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AutoGPT/v2: First pass with small fixes

Browse Source 
* 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
This commit is contained in:
Reinier van der Leer
2023-09-17 16:40:56 +02:00
parent f4d319cee4
commit 11920b8fe5
18 changed files with 449 additions and 149 deletions
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2
autogpts/autogpt/autogpt/core/ability/simple.py
View File

@@ -48,7 +48,7 @@ class SimpleAbilityRegistry(AbilityRegistry, Configurable):
self._memory = memory
self._workspace = workspace
self._model_providers = model_providers
self._abilities = []
self._abilities: list[Ability] = []
for (
ability_name,
ability_configuration,

1
autogpts/autogpt/autogpt/core/planning/__init__.py
View File

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

39
autogpts/autogpt/autogpt/core/planning/schema.py
View File

@@ -1,4 +1,5 @@
import enum
from typing import Optional
from pydantic import BaseModel, Field
@@ -6,7 +7,6 @@ from autogpt.core.ability.schema import AbilityResult
from autogpt.core.resource.model_providers.schema import (
LanguageModelFunction,
LanguageModelMessage,
LanguageModelProviderModelResponse,
)
@@ -19,8 +19,8 @@ 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):
@@ -28,34 +28,33 @@ class LanguageModelPrompt(BaseModel):
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)

5
autogpts/autogpt/autogpt/core/planning/simple.py
View File

@@ -14,14 +14,15 @@ from autogpt.core.planning import strategies
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 @@ class SimplePlanner(Configurable):
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,

18
autogpts/autogpt/autogpt/core/planning/strategies/initial_plan.py
View File

@@ -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 @@ from autogpt.core.resource.model_providers import (
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 @@ class InitialPlan(PromptStrategy):
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

27
autogpts/autogpt/autogpt/core/planning/strategies/name_and_goals.py
View File

@@ -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 @@ from autogpt.core.resource.model_providers import (
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 @@ class NameAndGoals(PromptStrategy):
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

28
autogpts/autogpt/autogpt/core/planning/strategies/next_ability.py
View File

@@ -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 @@ from autogpt.core.resource.model_providers import (
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 @@ class NextAbility(PromptStrategy):
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

144
autogpts/autogpt/autogpt/core/poetry.lock generated
View File

@@ -832,6 +832,103 @@ files = [
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]
[[package]]
name = "regex"
version = "2023.8.8"
description = "Alternative regular expression module, to replace re."
optional = false
python-versions = ">=3.6"
files = [
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]
[[package]]
name = "requests"
version = "2.31.0"
@@ -881,6 +978,51 @@ anyio = ">=3.4.0,<5"
[package.extras]
full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart", "pyyaml"]
[[package]]
name = "tiktoken"
version = "0.5.1"
description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
optional = false
python-versions = ">=3.8"
files = [
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{file = "tiktoken-0.5.1.tar.gz", hash = "sha256:27e773564232004f4f810fd1f85236673ec3a56ed7f1206fc9ed8670ebedb97a"},
]
[package.dependencies]
regex = ">=2022.1.18"
requests = ">=2.26.0"
[package.extras]
blobfile = ["blobfile (>=2)"]
[[package]]
name = "tomli"
version = "2.0.1"
@@ -1044,4 +1186,4 @@ multidict = ">=4.0"
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "f0352811def6ea015f9ea62162354da6ae075102b6dc19893ca3b93c8f1179f8"
content-hash = "50ad53581d2716ee6927df7200b2522acfaad35aadc76909bdab4073c49a824e"

1
autogpts/autogpt/autogpt/core/pyproject.toml
View File

@@ -28,6 +28,7 @@ inflection = "^0.5.1"
openai = "^0.28.0"
pydantic = "^1.10.12"
pyyaml = "^6.0.0"
tiktoken = "^0.5.1"
[build-system]
requires = ["poetry-core"]

