第一次提交

app-instance/backend/nanobot/providers/base.py

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+"""Base LLM provider interface."""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass
+class ToolCallRequest:
+    """A tool call request from the LLM."""
+    id: str
+    name: str
+    arguments: dict[str, Any]
+
+
+@dataclass
+class LLMResponse:
+    """Response from an LLM provider."""
+    content: str | None
+    tool_calls: list[ToolCallRequest] = field(default_factory=list)
+    finish_reason: str = "stop"
+    usage: dict[str, int] = field(default_factory=dict)
+    reasoning_content: str | None = None  # Kimi, DeepSeek-R1 etc.
+    
+    @property
+    def has_tool_calls(self) -> bool:
+        """Check if response contains tool calls."""
+        return len(self.tool_calls) > 0
+
+
+class LLMProvider(ABC):
+    """
+    Abstract base class for LLM providers.
+    
+    Implementations should handle the specifics of each provider's API
+    while maintaining a consistent interface.
+    """
+    
+    def __init__(self, api_key: str | None = None, api_base: str | None = None):
+        self.api_key = api_key
+        self.api_base = api_base
+
+    @staticmethod
+    def _sanitize_empty_content(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        """Replace empty text content that causes provider 400 errors.
+
+        Empty content can appear when MCP tools return nothing. Most providers
+        reject empty-string content or empty text blocks in list content.
+        """
+        result: list[dict[str, Any]] = []
+        for msg in messages:
+            content = msg.get("content")
+
+            if isinstance(content, str) and not content:
+                clean = dict(msg)
+                clean["content"] = None if (msg.get("role") == "assistant" and msg.get("tool_calls")) else "(empty)"
+                result.append(clean)
+                continue
+
+            if isinstance(content, list):
+                filtered = [
+                    item for item in content
+                    if not (
+                        isinstance(item, dict)
+                        and item.get("type") in ("text", "input_text", "output_text")
+                        and not item.get("text")
+                    )
+                ]
+                if len(filtered) != len(content):
+                    clean = dict(msg)
+                    if filtered:
+                        clean["content"] = filtered
+                    elif msg.get("role") == "assistant" and msg.get("tool_calls"):
+                        clean["content"] = None
+                    else:
+                        clean["content"] = "(empty)"
+                    result.append(clean)
+                    continue
+
+            result.append(msg)
+        return result
+    
+    @abstractmethod
+    async def chat(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]] | None = None,
+        model: str | None = None,
+        max_tokens: int = 4096,
+        temperature: float = 0.7,
+    ) -> LLMResponse:
+        """
+        Send a chat completion request.
+        
+        Args:
+            messages: List of message dicts with 'role' and 'content'.
+            tools: Optional list of tool definitions.
+            model: Model identifier (provider-specific).
+            max_tokens: Maximum tokens in response.
+            temperature: Sampling temperature.
+        
+        Returns:
+            LLMResponse with content and/or tool calls.
+        """
+        pass
+    
+    @abstractmethod
+    def get_default_model(self) -> str:
+        """Get the default model for this provider."""
+        pass