Small changes

src/llm/core/__init__.py

@@ -0,0 +1 @@
+"""Core module for LLM abstraction layer."""

src/llm/core/interface.py

@@ -0,0 +1,60 @@
+"""LLM Provider interface definition."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Any
+
+from llm.core.types import LLMInput, LLMOutput, ModelInfo, ProviderType
+
+
+class LLMProvider(ABC):
+    provider_type: ProviderType
+
+    @abstractmethod
+    def generate(self, input: LLMInput) -> LLMOutput: ...
+
+    @abstractmethod
+    def list_models(self) -> list[ModelInfo]: ...
+
+    @abstractmethod
+    def validate_config(self) -> bool: ...
+
+    def supports_tools(self) -> bool:
+        return True
+
+    def supports_vision(self) -> bool:
+        return False
+
+    def get_default_model(self) -> str:
+        raise NotImplementedError(f"{self.__class__.__name__} must implement get_default_model")
+
+
+class LLMError(Exception):
+    def __init__(
+        self,
+        message: str,
+        provider: ProviderType | None = None,
+        code: str | None = None,
+        details: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(message)
+        self.message = message
+        self.provider = provider
+        self.code = code
+        self.details = details or {}
+
+
+class AuthenticationError(LLMError): ...
+
+
+class RateLimitError(LLMError): ...
+
+
+class ContextLengthError(LLMError): ...
+
+
+class ModelNotFoundError(LLMError): ...
+
+
+class ToolExecutionError(LLMError): ...

src/llm/core/types.py

@@ -0,0 +1,140 @@
+"""Core type definitions for LLM abstraction layer."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any
+
+
+class Role(str, Enum):
+    SYSTEM = "system"
+    USER = "user"
+    ASSISTANT = "assistant"
+    TOOL = "tool"
+
+
+class ProviderType(str, Enum):
+    CLAUDE = "claude"
+    OPENAI = "openai"
+    OLLAMA = "ollama"
+
+
+@dataclass(frozen=True)
+class Message:
+    role: Role
+    content: str
+    name: str | None = None
+    tool_call_id: str | None = None
+
+    def to_dict(self) -> dict[str, Any]:
+        result: dict[str, Any] = {"role": self.role.value, "content": self.content}
+        if self.name:
+            result["name"] = self.name
+        if self.tool_call_id:
+            result["tool_call_id"] = self.tool_call_id
+        return result
+
+
+@dataclass(frozen=True)
+class ToolDefinition:
+    name: str
+    description: str
+    parameters: dict[str, Any]
+    strict: bool = True
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "name": self.name,
+            "description": self.description,
+            "parameters": self.parameters,
+            "strict": self.strict,
+        }
+
+
+@dataclass(frozen=True)
+class ToolCall:
+    id: str
+    name: str
+    arguments: dict[str, Any]
+
+
+@dataclass(frozen=True)
+class ToolResult:
+    tool_call_id: str
+    content: str
+    is_error: bool = False
+
+
+@dataclass(frozen=True)
+class LLMInput:
+    messages: list[Message]
+    model: str | None = None
+    temperature: float = 1.0
+    max_tokens: int | None = None
+    tools: list[ToolDefinition] | None = None
+    stream: bool = False
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        result: dict[str, Any] = {
+            "messages": [msg.to_dict() for msg in self.messages],
+            "temperature": self.temperature,
+            "stream": self.stream,
+        }
+        if self.model:
+            result["model"] = self.model
+        if self.max_tokens is not None:
+            result["max_tokens"] = self.max_tokens
+        if self.tools:
+            result["tools"] = [tool.to_dict() for tool in self.tools]
+        return result | self.metadata
+
+
+@dataclass(frozen=True)
+class LLMOutput:
+    content: str
+    tool_calls: list[ToolCall] | None = None
+    model: str | None = None
+    usage: dict[str, int] | None = None
+    stop_reason: str | None = None
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    @property
+    def has_tool_calls(self) -> bool:
+        return bool(self.tool_calls)
+
+    def to_dict(self) -> dict[str, Any]:
+        result: dict[str, Any] = {"content": self.content}
+        if self.tool_calls:
+            result["tool_calls"] = [
+                {"id": tc.id, "name": tc.name, "arguments": tc.arguments}
+                for tc in self.tool_calls
+            ]
+        if self.model:
+            result["model"] = self.model
+        if self.usage:
+            result["usage"] = self.usage
+        if self.stop_reason:
+            result["stop_reason"] = self.stop_reason
+        return result | self.metadata
+
+
+@dataclass(frozen=True)
+class ModelInfo:
+    name: str
+    provider: ProviderType
+    supports_tools: bool = True
+    supports_vision: bool = False
+    max_tokens: int | None = None
+    context_window: int | None = None
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "name": self.name,
+            "provider": self.provider.value,
+            "supports_tools": self.supports_tools,
+            "supports_vision": self.supports_vision,
+            "max_tokens": self.max_tokens,
+            "context_window": self.context_window,
+        }