mirror of
https://github.com/affaan-m/everything-claude-code.git
synced 2026-04-13 21:33:32 +08:00
fix: resolve git conflicts in LLM abstraction layer
- Fix gui() function import in __init__.py (use cli.selector) - Fix prompt builder system message merging logic - Add default max_tokens for Anthropic API in claude.py - Fix openai tool_call arguments parsing with json.loads - Fix test_builder.py PromptConfig import and assertions
This commit is contained in:
Anish
162
pyproject.toml
162
pyproject.toml
@@ -1,84 +1,78 @@
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[project]
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name = "llm-abstraction"
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version = "0.1.0"
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description = "Provider-agnostic LLM abstraction layer"
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readme = "README.md"
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requires-python = ">=3.11"
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license = {text = "MIT"}
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authors = [
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{name = "Affaan Mustafa", email = "affaan@example.com"}
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]
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keywords = ["llm", "openai", "anthropic", "ollama", "ai"]
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classifiers = [
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"Development Status :: 3 - Alpha",
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"Intended Audience :: Developers",
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"License :: OSI Approved :: MIT License",
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3.11",
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"Programming Language :: Python :: 3.12",
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]
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dependencies = [
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"anthropic>=0.25.0",
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"openai>=1.30.0",
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]
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[project.optional-dependencies]
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dev = [
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"pytest>=8.0",
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"pytest-asyncio>=0.23",
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"pytest-cov>=4.1",
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"ruff>=0.4",
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"mypy>=1.10",
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"ruff>=0.4",
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]
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test = [
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"pytest>=8.0",
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"pytest-asyncio>=0.23",
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"pytest-cov>=4.1",
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"pytest-mock>=3.12",
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]
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[project.urls]
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Homepage = "https://github.com/affaan-m/everything-claude-code"
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Repository = "https://github.com/affaan-m/everything-claude-code"
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[project.scripts]
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llm-select = "llm.cli.selector:main"
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["src/llm"]
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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asyncio_mode = "auto"
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filterwarnings = ["ignore::DeprecationWarning"]
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[tool.coverage.run]
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source = ["src/llm"]
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branch = true
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[tool.coverage.report]
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exclude_lines = [
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"pragma: no cover",
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"if TYPE_CHECKING:",
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"raise NotImplementedError",
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]
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[tool.ruff]
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src-path = ["src"]
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target-version = "py311"
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[tool.ruff.lint]
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select = ["E", "F", "I", "N", "W", "UP"]
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ignore = ["E501"]
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[tool.mypy]
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python_version = "3.11"
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src_paths = ["src"]
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warn_return_any = true
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warn_unused_ignores = true
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[project]
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name = "llm-abstraction"
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version = "0.1.0"
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description = "Provider-agnostic LLM abstraction layer"
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readme = "README.md"
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requires-python = ">=3.11"
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license = {text = "MIT"}
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authors = [
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{name = "Affaan Mustafa", email = "affaan@example.com"}
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]
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keywords = ["llm", "openai", "anthropic", "ollama", "ai"]
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classifiers = [
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"Development Status :: 3 - Alpha",
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"Intended Audience :: Developers",
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"License :: OSI Approved :: MIT License",
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3.11",
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"Programming Language :: Python :: 3.12",
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]
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dependencies = [
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"anthropic>=0.25.0",
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"openai>=1.30.0",
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]
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[project.optional-dependencies]
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dev = [
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"pytest>=8.0",
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"pytest-asyncio>=0.23",
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"pytest-cov>=4.1",
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"pytest-mock>=3.12",
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"ruff>=0.4",
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"mypy>=1.10",
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]
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[project.urls]
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Homepage = "https://github.com/affaan-m/everything-claude-code"
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Repository = "https://github.com/affaan-m/everything-claude-code"
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[project.scripts]
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llm-select = "llm.cli.selector:main"
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["src/llm"]
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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asyncio_mode = "auto"
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filterwarnings = ["ignore::DeprecationWarning"]
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[tool.coverage.run]
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source = ["src/llm"]
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branch = true
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[tool.coverage.report]
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exclude_lines = [
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"pragma: no cover",
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"if TYPE_CHECKING:",
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"raise NotImplementedError",
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]
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[tool.ruff]
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src-path = ["src"]
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target-version = "py311"
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[tool.ruff.lint]
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select = ["E", "F", "I", "N", "W", "UP"]
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ignore = ["E501"]
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[tool.mypy]
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python_version = "3.11"
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src_paths = ["src"]
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warn_return_any = true
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warn_unused_ignores = true
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@@ -1,33 +1,33 @@
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"""
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LLM Abstraction Layer
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Provider-agnostic interface for multiple LLM backends.
