                    parsed_result = completion_parser(assistant_msg)
+                except Exception as e:
+                    parse_errors.append(e)
+
+            if not parse_errors:
+                if attempts > 1:
+                    self._logger.debug(
+                        f"Total cost for {attempts} attempts: ${round(total_cost, 5)}"
                     )
 
-                response.parsed_result = completion_parser(assistant_msg)
-                break
-            except Exception as e:
-                self._logger.warning(f"Parsing attempt #{attempts} failed: {e}")
-                self._logger.debug(f"Parsing failed on response: '''{assistant_msg}'''")
-                sentry_sdk.capture_exception(
-                    error=e,
-                    extras={"assistant_msg": assistant_msg, "i_attempt": attempts},
+                return ChatModelResponse(
+                    response=AssistantChatMessage(
+                        content=_assistant_msg.content,
+                        tool_calls=tool_calls or None,
+                    ),
+                    parsed_result=parsed_result,
+                    model_info=OPEN_AI_CHAT_MODELS[model_name],
+                    prompt_tokens_used=t_input,
+                    completion_tokens_used=t_output,
                 )
-                if attempts < self._configuration.fix_failed_parse_tries:
-                    model_prompt.append(assistant_msg)
-                    model_prompt.append(
-                        ChatMessage.system(
-                            "ERROR PARSING YOUR RESPONSE:\n\n"
-                            f"{e.__class__.__name__}: {e}"
-                        )
+
+            else:
+                self._logger.debug(
+                    f"Parsing failed on response: '''{_assistant_msg}'''"
+                )
+                self._logger.warning(
+                    f"Parsing attempt #{attempts} failed: {parse_errors}"
+                )
+                for e in parse_errors:
+                    sentry_sdk.capture_exception(
+                        error=e,
+                        extras={"assistant_msg": _assistant_msg, "i_attempt": attempts},
                     )
+
+                if attempts < self._configuration.fix_failed_parse_tries:
+                    openai_messages.append(_assistant_msg.dict(exclude_none=True))
+                    openai_messages.append(
+                        {
+                            "role": "system",
+                            "content": (
+                                "ERROR PARSING YOUR RESPONSE:\n\n"
+                                + "\n\n".join(
+                                    f"{e.__class__.__name__}: {e}" for e in parse_errors
+                                )
+                            ),
+                        }
+                    )
+                    continue
                 else:
-                    raise
-
-        if attempts > 1:
-            self._logger.debug(f"Total cost for {attempts} attempts: ${round(cost, 5)}")
-
-        return response
+                    raise parse_errors[0]
 
     async def create_embedding(
         self,
@@ -504,21 +516,24 @@ class OpenAIProvider(
         self._budget.update_usage_and_cost(response)
         return response
 
-    def _get_completion_kwargs(
+    def _get_chat_completion_args(
         self,
+        model_prompt: list[ChatMessage],
         model_name: OpenAIModelName,
         functions: Optional[list[CompletionModelFunction]] = None,
         **kwargs,
-    ) -> dict:
-        """Get kwargs for completion API call.
+    ) -> tuple[list[ChatCompletionMessageParam], dict[str, Any]]:
+        """Prepare chat completion arguments and keyword arguments for API call.
 
         Args:
-            model: The model to use.
-            kwargs: Keyword arguments to override the default values.
+            model_prompt: List of ChatMessages.
+            model_name: The model to use.
+            functions: Optional list of functions available to the LLM.
+            kwargs: Additional keyword arguments.
 
         Returns:
-            The kwargs for the chat API call.
-
+            list[ChatCompletionMessageParam]: Prompt messages for the OpenAI call
+            dict[str, Any]: Any other kwargs for the OpenAI call
         """
         kwargs.update(self._credentials.get_model_access_kwargs(model_name))
 
@@ -541,7 +556,19 @@ class OpenAIProvider(
             kwargs["extra_headers"] = kwargs.get("extra_headers", {})
             kwargs["extra_headers"].update(extra_headers.copy())
 
-        return kwargs
+        if "messages" in kwargs:
+            model_prompt += kwargs["messages"]
+            del kwargs["messages"]
+
+        openai_messages: list[ChatCompletionMessageParam] = [
+            message.dict(
+                include={"role", "content", "tool_calls", "name"},
+                exclude_none=True,
+            )
+            for message in model_prompt
+        ]
+
+        return openai_messages, kwargs
 
     def _get_embedding_kwargs(
         self,
@@ -566,28 +593,106 @@ class OpenAIProvider(
 
         return kwargs
 
-    def _create_chat_completion(
-        self, messages: list[ChatMessage], *_, **kwargs
-    ) -> Coroutine[None, None, ChatCompletion]:
-        """Create a chat completion using the OpenAI API with retry handling."""
+    async def _create_chat_completion(
+        self,
+        messages: list[ChatCompletionMessageParam],
+        model: OpenAIModelName,
+        *_,
+        **kwargs,
+    ) -> tuple[ChatCompletion, float, int, int]:
+        """
+        Create a chat completion using the OpenAI API with retry handling.
+
+        Params:
+            openai_messages: List of OpenAI-consumable message dict objects
+            model: The model to use for the completion
+
+        Returns:
+            ChatCompletion: The chat completion response object
+            float: The cost ($) of this completion
+            int: Number of prompt tokens used
+            int: Number of completion tokens used
+        """
 
