beaver_project/app-instance/backend/beaver/engine/providers/anthropic.py

"""Native Anthropic Messages API provider."""

from __future__ import annotations

import json
from typing import Any

from .base import LLMProvider, LLMResponse, ToolCallRequest

try:  # pragma: no cover - optional dependency
    import anthropic
except ModuleNotFoundError:  # pragma: no cover
    anthropic = None  # type: ignore[assignment]


class AnthropicProvider(LLMProvider):
    """使用 Anthropic 原生 Messages API，而不是强行走 OpenAI-compatible path。"""

    def __init__(
        self,
        api_key: str | None = None,
        default_model: str = "claude-sonnet-4-5",
        api_base: str | None = None,
        request_timeout_seconds: float | None = None,
    ) -> None:
        super().__init__(api_key, api_base, request_timeout_seconds=request_timeout_seconds)
        self.default_model = default_model
        self._client = None

    def _client_or_raise(self):
        if anthropic is None:
            raise RuntimeError("anthropic package is not installed")
        if self._client is None:
            self._client = anthropic.AsyncAnthropic(
                api_key=self.api_key,
                base_url=self.api_base,
                timeout=self.request_timeout_seconds,
            )
        return self._client

    async def chat(
        self,
        messages: list[dict[str, Any]],
        tools: list[dict[str, Any]] | None = None,
        model: str | None = None,
        max_tokens: int | None = None,
        temperature: float = 0.7,
        thinking_enabled: bool | None = None,
    ) -> LLMResponse:
        try:
            client = self._client_or_raise()
        except Exception as exc:
            return LLMResponse(content=f"Error: {exc}", finish_reason="error", provider_name="anthropic")

        system_prompt, anthropic_messages = _convert_messages(messages)
        kwargs: dict[str, Any] = {
            "model": model or self.default_model,
            "system": system_prompt or "",
            "messages": anthropic_messages,
            "temperature": temperature,
        }
        resolved_max_tokens = (
            _default_max_tokens_for_model(model or self.default_model)
            if max_tokens is None
            else max(1, max_tokens)
        )
        kwargs["max_tokens"] = resolved_max_tokens
        if tools:
            kwargs["tools"] = _convert_tools(tools)

        try:
            response = await client.messages.create(**kwargs)
        except Exception as exc:
            return LLMResponse(content=f"Error: {exc}", finish_reason="error", provider_name="anthropic")

        content_parts: list[str] = []
        tool_calls: list[ToolCallRequest] = []
        for block in response.content:
            if block.type == "text":
                content_parts.append(block.text)
            elif block.type == "tool_use":
                tool_calls.append(
                    ToolCallRequest(
                        id=block.id,
                        name=block.name,
                        arguments=block.input,
                    )
                )
        usage_payload = {}
        if getattr(response, "usage", None):
            usage_payload = {
                "input_tokens": getattr(response.usage, "input_tokens", 0),
                "output_tokens": getattr(response.usage, "output_tokens", 0),
            }
        return LLMResponse(
            content="".join(content_parts) or None,
            tool_calls=tool_calls,
            finish_reason=getattr(response, "stop_reason", "stop") or "stop",
            usage=usage_payload,
            provider_name="anthropic",
            model=model or self.default_model,
        )

    def get_default_model(self) -> str:
        return self.default_model


def _default_max_tokens_for_model(model: str) -> int:
    """Return a conservative native output ceiling for Anthropic Messages."""

    normalized = model.lower().replace("_", "-")
    if "sonnet-4" in normalized or "opus-4" in normalized or "3-7" in normalized or "3.7" in normalized:
        return 64_000
    if "haiku" in normalized:
        return 4_096
    return 8_192


def _convert_messages(messages: list[dict[str, Any]]) -> tuple[str, list[dict[str, Any]]]:
    system_prompt = ""
    converted: list[dict[str, Any]] = []
    for message in messages:
        role = message.get("role")
        if role == "system":
            content = message.get("content")
            system_prompt = content if isinstance(content, str) else ""
            continue
        if role == "tool":
            converted.append(
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "tool_result",
                            "tool_use_id": message.get("tool_call_id"),
                            "content": message.get("content") or "",
                        }
                    ],
                }
            )
            continue
        if role == "assistant" and message.get("tool_calls"):
            content_blocks: list[dict[str, Any]] = []
            if message.get("content"):
                content_blocks.append({"type": "text", "text": message["content"]})
            for tool_call in message.get("tool_calls", []):
                function = tool_call.get("function", tool_call)
                arguments = function.get("arguments")
                if isinstance(arguments, str):
                    try:
                        arguments = json.loads(arguments)
                    except json.JSONDecodeError:
                        arguments = {}
                content_blocks.append(
                    {
                        "type": "tool_use",
                        "id": tool_call.get("id"),
                        "name": function.get("name"),
                        "input": arguments or {},
                    }
                )
            converted.append({"role": "assistant", "content": content_blocks})
            continue

        content = message.get("content")
        if isinstance(content, list):
            blocks = []
            for item in content:
                if isinstance(item, dict) and item.get("type") == "text":
                    blocks.append({"type": "text", "text": item.get("text", "")})
            converted.append({"role": role, "content": blocks or [{"type": "text", "text": ""}]})
        else:
            converted.append({"role": role, "content": content or ""})
    return system_prompt, converted


def _convert_tools(tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
    converted: list[dict[str, Any]] = []
    for tool in tools:
        fn = (tool.get("function") or {}) if tool.get("type") == "function" else tool
        if not fn.get("name"):
            continue
        converted.append(
            {
                "name": fn["name"],
                "description": fn.get("description") or "",
                "input_schema": fn.get("parameters") or {"type": "object", "properties": {}},
            }
        )
    return converted