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"""
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from llm.core.interface import LLMProvider
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from llm.core.types import LLMInput, LLMOutput, Message, ToolCall, ToolDefinition, ToolResult
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from llm.providers import get_provider
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from llm.tools import ToolExecutor, ToolRegistry
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from llm.cli.selector import interactive_select
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__version__ = "0.1.0"
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__all__ = [
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"LLMProvider",
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"LLMInput",
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"LLMOutput",
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"Message",
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"get_provider",
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"ToolCall",
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"ToolDefinition",
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"ToolResult",
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"ToolExecutor",
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"ToolRegistry",
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"interactive_select",
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]
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def gui() -> None:
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from llm.gui.selector import main
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main()
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"""
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LLM Abstraction Layer
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Provider-agnostic interface for multiple LLM backends.
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"""
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from llm.core.interface import LLMProvider
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from llm.core.types import LLMInput, LLMOutput, Message, ToolCall, ToolDefinition, ToolResult
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from llm.providers import get_provider
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from llm.tools import ToolExecutor, ToolRegistry
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from llm.cli.selector import interactive_select
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__version__ = "0.1.0"
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__all__ = [
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"LLMProvider",
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"LLMInput",
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"LLMOutput",
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"Message",
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"get_provider",
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"ToolCall",
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"ToolDefinition",
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"ToolResult",
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"ToolExecutor",
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"ToolRegistry",
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"interactive_select",
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]
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def gui() -> None:
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from llm.cli.selector import main
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main()
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@@ -1,101 +1,102 @@
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"""Prompt builder for normalizing prompts across providers."""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any
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from llm.core.types import LLMInput, Message, Role, ToolDefinition
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from llm.providers.claude import ClaudeProvider
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from llm.providers.openai import OpenAIProvider
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from llm.providers.ollama import OllamaProvider
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@dataclass
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class PromptConfig:
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system_template: str | None = None
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user_template: str | None = None
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include_tools_in_system: bool = True
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tool_format: str = "native"
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class PromptBuilder:
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def __init__(self, config: PromptConfig | None = None) -> None:
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self.config = config or PromptConfig()
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def build(self, messages: list[Message], tools: list[ToolDefinition] | None = None) -> list[Message]:
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if not messages:
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return []
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result: list[Message] = []
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system_parts: list[str] = []
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if self.config.system_template:
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system_parts.append(self.config.system_template)
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if tools and self.config.include_tools_in_system:
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tools_desc = self._format_tools(tools)
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system_parts.append(f"\n\n## Available Tools\n{tools_desc}")
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if messages[0].role == Role.SYSTEM:
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system_parts.insert(0, messages[0].content)
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result.extend(messages[1:])
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else:
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if system_parts:
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result.insert(0, Message(role=Role.SYSTEM, content="\n\n".join(system_parts)))
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result.extend(messages)
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return result
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def _format_tools(self, tools: list[ToolDefinition]) -> str:
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lines = []
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for tool in tools:
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lines.append(f"### {tool.name}")
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lines.append(tool.description)
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if tool.parameters:
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lines.append("Parameters:")
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lines.append(self._format_parameters(tool.parameters))
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return "\n".join(lines)
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def _format_parameters(self, params: dict[str, Any]) -> str:
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if "properties" not in params:
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return str(params)
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lines = []
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required = params.get("required", [])
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for name, spec in params["properties"].items():
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prop_type = spec.get("type", "any")
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desc = spec.get("description", "")
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required_mark = "(required)" if name in required else "(optional)"
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lines.append(f" - {name}: {prop_type} {required_mark} - {desc}")
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return "\n".join(lines) if lines else str(params)
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_PROVIDER_TEMPLATE_MAP: dict[str, dict[str, Any]] = {
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"claude": {
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"include_tools_in_system": False,
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"tool_format": "anthropic",
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},
