claw/insightflow
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix: auto-fix code issues (cron)

Browse Source 
- 修复重复导入/字段
- 修复异常处理
- 修复PEP8格式问题
- 添加类型注解
This commit is contained in:
AutoFix Bot
2026-03-02 12:14:39 +08:00
parent e23f1fec08
commit 98527c4de4
39 changed files with 8109 additions and 8147 deletions
Download Patch File Download Diff File Expand all files Collapse all files

106
backend/llm_client.py
View File

@@ -12,8 +12,8 @@ from dataclasses import dataclass
import httpx
KIMI_API_KEY = os.getenv("KIMI_API_KEY", "")
KIMI_BASE_URL = os.getenv("KIMI_BASE_URL", "https://api.kimi.com/coding")
KIMI_API_KEY = os.getenv("KIMI_API_KEY", "")
KIMI_BASE_URL = os.getenv("KIMI_BASE_URL", "https://api.kimi.com/coding")
@dataclass
@@ -41,22 +41,22 @@ class RelationExtractionResult:
class LLMClient:
"""Kimi API 客户端"""
def __init__(self, api_key: str = None, base_url: str = None) -> None:
self.api_key = api_key or KIMI_API_KEY
self.base_url = base_url or KIMI_BASE_URL
self.headers = {
def __init__(self, api_key: str = None, base_url: str = None) -> None:
self.api_key = api_key or KIMI_API_KEY
self.base_url = base_url or KIMI_BASE_URL
self.headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
async def chat(
self, messages: list[ChatMessage], temperature: float = 0.3, stream: bool = False
self, messages: list[ChatMessage], temperature: float = 0.3, stream: bool = False
) -> str:
"""发送聊天请求"""
if not self.api_key:
raise ValueError("KIMI_API_KEY not set")
payload = {
payload = {
"model": "k2p5",
"messages": [{"role": m.role, "content": m.content} for m in messages],
"temperature": temperature,
@@ -64,24 +64,24 @@ class LLMClient:
}
async with httpx.AsyncClient() as client:
response = await client.post(
response = await client.post(
f"{self.base_url}/v1/chat/completions",
headers = self.headers,
json = payload,
timeout = 120.0,
headers=self.headers,
json=payload,
timeout=120.0,
)
response.raise_for_status()
result = response.json()
result = response.json()
return result["choices"][0]["message"]["content"]
async def chat_stream(
self, messages: list[ChatMessage], temperature: float = 0.3
self, messages: list[ChatMessage], temperature: float = 0.3
) -> AsyncGenerator[str, None]:
"""流式聊天请求"""
if not self.api_key:
raise ValueError("KIMI_API_KEY not set")
payload = {
payload = {
"model": "k2p5",
"messages": [{"role": m.role, "content": m.content} for m in messages],
"temperature": temperature,
@@ -92,19 +92,19 @@ class LLMClient:
async with client.stream(
"POST",
f"{self.base_url}/v1/chat/completions",
headers = self.headers,
json = payload,
timeout = 120.0,
headers=self.headers,
json=payload,
timeout=120.0,
) as response:
response.raise_for_status()
async for line in response.aiter_lines():
if line.startswith("data: "):
data = line[6:]
data = line[6:]
if data == "[DONE]":
break
try:
chunk = json.loads(data)
delta = chunk["choices"][0]["delta"]
chunk = json.loads(data)
delta = chunk["choices"][0]["delta"]
if "content" in delta:
yield delta["content"]
except (json.JSONDecodeError, KeyError, IndexError):
@@ -114,7 +114,7 @@ class LLMClient:
self, text: str
) -> tuple[list[EntityExtractionResult], list[RelationExtractionResult]]:
"""提取实体和关系，带置信度分数"""
prompt = f"""从以下会议文本中提取关键实体和它们之间的关系，以 JSON 格式返回：
prompt = f"""从以下会议文本中提取关键实体和它们之间的关系，以 JSON 格式返回：
文本：{text[:3000]}
@@ -139,30 +139,30 @@ class LLMClient:
]
}}"""
messages = [ChatMessage(role = "user", content = prompt)]
content = await self.chat(messages, temperature = 0.1)
messages = [ChatMessage(role="user", content=prompt)]