35
autogpts/autogpt/autogpt/core/resource/model_providers/__init__.py
View File

@@ -6,39 +6,48 @@ from autogpt.core.resource.model_providers.openai import (
)
from autogpt.core.resource.model_providers.schema import (
Embedding,
EmbeddingModelInfo,
EmbeddingModelProvider,
EmbeddingModelProviderModelInfo,
EmbeddingModelProviderModelResponse,
EmbeddingModelResponse,
LanguageModelFunction,
LanguageModelInfo,
LanguageModelMessage,
LanguageModelProvider,
LanguageModelProviderModelInfo,
LanguageModelProviderModelResponse,
LanguageModelResponse,
MessageRole,
ModelInfo,
ModelProvider,
ModelProviderBudget,
ModelProviderCredentials,
ModelProviderModelInfo,
ModelProviderModelResponse,
ModelProviderName,
ModelProviderService,
ModelProviderSettings,
ModelProviderUsage,
ModelResponse,
)
__all__ = [
"ModelProvider",
"ModelProviderName",
"ModelProviderSettings",
"Embedding",
"EmbeddingModelInfo",
"EmbeddingModelProvider",
"EmbeddingModelProviderModelResponse",
"LanguageModelProvider",
"LanguageModelProviderModelResponse",
"EmbeddingModelResponse",
"LanguageModelFunction",
"LanguageModelInfo",
"LanguageModelMessage",
"LanguageModelProvider",
"LanguageModelResponse",
"MessageRole",
"OpenAIModelName",
"ModelInfo",
"ModelProvider",
"ModelProviderBudget",
"ModelProviderCredentials",
"ModelProviderName",
"ModelProviderService",
"ModelProviderSettings",
"ModelProviderUsage",
"ModelResponse",
"OPEN_AI_MODELS",
"OpenAIModelName",
"OpenAIProvider",
"OpenAISettings",
]