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"openai": {
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"include_tools_in_system": False,
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"tool_format": "openai",
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},
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"ollama": {
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"include_tools_in_system": True,
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"tool_format": "text",
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},
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}
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def get_provider_builder(provider_name: str) -> PromptBuilder:
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config_dict = _PROVIDER_TEMPLATE_MAP.get(provider_name.lower(), {})
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config = PromptConfig(**config_dict)
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return PromptBuilder(config)
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def adapt_messages_for_provider(
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messages: list[Message],
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provider: str,
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tools: list[ToolDefinition] | None = None,
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) -> list[Message]:
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builder = get_provider_builder(provider)
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return builder.build(messages, tools)
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"""Prompt builder for normalizing prompts across providers."""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any
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from llm.core.types import LLMInput, Message, Role, ToolDefinition
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from llm.providers.claude import ClaudeProvider
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from llm.providers.openai import OpenAIProvider
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from llm.providers.ollama import OllamaProvider
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@dataclass
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class PromptConfig:
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system_template: str | None = None
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user_template: str | None = None
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include_tools_in_system: bool = True
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tool_format: str = "native"
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class PromptBuilder:
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def __init__(self, config: PromptConfig | None = None) -> None:
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self.config = config or PromptConfig()
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def build(self, messages: list[Message], tools: list[ToolDefinition] | None = None) -> list[Message]:
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if not messages:
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return []
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result: list[Message] = []
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system_parts: list[str] = []
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if self.config.system_template:
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system_parts.append(self.config.system_template)
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if tools and self.config.include_tools_in_system:
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tools_desc = self._format_tools(tools)
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system_parts.append(f"\n\n## Available Tools\n{tools_desc}")
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if messages[0].role == Role.SYSTEM:
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system_parts.insert(0, messages[0].content)
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result.insert(0, Message(role=Role.SYSTEM, content="\n\n".join(system_parts)))
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result.extend(messages[1:])
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else:
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if system_parts:
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result.insert(0, Message(role=Role.SYSTEM, content="\n\n".join(system_parts)))
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result.extend(messages)
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return result
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def _format_tools(self, tools: list[ToolDefinition]) -> str:
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lines = []
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for tool in tools:
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lines.append(f"### {tool.name}")
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lines.append(tool.description)
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if tool.parameters:
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lines.append("Parameters:")
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lines.append(self._format_parameters(tool.parameters))
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return "\n".join(lines)
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def _format_parameters(self, params: dict[str, Any]) -> str:
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if "properties" not in params:
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return str(params)
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lines = []
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required = params.get("required", [])
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for name, spec in params["properties"].items():
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prop_type = spec.get("type", "any")
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desc = spec.get("description", "")
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required_mark = "(required)" if name in required else "(optional)"
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lines.append(f" - {name}: {prop_type} {required_mark} - {desc}")
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return "\n".join(lines) if lines else str(params)
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_PROVIDER_TEMPLATE_MAP: dict[str, dict[str, Any]] = {
|
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"claude": {
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"include_tools_in_system": False,
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"tool_format": "anthropic",
|
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},
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"openai": {
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"include_tools_in_system": False,
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"tool_format": "openai",
|
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},
|
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"ollama": {
|
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"include_tools_in_system": True,
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"tool_format": "text",
|
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},
|
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}
|
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|
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|
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def get_provider_builder(provider_name: str) -> PromptBuilder:
|
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config_dict = _PROVIDER_TEMPLATE_MAP.get(provider_name.lower(), {})
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config = PromptConfig(**config_dict)
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return PromptBuilder(config)
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|
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|
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def adapt_messages_for_provider(
|
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messages: list[Message],
|
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provider: str,
|
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tools: list[ToolDefinition] | None = None,
|
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) -> list[Message]:
|
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builder = get_provider_builder(provider)
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return builder.build(messages, tools)
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@@ -1,103 +1,105 @@
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"""Claude provider adapter."""
