mirror of
https://github.com/aljazceru/Auto-GPT.git
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39c46ef6be
- feat(agent/core): Add `AnthropicProvider`
- Add `ANTHROPIC_API_KEY` to .env.template and docs
Notable differences in logic compared to `OpenAIProvider`:
- Merges subsequent user messages in `AnthropicProvider._get_chat_completion_args`
- Merges and extracts all system messages into `system` parameter in `AnthropicProvider._get_chat_completion_args`
- Supports prefill; merges prefill content (if any) into generated response
- Prompt changes to improve compatibility with `AnthropicProvider`
Anthropic has a slightly different API compared to OpenAI, and has much stricter input validation. E.g. Anthropic only supports a single `system` prompt, where OpenAI allows multiple `system` messages. Anthropic also forbids sequences of multiple `user` or `assistant` messages and requires that messages alternate between roles.
- Move response format instruction from separate message into main system prompt
- Fix clock message format
- Add pre-fill to `OneShot` generated prompt
- refactor(agent/core): Tweak `model_providers.schema`
- Simplify `ModelProviderUsage`
- Remove attribute `total_tokens` as it is always equal to `prompt_tokens + completion_tokens`
- Modify signature of `update_usage(..)`; no longer requires a full `ModelResponse` object as input
- Improve `ModelProviderBudget`
- Change type of attribute `usage` to `defaultdict[str, ModelProviderUsage]` -> allow per-model usage tracking
- Modify signature of `update_usage_and_cost(..)`; no longer requires a full `ModelResponse` object as input
- Allow `ModelProviderBudget` zero-argument instantiation
- Fix type of `AssistantChatMessage.role` to match `ChatMessage.role` (str -> `ChatMessage.Role`)
- Add shared attributes and constructor to `ModelProvider` base class
- Add `max_output_tokens` parameter to `create_chat_completion` interface
- Add pre-filling as a global feature
- Add `prefill_response` field to `ChatPrompt` model
- Add `prefill_response` parameter to `create_chat_completion` interface
- Add `ChatModelProvider.get_available_models()` and remove `ApiManager`
- Remove unused `OpenAIChatParser` typedef in openai.py
- Remove redundant `budget` attribute definition on `OpenAISettings`
- Remove unnecessary `usage` in `OpenAIProvider` > `default_settings` > `budget`
- feat(agent): Allow use of any available LLM provider through `MultiProvider`
- Add `MultiProvider` (`model_providers.multi`)
- Replace all references to / uses of `OpenAIProvider` with `MultiProvider`
- Change type of `Config.smart_llm` and `Config.fast_llm` from `str` to `ModelName`
- feat(agent/core): Validate function call arguments in `create_chat_completion`
- Add `validate_call` method to `CompletionModelFunction` in `model_providers.schema`
- Add `validate_tool_calls` utility function in `model_providers.utils`
- Add tool call validation step to `create_chat_completion` in `OpenAIProvider` and `AnthropicProvider`
- Remove (now redundant) command argument validation logic in agent.py and models/command.py
- refactor(agent): Rename `get_openai_command_specs` to `function_specs_from_commands`
111 lines
2.7 KiB
Python
111 lines
2.7 KiB
Python
from __future__ import annotations
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import os
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import uuid
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from pathlib import Path
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import pytest
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from pytest_mock import MockerFixture
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from autogpt.agents.agent import Agent, AgentConfiguration, AgentSettings
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from autogpt.app.main import _configure_llm_provider
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from autogpt.config import AIProfile, Config, ConfigBuilder
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from autogpt.core.resource.model_providers import ChatModelProvider
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from autogpt.file_storage.local import (
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FileStorage,
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FileStorageConfiguration,
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LocalFileStorage,
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)
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from autogpt.logs.config import configure_logging
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pytest_plugins = [
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"tests.integration.agent_factory",
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"tests.integration.memory.utils",
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"tests.vcr",
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]
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@pytest.fixture()
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def tmp_project_root(tmp_path: Path) -> Path:
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return tmp_path
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@pytest.fixture()
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def app_data_dir(tmp_project_root: Path) -> Path:
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dir = tmp_project_root / "data"
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dir.mkdir(parents=True, exist_ok=True)
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return dir
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@pytest.fixture()
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def storage(app_data_dir: Path) -> FileStorage:
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storage = LocalFileStorage(
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FileStorageConfiguration(root=app_data_dir, restrict_to_root=False)
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)
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storage.initialize()
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return storage
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@pytest.fixture(scope="function")
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def config(
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tmp_project_root: Path,
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app_data_dir: Path,
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mocker: MockerFixture,
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):
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if not os.environ.get("OPENAI_API_KEY"):
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os.environ["OPENAI_API_KEY"] = "sk-dummy"
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config = ConfigBuilder.build_config_from_env(project_root=tmp_project_root)
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config.app_data_dir = app_data_dir
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config.noninteractive_mode = True
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yield config
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@pytest.fixture(scope="session")
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def setup_logger(config: Config):
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configure_logging(
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debug=True,
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log_dir=Path(__file__).parent / "logs",
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plain_console_output=True,
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)
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@pytest.fixture
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def llm_provider(config: Config) -> ChatModelProvider:
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return _configure_llm_provider(config)
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@pytest.fixture
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def agent(
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config: Config, llm_provider: ChatModelProvider, storage: FileStorage
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) -> Agent:
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ai_profile = AIProfile(
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ai_name="Base",
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ai_role="A base AI",
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ai_goals=[],
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)
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agent_settings = AgentSettings(
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name=Agent.default_settings.name,
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description=Agent.default_settings.description,
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agent_id=f"AutoGPT-test-agent-{str(uuid.uuid4())[:8]}",
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ai_profile=ai_profile,
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config=AgentConfiguration(
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fast_llm=config.fast_llm,
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smart_llm=config.smart_llm,
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allow_fs_access=not config.restrict_to_workspace,
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use_functions_api=config.openai_functions,
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),
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history=Agent.default_settings.history.copy(deep=True),
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)
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agent = Agent(
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settings=agent_settings,
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llm_provider=llm_provider,
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file_storage=storage,
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legacy_config=config,
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)
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return agent
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