178
autogpts/autogpt/autogpt/core/resource/model_providers/openai.py
View File

@@ -6,6 +6,7 @@ import time
from typing import Callable, ParamSpec, TypeVar
import openai
import tiktoken
from openai.error import APIError, RateLimitError
from autogpt.core.configuration import (
@@ -16,13 +17,13 @@ from autogpt.core.configuration import (
from autogpt.core.resource.model_providers.schema import (
Embedding,
EmbeddingModelProvider,
EmbeddingModelProviderModelInfo,
EmbeddingModelProviderModelResponse,
EmbeddingModelInfo,
EmbeddingModelResponse,
LanguageModelFunction,
LanguageModelMessage,
LanguageModelProvider,
LanguageModelProviderModelInfo,
LanguageModelProviderModelResponse,
LanguageModelInfo,
LanguageModelResponse,
ModelProviderBudget,
ModelProviderCredentials,
ModelProviderName,
@@ -37,19 +38,31 @@ OpenAIChatParser = Callable[[str], dict]
class OpenAIModelName(str, enum.Enum):
ADA = "text-embedding-ada-002"
GPT3 = "gpt-3.5-turbo-0613"
GPT3_16K = "gpt-3.5-turbo-16k-0613"
GPT4 = "gpt-4-0613"
GPT4_32K = "gpt-4-32k-0613"
GPT3_v1 = "gpt-3.5-turbo-0301"
GPT3_v2 = "gpt-3.5-turbo-0613"
GPT3_v2_16k = "gpt-3.5-turbo-16k-0613"
GPT3_ROLLING = "gpt-3.5-turbo"
GPT3_ROLLING_16k = "gpt-3.5-turbo-16k"
GPT3 = GPT3_ROLLING
GPT3_16k = GPT3_ROLLING_16k
GPT4_v1 = "gpt-4-0314"
GPT4_v1_32k = "gpt-4-32k-0314"
GPT4_v2 = "gpt-4-0613"
GPT4_v2_32k = "gpt-4-32k-0613"
GPT4_ROLLING = "gpt-4"
GPT4_ROLLING_32k = "gpt-4-32k"
GPT4 = GPT4_ROLLING
GPT4_32k = GPT4_ROLLING_32k
OPEN_AI_EMBEDDING_MODELS = {
OpenAIModelName.ADA: EmbeddingModelProviderModelInfo(
OpenAIModelName.ADA: EmbeddingModelInfo(
name=OpenAIModelName.ADA,
service=ModelProviderService.EMBEDDING,
provider_name=ModelProviderName.OPENAI,
prompt_token_cost=0.0004,
completion_token_cost=0.0,
prompt_token_cost=0.0001/1000,
max_tokens=8191,
embedding_dimensions=1536,
),
@@ -57,39 +70,54 @@ OPEN_AI_EMBEDDING_MODELS = {
OPEN_AI_LANGUAGE_MODELS = {
OpenAIModelName.GPT3: LanguageModelProviderModelInfo(
name=OpenAIModelName.GPT3,
service=ModelProviderService.LANGUAGE,
provider_name=ModelProviderName.OPENAI,
prompt_token_cost=0.0015,
completion_token_cost=0.002,
max_tokens=4096,
),
OpenAIModelName.GPT3_16K: LanguageModelProviderModelInfo(
name=OpenAIModelName.GPT3,
service=ModelProviderService.LANGUAGE,
provider_name=ModelProviderName.OPENAI,
prompt_token_cost=0.003,
completion_token_cost=0.002,
max_tokens=16384,
),
OpenAIModelName.GPT4: LanguageModelProviderModelInfo(
name=OpenAIModelName.GPT4,
service=ModelProviderService.LANGUAGE,
provider_name=ModelProviderName.OPENAI,
prompt_token_cost=0.03,
completion_token_cost=0.06,
max_tokens=8192,
),
OpenAIModelName.GPT4_32K: LanguageModelProviderModelInfo(
name=OpenAIModelName.GPT4_32K,
service=ModelProviderService.LANGUAGE,
provider_name=ModelProviderName.OPENAI,
prompt_token_cost=0.06,
completion_token_cost=0.12,
max_tokens=32768,
),
info.name: info
for info in [
LanguageModelInfo(
name=OpenAIModelName.GPT3,
service=ModelProviderService.LANGUAGE,
provider_name=ModelProviderName.OPENAI,
prompt_token_cost=0.0015/1000,
completion_token_cost=0.002/1000,
max_tokens=4096,
),
LanguageModelInfo(
name=OpenAIModelName.GPT3_16k,
service=ModelProviderService.LANGUAGE,
provider_name=ModelProviderName.OPENAI,
prompt_token_cost=0.003/1000,
completion_token_cost=0.004/1000,
max_tokens=16384,
),
LanguageModelInfo(
name=OpenAIModelName.GPT4,
service=ModelProviderService.LANGUAGE,
provider_name=ModelProviderName.OPENAI,
prompt_token_cost=0.03/1000,
completion_token_cost=0.06/1000,
max_tokens=8192,
),
LanguageModelInfo(
name=OpenAIModelName.GPT4_32k,
service=ModelProviderService.LANGUAGE,
provider_name=ModelProviderName.OPENAI,
prompt_token_cost=0.06/1000,
completion_token_cost=0.12/1000,
max_tokens=32768,
),
]
}
# Copy entries for equivalent models
chat_model_mapping = {
OpenAIModelName.GPT3: [OpenAIModelName.GPT3_v1, OpenAIModelName.GPT3_v2],
OpenAIModelName.GPT3_16k: [OpenAIModelName.GPT3_v2_16k],