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from __future__ import annotations
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import os
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from typing import Any
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|
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from anthropic import Anthropic
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from llm.core.interface import (
|
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AuthenticationError,
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ContextLengthError,
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LLMProvider,
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RateLimitError,
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)
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from llm.core.types import LLMInput, LLMOutput, Message, ModelInfo, ProviderType, ToolCall
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class ClaudeProvider(LLMProvider):
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provider_type = ProviderType.CLAUDE
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def __init__(self, api_key: str | None = None, base_url: str | None = None) -> None:
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self.client = Anthropic(api_key=api_key or os.environ.get("ANTHROPIC_API_KEY"), base_url=base_url)
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self._models = [
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ModelInfo(
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name="claude-opus-4-5",
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provider=ProviderType.CLAUDE,
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supports_tools=True,
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supports_vision=True,
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max_tokens=8192,
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context_window=200000,
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),
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ModelInfo(
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||||
name="claude-sonnet-4-7",
|
||||
provider=ProviderType.CLAUDE,
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||||
supports_tools=True,
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||||
supports_vision=True,
|
||||
max_tokens=8192,
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context_window=200000,
|
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),
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ModelInfo(
|
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name="claude-haiku-4-7",
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||||
provider=ProviderType.CLAUDE,
|
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supports_tools=True,
|
||||
supports_vision=False,
|
||||
max_tokens=4096,
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context_window=200000,
|
||||
),
|
||||
]
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|
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def generate(self, input: LLMInput) -> LLMOutput:
|
||||
try:
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||||
params: dict[str, Any] = {
|
||||
"model": input.model or "claude-sonnet-4-7",
|
||||
"messages": [msg.to_dict() for msg in input.messages],
|
||||
"temperature": input.temperature,
|
||||
}
|
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if input.max_tokens:
|
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params["max_tokens"] = input.max_tokens
|
||||
if input.tools:
|
||||
params["tools"] = [tool.to_dict() for tool in input.tools]
|
||||
|
||||
response = self.client.messages.create(**params)
|
||||
|
||||
tool_calls = None
|
||||
if response.content and hasattr(response.content[0], "type"):
|
||||
if response.content[0].type == "tool_use":
|
||||
tool_calls = [
|
||||
ToolCall(
|
||||
id=getattr(response.content[0], "id", ""),
|
||||
name=getattr(response.content[0], "name", ""),
|
||||
arguments=getattr(response.content[0].input, "__dict__", {}),
|
||||
)
|
||||
]
|
||||
|
||||
return LLMOutput(