         @self._retry_api_request
         async def _create_chat_completion_with_retry(
-            messages: list[ChatMessage], *_, **kwargs
+            messages: list[ChatCompletionMessageParam], **kwargs
         ) -> ChatCompletion:
-            raw_messages = [
-                message.dict(
-                    include={"role", "content", "tool_calls", "name"},
-                    exclude_none=True,
-                )
-                for message in messages
-            ]
             return await self._client.chat.completions.create(
-                messages=raw_messages,  # type: ignore
+                messages=messages,  # type: ignore
                 **kwargs,
             )
 
-        return _create_chat_completion_with_retry(messages, *_, **kwargs)
+        completion = await _create_chat_completion_with_retry(
+            messages, model=model, **kwargs
+        )
+
+        if completion.usage:
+            prompt_tokens_used = completion.usage.prompt_tokens
+            completion_tokens_used = completion.usage.completion_tokens
+        else:
+            prompt_tokens_used = completion_tokens_used = 0
+
+        cost = self._budget.update_usage_and_cost(
+            ChatModelResponse(
+                response=AssistantChatMessage(content=None),
+                model_info=OPEN_AI_CHAT_MODELS[model],
+                prompt_tokens_used=prompt_tokens_used,
+                completion_tokens_used=completion_tokens_used,
+            )
+        )
+        self._logger.debug(
+            f"Completion usage: {prompt_tokens_used} input, "
+            f"{completion_tokens_used} output - ${round(cost, 5)}"
+        )
+        return completion, cost, prompt_tokens_used, completion_tokens_used
+
+    def _parse_assistant_tool_calls(
+        self, assistant_message: ChatCompletionMessage, compat_mode: bool = False
+    ):
+        tool_calls: list[AssistantToolCall] = []
+        parse_errors: list[Exception] = []
+
+        if assistant_message.tool_calls:
+            for _tc in assistant_message.tool_calls:
+                try:
+                    parsed_arguments = json_loads(_tc.function.arguments)
+                except Exception as e:
+                    err_message = (
+                        f"Decoding arguments for {_tc.function.name} failed: "
+                        + str(e.args[0])
+                    )
+                    parse_errors.append(
+                        type(e)(err_message, *e.args[1:]).with_traceback(
+                            e.__traceback__
+                        )
+                    )
+                    continue
+
+                tool_calls.append(
+                    AssistantToolCall(
+                        id=_tc.id,
+                        type=_tc.type,
+                        function=AssistantFunctionCall(
+                            name=_tc.function.name,
+                            arguments=parsed_arguments,
+                        ),
+                    )
+                )
+
+            # If parsing of all tool calls succeeds in the end, we ignore any issues
+            if len(tool_calls) == len(assistant_message.tool_calls):
+                parse_errors = []
+
+        elif compat_mode and assistant_message.content:
+            try:
+                tool_calls = list(
+                    _tool_calls_compat_extract_calls(assistant_message.content)
+                )
+            except Exception as e:
+                parse_errors.append(e)
+
+        return tool_calls, parse_errors
 
     def _create_embedding(
         self, text: str, *_, **kwargs

autogpts/autogpt/autogpt/core/resource/model_providers/schema.py

@@ -2,6 +2,7 @@ import abc
 import enum
 import math
 from typing import (
+    Any,
     Callable,
     ClassVar,
     Generic,
@@ -68,12 +69,12 @@ class ChatMessageDict(TypedDict):
 
 class AssistantFunctionCall(BaseModel):
     name: str
-    arguments: str
+    arguments: dict[str, Any]
 
 
 class AssistantFunctionCallDict(TypedDict):
     name: str
-    arguments: str
+    arguments: dict[str, Any]
 
 
 class AssistantToolCall(BaseModel):

autogpts/autogpt/autogpt/core/utils/json_utils.py

@@ -1,4 +1,3 @@
-import io
 import logging
 import re
 from typing import Any
@@ -32,16 +31,18 @@ def json_loads(json_str: str) -> Any:
     if match:
         json_str = match.group(1).strip()
 
-    error_buffer = io.StringIO()
-    json_result = demjson3.decode(
-        json_str, return_errors=True, write_errors=error_buffer
-    )
+    json_result = demjson3.decode(json_str, return_errors=True)
+    assert json_result is not None  # by virtue of return_errors=True
 
-    if error_buffer.getvalue():
-        logger.debug(f"JSON parse errors:\n{error_buffer.getvalue()}")
+    if json_result.errors:
+        logger.debug(
+            "JSON parse errors:\n" + "\n".join(str(e) for e in json_result.errors)
+        )
 
-    if json_result is None:
-        raise ValueError(f"Failed to parse JSON string: {json_str}")
+    if json_result.object is demjson3.undefined:
+        raise ValueError(
+            f"Failed to parse JSON string: {json_str}", *json_result.errors
+        )
 
     return json_result.object