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♻️ refactor!: 重构LLM服务架构并统一Pydantic兼容性处理 (#2002)
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* ♻️ refactor(pydantic): 提取 Pydantic 兼容函数到独立模块

* ♻️ refactor!(llm): 重构LLM服务，引入现代化工具和执行器架构

🏗️ **架构变更**
- 引入ToolProvider/ToolExecutable协议，取代ToolRegistry
- 新增LLMToolExecutor，分离工具调用逻辑
- 新增BaseMemory抽象，解耦会话状态管理

🔄 **API重构**
- 移除：analyze, analyze_multimodal, pipeline_chat
- 新增：generate_structured, run_with_tools
- 重构：chat, search, code变为无状态调用

🛠️ **工具系统**
- 新增@function_tool装饰器
- 统一工具定义到ToolExecutable协议
- 移除MCP工具系统和mcp_tools.json

---------

Co-authored-by: webjoin111 <455457521@qq.com>
This commit is contained in:
Rumio
2025-08-04 23:36:12 +08:00
committed by GitHub
co-authored by webjoin111
parent 59d72c3b3d
commit 7c153721f0
27 changed files with 1495 additions and 1795 deletions
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zhenxun/services/llm/adapters/gemini.py
+72 -62
View File
@@ -7,6 +7,7 @@ from typing import TYPE_CHECKING, Any
from zhenxun.services.log import logger
from ..types.exceptions import LLMErrorCode, LLMException
from ..utils import sanitize_schema_for_llm
from .base import BaseAdapter, RequestData, ResponseData
if TYPE_CHECKING:
@@ -14,7 +15,8 @@ if TYPE_CHECKING:
from ..service import LLMModel
from ..types.content import LLMMessage
from ..types.enums import EmbeddingTaskType
from ..types.models import LLMTool, LLMToolCall
from ..types.models import LLMToolCall
from ..types.protocols import ToolExecutable
class GeminiAdapter(BaseAdapter):
@@ -44,7 +46,7 @@ class GeminiAdapter(BaseAdapter):
api_key: str,
messages: list["LLMMessage"],
config: "LLMGenerationConfig | None" = None,
tools: list["LLMTool"] | None = None,
tools: dict[str, "ToolExecutable"] | None = None,
tool_choice: str | dict[str, Any] | None = None,
) -> RequestData:
"""准备高级请求"""
@@ -128,11 +130,22 @@ class GeminiAdapter(BaseAdapter):
)
tool_result_obj = {"raw_output": content_str}
if isinstance(tool_result_obj, list):
logger.debug(
f"工具 '{msg.name}' 的返回结果是列表，"
f"正在为Gemini API包装为JSON对象。"
)
final_response_payload = {"result": tool_result_obj}
elif not isinstance(tool_result_obj, dict):
final_response_payload = {"result": tool_result_obj}
else:
final_response_payload = tool_result_obj
current_parts.append(
{
"functionResponse": {
"name": msg.name,
"response": tool_result_obj,
"response": final_response_payload,
}
}
)
@@ -145,22 +158,26 @@ class GeminiAdapter(BaseAdapter):
all_tools_for_request = []
if tools:
for tool in tools:
if tool.type == "function" and tool.function:
all_tools_for_request.append(
{"functionDeclarations": [tool.function]}
)
elif tool.type == "mcp" and tool.mcp_session:
if callable(tool.mcp_session):
raise ValueError(
"适配器接收到未激活的 MCP 会话工厂。"
"会话工厂应该在 LLMModel.generate_response 中被激活。"
)
all_tools_for_request.append(
tool.mcp_session.to_api_tool(api_type=self.api_type)
)
elif tool.type == "google_search":
all_tools_for_request.append({"googleSearch": {}})
import asyncio
from zhenxun.utils.pydantic_compat import model_dump
definition_tasks = [
executable.get_definition() for executable in tools.values()
]
tool_definitions = await asyncio.gather(*definition_tasks)
function_declarations = []
for tool_def in tool_definitions:
tool_def.parameters = sanitize_schema_for_llm(
tool_def.parameters, api_type="gemini"
)
function_declarations.append(model_dump(tool_def))
if function_declarations:
all_tools_for_request.append(
{"functionDeclarations": function_declarations}
)
if effective_config:
if getattr(effective_config, "enable_grounding", False):
@@ -289,49 +306,21 @@ class GeminiAdapter(BaseAdapter):
self, model: "LLMModel", config: "LLMGenerationConfig | None" = None
) -> dict[str, Any]:
"""构建Gemini生成配置"""
generation_config: dict[str, Any] = {}
effective_config = config if config is not None else model._generation_config
if effective_config:
base_api_params = effective_config.to_api_params(
api_type="gemini", model_name=model.model_name
if not effective_config:
return {}
generation_config = effective_config.to_api_params(
api_type="gemini", model_name=model.model_name
)
if generation_config:
param_keys = list(generation_config.keys())
logger.debug(
f"构建Gemini生成配置完成，包含 {len(generation_config)} 个参数: "
f"{param_keys}"
)
generation_config.update(base_api_params)
if getattr(effective_config, "response_mime_type", None):
generation_config["responseMimeType"] = (
effective_config.response_mime_type
)
if getattr(effective_config, "response_schema", None):
generation_config["responseSchema"] = effective_config.response_schema
thinking_budget = getattr(effective_config, "thinking_budget", None)
if thinking_budget is not None:
if "thinkingConfig" not in generation_config:
generation_config["thinkingConfig"] = {}
generation_config["thinkingConfig"]["thinkingBudget"] = thinking_budget
if getattr(effective_config, "response_modalities", None):
modalities = effective_config.response_modalities
if isinstance(modalities, list):
generation_config["responseModalities"] = [
m.upper() for m in modalities
]
elif isinstance(modalities, str):
generation_config["responseModalities"] = [modalities.upper()]
generation_config = {
k: v for k, v in generation_config.items() if v is not None
}
if generation_config:
param_keys = list(generation_config.keys())
logger.debug(
f"构建Gemini生成配置完成，包含 {len(generation_config)} 个参数: "
f"{param_keys}"
)
return generation_config
@@ -410,10 +399,16 @@ class GeminiAdapter(BaseAdapter):
text_content = ""
parsed_tool_calls: list["LLMToolCall"] | None = None
thought_summary_parts = []
answer_parts = []
for part in parts:
if "text" in part:
text_content += part["text"]
answer_parts.append(part["text"])
elif "thought" in part:
thought_summary_parts.append(part["thought"])
elif "thoughtSummary" in part:
thought_summary_parts.append(part["thoughtSummary"])
elif "functionCall" in part:
if parsed_tool_calls is None:
parsed_tool_calls = []
@@ -445,12 +440,27 @@ class GeminiAdapter(BaseAdapter):
result = part["codeExecutionResult"]
if result.get("outcome") == "OK":
output = result.get("output", "")
text_content += f"\n[代码执行结果]:\n{output}\n"
answer_parts.append(f"\n[代码执行结果]:\n```\n{output}\n```\n")
else:
text_content += (
answer_parts.append(
f"\n[代码执行失败]: {result.get('outcome', 'UNKNOWN')}\n"
)
if thought_summary_parts:
full_thought_summary = "\n".join(thought_summary_parts).strip()
full_answer = "".join(answer_parts).strip()
formatted_parts = []
if full_thought_summary:
formatted_parts.append(f"🤔 **思考过程**\n\n{full_thought_summary}")
if full_answer:
separator = "\n\n---\n\n" if full_thought_summary else ""
formatted_parts.append(f"{separator}✅ **回答**\n\n{full_answer}")
text_content = "".join(formatted_parts)
else:
text_content = "".join(answer_parts)
usage_info = response_json.get("usageMetadata")
grounding_metadata_obj = None