OpenAIModelName.GPT4: [OpenAIModelName.GPT4_v1, OpenAIModelName.GPT4_v2],
OpenAIModelName.GPT4_32k: [OpenAIModelName.GPT4_v1_32k, OpenAIModelName.GPT4_v2_32k],
}
for base, copies in chat_model_mapping.items():
for copy in copies:
copy_info = LanguageModelInfo(**OPEN_AI_LANGUAGE_MODELS[base].__dict__)
copy_info.name = copy
OPEN_AI_LANGUAGE_MODELS[copy] = copy_info
OPEN_AI_MODELS = {
@@ -113,11 +141,7 @@ class OpenAISettings(ModelProviderSettings):
budget: OpenAIModelProviderBudget
class OpenAIProvider(
Configurable,
LanguageModelProvider,
EmbeddingModelProvider,
):
class OpenAIProvider(Configurable, LanguageModelProvider, EmbeddingModelProvider):
default_settings = OpenAISettings(
name="openai_provider",
description="Provides access to OpenAI's API.",
@@ -139,6 +163,8 @@ class OpenAIProvider(
),
)
logger = logging.getLogger("model_providers.OpenAIProvider")
def __init__(
self,
settings: OpenAISettings,
@@ -166,14 +192,60 @@ class OpenAIProvider(
"""Get the remaining budget."""
return self._budget.remaining_budget
def count_tokens(self, text: str, model_name: OpenAIModelName) -> int:
encoding = tiktoken.encoding_for_model(model_name)
return len(encoding.encode(text))
def count_message_tokens(
self,
messages: LanguageModelMessage | list[LanguageModelMessage],
model_name: OpenAIModelName,
) -> int:
if isinstance(messages, LanguageModelMessage):
messages = [messages]
if model_name.startswith("gpt-3.5-turbo"):
tokens_per_message = (
4 # every message follows <|start|>{role/name}\n{content}<|end|>\n
)
tokens_per_name = -1 # if there's a name, the role is omitted
encoding_model = "gpt-3.5-turbo"
elif model_name.startswith("gpt-4"):
tokens_per_message = 3
tokens_per_name = 1
encoding_model = "gpt-4"
else:
raise NotImplementedError(
f"count_message_tokens() is not implemented for model {model_name}.\n"
" See https://github.com/openai/openai-python/blob/main/chatml.md for"
" information on how messages are converted to tokens."
)
try:
encoding = tiktoken.encoding_for_model(encoding_model)
except KeyError:
self.logger.warn(
f"Model {model_name} not found. Defaulting to cl100k_base encoding."
)
encoding = tiktoken.get_encoding("cl100k_base")
num_tokens = 0
for message in messages:
num_tokens += tokens_per_message
for key, value in message.dict().items():
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
num_tokens += 3 # every reply is primed with <|start|>assistant<|message|>
return num_tokens
async def create_language_completion(
self,
model_prompt: list[LanguageModelMessage],
functions: list[LanguageModelFunction],
model_name: OpenAIModelName,
completion_parser: Callable[[dict], dict],
functions: list[LanguageModelFunction] = [],
**kwargs,
) -> LanguageModelProviderModelResponse:
) -> LanguageModelResponse:
"""Create a completion using the OpenAI API."""
completion_kwargs = self._get_completion_kwargs(model_name, functions, **kwargs)
response = await self._create_completion(
@@ -189,7 +261,7 @@ class OpenAIProvider(
parsed_response = completion_parser(
response.choices[0].message.to_dict_recursive()
)
response = LanguageModelProviderModelResponse(
response = LanguageModelResponse(
content=parsed_response, **response_args
)
self._budget.update_usage_and_cost(response)
@@ -201,7 +273,7 @@ class OpenAIProvider(
model_name: OpenAIModelName,
embedding_parser: Callable[[Embedding], Embedding],
**kwargs,
) -> EmbeddingModelProviderModelResponse:
) -> EmbeddingModelResponse:
"""Create an embedding using the OpenAI API."""
embedding_kwargs = self._get_embedding_kwargs(model_name, **kwargs)
response = await self._create_embedding(text=text, **embedding_kwargs)
@@ -211,7 +283,7 @@ class OpenAIProvider(
"prompt_tokens_used": response.usage.prompt_tokens,
"completion_tokens_used": response.usage.completion_tokens,
}
response = EmbeddingModelProviderModelResponse(
response = EmbeddingModelResponse(
**response_args,
embedding=embedding_parser(response.embeddings[0]),
)