|
||||
content=response.content[0].text if response.content else "",
|
||||
tool_calls=tool_calls,
|
||||
model=response.model,
|
||||
usage={
|
||||
"input_tokens": response.usage.input_tokens,
|
||||
"output_tokens": response.usage.output_tokens,
|
||||
},
|
||||
stop_reason=response.stop_reason,
|
||||
)
|
||||
except Exception as e:
|
||||
msg = str(e)
|
||||
if "401" in msg or "authentication" in msg.lower():
|
||||
raise AuthenticationError(msg, provider=ProviderType.CLAUDE) from e
|
||||
if "429" in msg or "rate_limit" in msg.lower():
|
||||
raise RateLimitError(msg, provider=ProviderType.CLAUDE) from e
|
||||
if "context" in msg.lower() and "length" in msg.lower():
|
||||
raise ContextLengthError(msg, provider=ProviderType.CLAUDE) from e
|
||||
raise
|
||||
|
||||
def list_models(self) -> list[ModelInfo]:
|
||||
return self._models.copy()
|
||||
|
||||
def validate_config(self) -> bool:
|
||||
return bool(self.client.api_key)
|
||||
|
||||
def get_default_model(self) -> str:
|
||||
return "claude-sonnet-4-7"
|
||||
"""Claude provider adapter."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from typing import Any
|
||||
|
||||
from anthropic import Anthropic
|
||||
|
||||
from llm.core.interface import (
|
||||
AuthenticationError,
|
||||
ContextLengthError,
|
||||
LLMProvider,
|
||||
RateLimitError,
|
||||
)
|
||||
from llm.core.types import LLMInput, LLMOutput, Message, ModelInfo, ProviderType, ToolCall
|
||||
|
||||
|
||||
class ClaudeProvider(LLMProvider):
|
||||
provider_type = ProviderType.CLAUDE
|
||||
|
||||
def __init__(self, api_key: str | None = None, base_url: str | None = None) -> None:
|
||||
self.client = Anthropic(api_key=api_key or os.environ.get("ANTHROPIC_API_KEY"), base_url=base_url)
|
||||
self._models = [
|
||||
ModelInfo(
|
||||
name="claude-opus-4-5",
|
||||
provider=ProviderType.CLAUDE,
|
||||
supports_tools=True,
|
||||
supports_vision=True,
|
||||
max_tokens=8192,
|
||||
context_window=200000,
|
||||
),
|
||||
ModelInfo(
|
||||
name="claude-sonnet-4-7",
|
||||
provider=ProviderType.CLAUDE,
|
||||
supports_tools=True,
|
||||
supports_vision=True,
|
||||
max_tokens=8192,
|
||||
context_window=200000,
|
||||
),
|
||||
ModelInfo(
|
||||
name="claude-haiku-4-7",
|
||||
provider=ProviderType.CLAUDE,
|
||||
supports_tools=True,
|
||||
supports_vision=False,
|
||||
max_tokens=4096,
|
||||
context_window=200000,
|
||||
),
|
||||
]
|
||||
|
||||
def generate(self, input: LLMInput) -> LLMOutput:
|
||||
try:
|
||||
params: dict[str, Any] = {
|
||||
"model": input.model or "claude-sonnet-4-7",
|
||||
"messages": [msg.to_dict() for msg in input.messages],
|
||||
"temperature": input.temperature,
|
||||
}
|
||||
if input.max_tokens:
|
||||
params["max_tokens"] = input.max_tokens
|
||||
else:
|
||||
params["max_tokens"] = 8192 # required by Anthropic API
|
||||
if input.tools:
|
||||
params["tools"] = [tool.to_dict() for tool in input.tools]
|
||||
|
||||
response = self.client.messages.create(**params)
|
||||
|
||||
tool_calls = None
|
||||
if response.content and hasattr(response.content[0], "type"):
|
||||
if response.content[0].type == "tool_use":
|
||||
tool_calls = [
|
||||
ToolCall(
|
||||
id=getattr(response.content[0], "id", ""),
|
||||
name=getattr(response.content[0], "name", ""),
|
||||
arguments=getattr(response.content[0].input, "__dict__", {}),
|
||||
)
|
||||
]
|
||||
|
||||
return LLMOutput(
|
||||
content=response.content[0].text if response.content else "",
|
||||
tool_calls=tool_calls,
|
||||
model=response.model,
|
||||
usage={
|
||||
"input_tokens": response.usage.input_tokens,
|
||||
"output_tokens": response.usage.output_tokens,
|
||||
},
|
||||
stop_reason=response.stop_reason,
|
||||
)
|
||||
except Exception as e:
|
||||
msg = str(e)
|
||||
if "401" in msg or "authentication" in msg.lower():
|
||||
raise AuthenticationError(msg, provider=ProviderType.CLAUDE) from e
|
||||
if "429" in msg or "rate_limit" in msg.lower():
|
||||
raise RateLimitError(msg, provider=ProviderType.CLAUDE) from e
|
||||
if "context" in msg.lower() and "length" in msg.lower():
|
||||
raise ContextLengthError(msg, provider=ProviderType.CLAUDE) from e
|