content = await self.chat(messages, temperature=0.1)
json_match = re.search(r"\{{.*?\}}", content, re.DOTALL)
json_match = re.search(r"\{{.*?\}}", content, re.DOTALL)
if not json_match:
return [], []
try:
data = json.loads(json_match.group())
entities = [
data = json.loads(json_match.group())
entities = [
EntityExtractionResult(
name = e["name"],
type = e.get("type", "OTHER"),
definition = e.get("definition", ""),
confidence = e.get("confidence", 0.8),
name=e["name"],
type=e.get("type", "OTHER"),
definition=e.get("definition", ""),
confidence=e.get("confidence", 0.8),
)
for e in data.get("entities", [])
]
relations = [
relations = [
RelationExtractionResult(
source = r["source"],
target = r["target"],
type = r.get("type", "related"),
confidence = r.get("confidence", 0.8),
source=r["source"],
target=r["target"],
type=r.get("type", "related"),
confidence=r.get("confidence", 0.8),
)
for r in data.get("relations", [])
]
@@ -173,10 +173,10 @@ class LLMClient:
async def rag_query(self, query: str, context: str, project_context: dict) -> str:
"""RAG 问答 - 基于项目上下文回答问题"""
prompt = f"""你是一个专业的项目分析助手。基于以下项目信息回答问题：
prompt = f"""你是一个专业的项目分析助手。基于以下项目信息回答问题：
## 项目信息
{json.dumps(project_context, ensure_ascii = False, indent = 2)}
{json.dumps(project_context, ensure_ascii=False, indent=2)}
## 相关上下文
{context[:4000]}
@@ -186,21 +186,21 @@ class LLMClient:
请用中文回答，保持简洁专业。如果信息不足，请明确说明。"""
messages = [
messages = [
ChatMessage(
role = "system", content = "你是一个专业的项目分析助手，擅长从会议记录中提取洞察。"
role="system", content="你是一个专业的项目分析助手，擅长从会议记录中提取洞察。"
),
ChatMessage(role = "user", content = prompt),
ChatMessage(role="user", content=prompt),
]
return await self.chat(messages, temperature = 0.3)
return await self.chat(messages, temperature=0.3)
async def agent_command(self, command: str, project_context: dict) -> dict:
"""Agent 指令解析 - 将自然语言指令转换为结构化操作"""
prompt = f"""解析以下用户指令，转换为结构化操作：
prompt = f"""解析以下用户指令，转换为结构化操作：
## 项目信息
{json.dumps(project_context, ensure_ascii = False, indent = 2)}
{json.dumps(project_context, ensure_ascii=False, indent=2)}
## 用户指令
{command}
@@ -221,10 +221,10 @@ class LLMClient:
- create_relation: 创建关系，params 包含 source(源实体), target(目标实体), relation_type(关系类型)
"""
messages = [ChatMessage(role = "user", content = prompt)]
content = await self.chat(messages, temperature = 0.1)
messages = [ChatMessage(role="user", content=prompt)]
content = await self.chat(messages, temperature=0.1)
json_match = re.search(r"\{{.*?\}}", content, re.DOTALL)
json_match = re.search(r"\{{.*?\}}", content, re.DOTALL)
if not json_match:
return {"intent": "unknown", "explanation": "无法解析指令"}
@@ -235,14 +235,14 @@ class LLMClient:
async def analyze_entity_evolution(self, entity_name: str, mentions: list[dict]) -> str:
"""分析实体在项目中的演变/态度变化"""
mentions_text = "\n".join(
mentions_text = "\n".join(
[
f"[{m.get('created_at', '未知时间')}] {m.get('text_snippet', '')}"
for m in mentions[:20]
] # 限制数量
)
prompt = f"""分析实体 "{entity_name}" 在项目中的演变和态度变化：
prompt = f"""分析实体 "{entity_name}" 在项目中的演变和态度变化：
## 提及记录
{mentions_text}
@@ -255,16 +255,16 @@ class LLMClient:
用中文回答，结构清晰。"""
messages = [ChatMessage(role = "user", content = prompt)]
return await self.chat(messages, temperature = 0.3)
messages = [ChatMessage(role="user", content=prompt)]
return await self.chat(messages, temperature=0.3)
# Singleton instance
_llm_client = None
_llm_client = None
def get_llm_client() -> LLMClient:
global _llm_client
if _llm_client is None:
_llm_client = LLMClient()
_llm_client = LLMClient()
return _llm_client