49
autogpts/autogpt/autogpt/core/resource/model_providers/schema.py
View File

@@ -18,13 +18,13 @@ from autogpt.core.resource.schema import (
class ModelProviderService(str, enum.Enum):
"""A ModelService describes what kind of service the model provides."""
EMBEDDING: str = "embedding"
LANGUAGE: str = "language"
TEXT: str = "text"
EMBEDDING = "embedding"
LANGUAGE = "language"
TEXT = "text"
class ModelProviderName(str, enum.Enum):
OPENAI: str = "openai"
OPENAI = "openai"
class MessageRole(str, enum.Enum):
@@ -42,7 +42,7 @@ class LanguageModelFunction(BaseModel):
json_schema: dict
class ModelProviderModelInfo(BaseModel):
class ModelInfo(BaseModel):
"""Struct for model information.
Would be lovely to eventually get this directly from APIs, but needs to be
@@ -57,12 +57,12 @@ class ModelProviderModelInfo(BaseModel):
completion_token_cost: float = 0.0
class ModelProviderModelResponse(BaseModel):
class ModelResponse(BaseModel):
"""Standard response struct for a response from a model."""
prompt_tokens_used: int
completion_tokens_used: int
model_info: ModelProviderModelInfo
model_info: ModelInfo
class ModelProviderCredentials(ProviderCredentials):
@@ -101,7 +101,7 @@ class ModelProviderUsage(ProviderUsage):
def update_usage(
self,
model_response: ModelProviderModelResponse,
model_response: ModelResponse,
) -> None:
self.completion_tokens += model_response.completion_tokens_used
self.prompt_tokens += model_response.prompt_tokens_used
@@ -118,7 +118,7 @@ class ModelProviderBudget(ProviderBudget):
def update_usage_and_cost(
self,
model_response: ModelProviderModelResponse,
model_response: ModelResponse,
) -> None:
"""Update the usage and cost of the provider."""
model_info = model_response.model_info
@@ -126,7 +126,7 @@ class ModelProviderBudget(ProviderBudget):
incremental_cost = (
model_response.completion_tokens_used * model_info.completion_token_cost
+ model_response.prompt_tokens_used * model_info.prompt_token_cost
) / 1000.0
)
self.total_cost += incremental_cost
self.remaining_budget -= incremental_cost
@@ -142,6 +142,10 @@ class ModelProvider(abc.ABC):
defaults: ClassVar[ModelProviderSettings]
@abc.abstractmethod
def count_tokens(self, text: str, model_name: str) -> int:
...
@abc.abstractmethod
def get_token_limit(self, model_name: str) -> int:
...
@@ -156,14 +160,15 @@ class ModelProvider(abc.ABC):
####################
class EmbeddingModelProviderModelInfo(ModelProviderModelInfo):
class EmbeddingModelInfo(ModelInfo):
"""Struct for embedding model information."""
llm_service: ModelProviderService = ModelProviderService.EMBEDDING
llm_service = ModelProviderService.EMBEDDING
max_tokens: int
embedding_dimensions: int
class EmbeddingModelProviderModelResponse(ModelProviderModelResponse):
class EmbeddingModelResponse(ModelResponse):
"""Standard response struct for a response from an embedding model."""
embedding: Embedding = Field(default_factory=list)
@@ -184,7 +189,7 @@ class EmbeddingModelProvider(ModelProvider):
model_name: str,
embedding_parser: Callable[[Embedding], Embedding],
**kwargs,
) -> EmbeddingModelProviderModelResponse:
) -> EmbeddingModelResponse:
...
@@ -193,20 +198,28 @@ class EmbeddingModelProvider(ModelProvider):
###################
class LanguageModelProviderModelInfo(ModelProviderModelInfo):
class LanguageModelInfo(ModelInfo):
"""Struct for language model information."""
llm_service: ModelProviderService = ModelProviderService.LANGUAGE
llm_service = ModelProviderService.LANGUAGE
max_tokens: int
class LanguageModelProviderModelResponse(ModelProviderModelResponse):
class LanguageModelResponse(ModelResponse):
"""Standard response struct for a response from a language model."""
content: dict = None
class LanguageModelProvider(ModelProvider):
@abc.abstractmethod
def count_message_tokens(
self,
messages: LanguageModelMessage | list[LanguageModelMessage],
model_name: str,
) -> int:
...
@abc.abstractmethod
async def create_language_completion(
self,
@@ -215,5 +228,5 @@ class LanguageModelProvider(ModelProvider):
model_name: str,
completion_parser: Callable[[dict], dict],
**kwargs,
) -> LanguageModelProviderModelResponse:
) -> LanguageModelResponse:
...