||||
raise
|
||||
|
||||
def list_models(self) -> list[ModelInfo]:
|
||||
return self._models.copy()
|
||||
|
||||
def validate_config(self) -> bool:
|
||||
return bool(self.client.api_key)
|
||||
|
||||
def get_default_model(self) -> str:
|
||||
return "claude-sonnet-4-7"
|
||||
|
||||
@@ -1,113 +1,114 @@
|
||||
"""OpenAI provider adapter."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from typing import Any
|
||||
|
||||
from openai import OpenAI
|
||||
|
||||
from llm.core.interface import (
|
||||
AuthenticationError,
|
||||
ContextLengthError,
|
||||
LLMProvider,
|
||||
RateLimitError,
|
||||
)
|
||||
from llm.core.types import LLMInput, LLMOutput, Message, ModelInfo, ProviderType, ToolCall
|
||||
|
||||
|
||||
class OpenAIProvider(LLMProvider):
|
||||
provider_type = ProviderType.OPENAI
|
||||
|
||||
def __init__(self, api_key: str | None = None, base_url: str | None = None) -> None:
|
||||
self.client = OpenAI(api_key=api_key or os.environ.get("OPENAI_API_KEY"), base_url=base_url)
|
||||
self._models = [
|
||||
ModelInfo(
|
||||
name="gpt-4o",
|
||||
provider=ProviderType.OPENAI,
|
||||
supports_tools=True,
|
||||
supports_vision=True,
|
||||
max_tokens=4096,
|
||||
context_window=128000,
|
||||
),
|
||||
ModelInfo(
|
||||
name="gpt-4o-mini",
|
||||
provider=ProviderType.OPENAI,
|
||||
supports_tools=True,
|
||||
supports_vision=True,
|
||||
max_tokens=4096,
|
||||
context_window=128000,
|
||||
),
|
||||
ModelInfo(
|
||||
name="gpt-4-turbo",
|
||||
provider=ProviderType.OPENAI,
|
||||
supports_tools=True,
|
||||
supports_vision=True,
|
||||
max_tokens=4096,
|
||||
context_window=128000,
|
||||
),
|
||||
ModelInfo(
|
||||
name="gpt-3.5-turbo",
|
||||
provider=ProviderType.OPENAI,
|
||||
supports_tools=True,
|
||||
supports_vision=False,
|
||||
max_tokens=4096,
|
||||
context_window=16385,
|
||||
),
|
||||
]
|
||||
|
||||
def generate(self, input: LLMInput) -> LLMOutput:
|
||||
try:
|
||||
params: dict[str, Any] = {
|
||||
"model": input.model or "gpt-4o-mini",
|
||||
"messages": [msg.to_dict() for msg in input.messages],
|
||||
"temperature": input.temperature,
|
||||
}
|
||||
if input.max_tokens:
|
||||
params["max_tokens"] = input.max_tokens
|
||||
if input.tools:
|
||||
params["tools"] = [tool.to_dict() for tool in input.tools]
|
||||
|
||||
response = self.client.chat.completions.create(**params)
|
||||
choice = response.choices[0]
|
||||
|
||||
tool_calls = None
|
||||
if choice.message.tool_calls:
|
||||
tool_calls = [
|
||||
ToolCall(
|
||||
id=tc.id or "",
|
||||
name=tc.function.name,
|
||||
arguments={} if tc.function.arguments == "" else tc.function.arguments,
|
||||
)
|
||||
for tc in choice.message.tool_calls
|
||||
]
|
||||
|
||||
return LLMOutput(
|
||||
content=choice.message.content or "",
|
||||
tool_calls=tool_calls,
|
||||
model=response.model,
|
||||
usage={
|
||||
"prompt_tokens": response.usage.prompt_tokens,
|
||||
"completion_tokens": response.usage.completion_tokens,
|
||||
"total_tokens": response.usage.total_tokens,
|
||||
},
|
||||
stop_reason=choice.finish_reason,
|
||||
)
|
||||
except Exception as e:
|
||||
msg = str(e)
|
||||
if "401" in msg or "authentication" in msg.lower():
|
||||
raise AuthenticationError(msg, provider=ProviderType.OPENAI) from e
|
||||
if "429" in msg or "rate_limit" in msg.lower():
|
||||
raise RateLimitError(msg, provider=ProviderType.OPENAI) from e
|
||||
if "context" in msg.lower() and "length" in msg.lower():
|
||||
raise ContextLengthError(msg, provider=ProviderType.OPENAI) from e
|
||||
raise
|
||||
|
||||
def list_models(self) -> list[ModelInfo]:
|
||||
return self._models.copy()
|
||||
|
||||
def validate_config(self) -> bool:
|
||||
return bool(self.client.api_key)
|
||||
|
||||
def get_default_model(self) -> str:
|
||||
return "gpt-4o-mini"
|
||||
"""OpenAI provider adapter."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
from typing import Any
|
||||
|
||||
from openai import OpenAI
|
||||
|
||||
from llm.core.interface import (
|
||||
AuthenticationError,
|
||||
ContextLengthError,
|
||||
LLMProvider,
|
||||
RateLimitError,
|
||||
)
|
||||
from llm.core.types import LLMInput, LLMOutput, Message, ModelInfo, ProviderType, ToolCall
