Small changes

src/llm/providers/openai.py

@@ -0,0 +1,113 @@
+"""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"