6
autogpts/autogpt/autogpt/core/runner/cli_app/main.py
View File

@@ -44,20 +44,20 @@ async def run_auto_gpt(user_configuration: dict):
agent_settings,
client_logger,
)
print(parse_agent_name_and_goals(name_and_goals))
print("\n" + parse_agent_name_and_goals(name_and_goals))
# Finally, update the agent settings with the name and goals.
agent_settings.update_agent_name_and_goals(name_and_goals)
# Step 3. Provision the agent.
agent_workspace = SimpleAgent.provision_agent(agent_settings, client_logger)
print("agent is provisioned")
client_logger.info("Agent is provisioned")
# launch agent interaction loop
agent = SimpleAgent.from_workspace(
agent_workspace,
client_logger,
)
print("agent is loaded")
client_logger.info("Agent is loaded")
plan = await agent.build_initial_plan()
print(parse_agent_plan(plan))

22
autogpts/autogpt/autogpt/core/runner/client_lib/logging/__init__.py Normal file
View File

@@ -0,0 +1,22 @@
import logging
from .config import configure_root_logger, FancyConsoleFormatter, BelowLevelFilter
from .helpers import dump_prompt
def get_client_logger():
# Configure logging before we do anything else.
# Application logs need a place to live.
client_logger = logging.getLogger("autogpt_client_application")
client_logger.setLevel(logging.DEBUG)
return client_logger
__all__ = [
"configure_root_logger",
"get_client_logger",
"FancyConsoleFormatter",
"BelowLevelFilter",
"dump_prompt",
]

11
autogpts/autogpt/autogpt/core/runner/client_lib/logging.py → autogpts/autogpt/autogpt/core/runner/client_lib/logging/config.py
View File

@@ -24,16 +24,7 @@ def configure_root_logger():
logging.basicConfig(level=logging.DEBUG, handlers=[stdout, stderr])
# Disable debug logging from OpenAI library
openai_logger.setLevel(logging.INFO)
def get_client_logger():
# Configure logging before we do anything else.
# Application logs need a place to live.
client_logger = logging.getLogger("autogpt_client_application")
client_logger.setLevel(logging.DEBUG)
return client_logger
openai_logger.setLevel(logging.WARNING)
class FancyConsoleFormatter(logging.Formatter):

21
autogpts/autogpt/autogpt/core/runner/client_lib/logging/helpers.py Normal file
View File

@@ -0,0 +1,21 @@
from math import ceil, floor
from autogpt.core.planning import LanguageModelPrompt
SEPARATOR_LENGTH = 42
def dump_prompt(prompt: LanguageModelPrompt) -> str:
def separator(text: str):
half_sep_len = (SEPARATOR_LENGTH - 2 - len(text)) / 2
return f"{floor(half_sep_len)*'-'} {text.upper()} {ceil(half_sep_len)*'-'}"
formatted_messages = "\n".join(
[f"{separator(m.role)}\n{m.content}" for m in prompt.messages]
)
return f"""
============== {prompt.__class__.__name__} ==============
Length: {len(prompt.messages)} messages
{formatted_messages}
==========================================
"""

6
autogpts/autogpt/autogpt/core/runner/client_lib/parser.py
View File

@@ -8,15 +8,15 @@ def parse_agent_name_and_goals(name_and_goals: dict) -> str:
def parse_agent_plan(plan: dict) -> str:
parsed_response = f"Agent Plan:\n"
parsed_response = "Agent Plan:\n"
for i, task in enumerate(plan["task_list"]):
parsed_response += f"{i+1}. {task['objective']}\n"
parsed_response += f"Task type: {task['type']} "
parsed_response += f"Priority: {task['priority']}\n"
parsed_response += f"Ready Criteria:\n"
parsed_response += "Ready Criteria:\n"
for j, criteria in enumerate(task["ready_criteria"]):
parsed_response += f" {j+1}. {criteria}\n"
parsed_response += f"Acceptance Criteria:\n"
parsed_response += "Acceptance Criteria:\n"
for j, criteria in enumerate(task["acceptance_criteria"]):
parsed_response += f" {j+1}. {criteria}\n"
parsed_response += "\n"

5
autogpts/autogpt/autogpt/logs/formatters.py
View File

@@ -21,7 +21,10 @@ class AutoGptFormatter(FancyConsoleFormatter):
# Determine color for title
title = getattr(record, "title", "")
title_color = getattr(record, "title_color", "") or self.LEVEL_COLOR_MAP.get(record.levelno, "")
title_color = (
getattr(record, "title_color", "")
or self.LEVEL_COLOR_MAP.get(record.levelno, "")
)
if title and title_color:
title = f"{title_color + Style.BRIGHT}{title}{Style.RESET_ALL}"
# Make sure record.title is set, and padded with a space if not empty