|
||||
|
||||
|
||||
class OpenAIProvider(LLMProvider):
|
||||
provider_type = ProviderType.OPENAI
|
||||
|
||||
def __init__(self, api_key: str | None = None, base_url: str | None = None) -> None:
|
||||
self.client = OpenAI(api_key=api_key or os.environ.get("OPENAI_API_KEY"), base_url=base_url)
|
||||
self._models = [
|
||||
ModelInfo(
|
||||
name="gpt-4o",
|
||||
provider=ProviderType.OPENAI,
|
||||
supports_tools=True,
|
||||
supports_vision=True,
|
||||
max_tokens=4096,
|
||||
context_window=128000,
|
||||
),
|
||||
ModelInfo(
|
||||
name="gpt-4o-mini",
|
||||
provider=ProviderType.OPENAI,
|
||||
supports_tools=True,
|
||||
supports_vision=True,
|
||||
max_tokens=4096,
|
||||
context_window=128000,
|
||||
),
|
||||
ModelInfo(
|
||||
name="gpt-4-turbo",
|
||||
provider=ProviderType.OPENAI,
|
||||
supports_tools=True,
|
||||
supports_vision=True,
|
||||
max_tokens=4096,
|
||||
context_window=128000,
|
||||
),
|
||||
ModelInfo(
|
||||
name="gpt-3.5-turbo",
|
||||
provider=ProviderType.OPENAI,
|
||||
supports_tools=True,
|
||||
supports_vision=False,
|
||||
max_tokens=4096,
|
||||
context_window=16385,
|
||||
),
|
||||
]
|
||||
|
||||
def generate(self, input: LLMInput) -> LLMOutput:
|
||||
try:
|
||||
params: dict[str, Any] = {
|
||||
"model": input.model or "gpt-4o-mini",
|
||||
"messages": [msg.to_dict() for msg in input.messages],
|
||||
"temperature": input.temperature,
|
||||
}
|
||||
if input.max_tokens:
|
||||
params["max_tokens"] = input.max_tokens
|
||||
if input.tools:
|
||||
params["tools"] = [tool.to_dict() for tool in input.tools]
|
||||
|
||||
response = self.client.chat.completions.create(**params)
|
||||
choice = response.choices[0]
|
||||
|
||||
tool_calls = None
|
||||
if choice.message.tool_calls:
|
||||
tool_calls = [
|
||||
ToolCall(
|
||||
id=tc.id or "",
|
||||
name=tc.function.name,
|
||||
arguments={} if not tc.function.arguments else json.loads(tc.function.arguments),
|
||||
)
|
||||
for tc in choice.message.tool_calls
|
||||
]
|
||||
|
||||
return LLMOutput(
|
||||
content=choice.message.content or "",
|
||||
tool_calls=tool_calls,
|
||||
model=response.model,
|
||||
usage={
|
||||
"prompt_tokens": response.usage.prompt_tokens,
|
||||
"completion_tokens": response.usage.completion_tokens,
|
||||
"total_tokens": response.usage.total_tokens,
|
||||
},
|
||||
stop_reason=choice.finish_reason,
|
||||
)
|
||||
except Exception as e:
|
||||
msg = str(e)
|
||||
if "401" in msg or "authentication" in msg.lower():
|
||||
raise AuthenticationError(msg, provider=ProviderType.OPENAI) from e
|
||||
if "429" in msg or "rate_limit" in msg.lower():
|
||||
raise RateLimitError(msg, provider=ProviderType.OPENAI) from e
|
||||
if "context" in msg.lower() and "length" in msg.lower():
|
||||
raise ContextLengthError(msg, provider=ProviderType.OPENAI) from e
|
||||
raise
|
||||
|
||||
def list_models(self) -> list[ModelInfo]:
|
||||
return self._models.copy()
|
||||
|
||||
def validate_config(self) -> bool:
|
||||
return bool(self.client.api_key)
|
||||
|
||||
def get_default_model(self) -> str:
|
||||
return "gpt-4o-mini"
|
||||
|
||||
@@ -1,61 +1,69 @@
|
||||
import pytest
|
||||
from llm.core.types import LLMInput, Message, Role, ToolDefinition
|
||||
from llm.prompt import PromptBuilder, adapt_messages_for_provider
|
||||
|
||||
|
||||
class TestPromptBuilder:
|
||||
def test_build_without_system(self):
|
||||
messages = [Message(role=Role.USER, content="Hello")]
|
||||
builder = PromptBuilder()
|
||||
result = builder.build(messages)
|
||||
|
||||
assert len(result) == 1
|
||||
assert result[0].role == Role.USER
|
||||
|
||||
def test_build_with_system(self):
|
||||
messages = [
|
||||
Message(role=Role.SYSTEM, content="You are helpful."),
|
||||
Message(role=Role.USER, content="Hello"),
|
||||
]
|
||||
builder = PromptBuilder()
|
||||
result = builder.build(messages)
|
||||
|
||||
assert len(result) == 2
|
||||
assert result[0].role == Role.SYSTEM
|
||||
|
||||
def test_build_adds_system_from_config(self):
|
||||
messages = [Message(role=Role.USER, content="Hello")]
|
||||
builder = PromptBuilder(system_template="You are a pirate.")
|
||||
result = builder.build(messages)
|
||||
|
||||
assert len(result) == 2
|
||||
assert "pirate" in result[0].content
|
||||
|
||||
def test_build_with_tools(self):
|
||||
messages = [Message(role=Role.USER, content="Search for something")]
|
||||
tools = [
|
||||
ToolDefinition(name="search", description="Search the web", parameters={}),
|
||||
]
|
||||
builder = PromptBuilder(include_tools_in_system=True)
|
||||
result = builder.build(messages, tools)
|
||||
|
||||
assert len(result) == 2
|
||||
assert "search" in result[0].content
|
||||
assert "Available Tools" in result[0].content
|
||||
|
||||
|
||||
class TestAdaptMessagesForProvider:
|
||||
def test_adapt_for_claude(self):
|
||||
messages = [Message(role=Role.USER, content="Hello")]
|
||||
result = adapt_messages_for_provider(messages, "claude")
|
||||
assert len(result) == 1
|
||||
|
||||
def test_adapt_for_openai(self):
|
||||
messages = [Message(role=Role.USER, content="Hello")]
|
||||
result = adapt_messages_for_provider(messages, "openai")
|
||||
assert len(result) == 1
|
||||
|
||||
def test_adapt_for_ollama(self):
|
||||
messages = [Message(role=Role.USER, content="Hello")]
|
||||
result = adapt_messages_for_provider(messages, "ollama")
|
||||
assert len(result) == 1
|
||||
import pytest
|
||||
from llm.core.types import LLMInput, Message, Role, ToolDefinition
|
||||
from llm.prompt import PromptBuilder, adapt_messages_for_provider
|
||||
from llm.prompt.builder import PromptConfig
|
||||
|
||||
|
||||
class TestPromptBuilder:
|
||||
def test_build_without_system(self):
|
||||
messages = [Message(role=Role.USER, content="Hello")]
|
||||
builder = PromptBuilder()
|
||||
result = builder.build(messages)
|
||||
|
||||
assert len(result) == 1
|
||||
assert result[0].role == Role.USER
|
||||
|
||||
def test_build_with_system(self):
|
||||
messages = [
|
||||
Message(role=Role.SYSTEM, content="You are helpful."),
|
||||
Message(role=Role.USER, content="Hello"),
|
||||
]
|
||||
builder = PromptBuilder()
|
||||
result = builder.build(messages)
|
||||
|
||||
assert len(result) == 2
|
||||
assert result[0].role == Role.SYSTEM
|
||||
|
||||
def test_build_adds_system_from_config(self):
|
||||
messages = [Message(role=Role.USER, content="Hello")]
|
||||
builder = PromptBuilder(system_template="You are a pirate.")
|
||||
result = builder.build(messages)
|
||||
|
||||
assert len(result) == 2
|
||||
assert "pirate" in result[0].content
|
||||
|
||||
def test_build_adds_system_from_config(self):
|
||||
messages = [Message(role=Role.USER, content="Hello")]
|
||||
builder = PromptBuilder(config=PromptConfig(system_template="You are a pirate."))
|
||||
result = builder.build(messages)
|
||||
|
||||
assert len(result) == 2
|
||||
assert "pirate" in result[0].content
|
||||
def test_build_with_tools(self):
|
||||
messages = [Message(role=Role.USER, content="Search for something")]
|
||||
tools = [
|
||||
ToolDefinition(name="search", description="Search the web", parameters={}),
|
||||
]
|
||||
builder = PromptBuilder(include_tools_in_system=True)
|
||||
result = builder.build(messages, tools)
|
||||
|
||||
assert len(result) == 2
|
||||
assert "search" in result[0].content
|
||||
assert "Available Tools" in result[0].content
|
||||
|
||||
|
||||
class TestAdaptMessagesForProvider:
|
||||
def test_adapt_for_claude(self):
|
||||
messages = [Message(role=Role.USER, content="Hello")]
|
||||
result = adapt_messages_for_provider(messages, "claude")
|
||||
assert len(result) == 1
|
||||
|
||||
def test_adapt_for_openai(self):
|
||||
messages = [Message(role=Role.USER, content="Hello")]
|
||||
result = adapt_messages_for_provider(messages, "openai")
|
||||
assert len(result) == 1
|
||||
|
||||
def test_adapt_for_ollama(self):
|
||||
messages = [Message(role=Role.USER, content="Hello")]
|
||||
result = adapt_messages_for_provider(messages, "ollama")
|
||||
assert len(result) == 1
|
||||
|
||||
Reference in New Issue
Block a user