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fix: bug fixes

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zhayujie
2026-02-02 22:22:10 +08:00
parent 5d02acbf37
commit 50e60e6d05
15 changed files with 865 additions and 575 deletions
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23
agent/memory/embedding.py
View File

@@ -45,8 +45,9 @@ class OpenAIEmbeddingProvider(EmbeddingProvider):
self.api_key = api_key
self.api_base = api_base or "https://api.openai.com/v1"
if not self.api_key:
raise ValueError("OpenAI API key is required")
# Validate API key
if not self.api_key or self.api_key in ["", "YOUR API KEY", "YOUR_API_KEY"]:
raise ValueError("OpenAI API key is not configured. Please set 'open_ai_api_key' in config.json")
# Set dimensions based on model
self._dimensions = 1536 if "small" in model else 3072
@@ -65,9 +66,21 @@ class OpenAIEmbeddingProvider(EmbeddingProvider):
"model": self.model
}
response = requests.post(url, headers=headers, json=data, timeout=30)
response.raise_for_status()
return response.json()
try:
response = requests.post(url, headers=headers, json=data, timeout=5)
response.raise_for_status()
return response.json()
except requests.exceptions.ConnectionError as e:
raise ConnectionError(f"Failed to connect to OpenAI API at {url}. Please check your network connection and api_base configuration. Error: {str(e)}")
except requests.exceptions.Timeout as e:
raise TimeoutError(f"OpenAI API request timed out after 10s. Please check your network connection. Error: {str(e)}")
except requests.exceptions.HTTPError as e:
if e.response.status_code == 401:
raise ValueError(f"Invalid OpenAI API key. Please check your 'open_ai_api_key' in config.json")
elif e.response.status_code == 429:
raise ValueError(f"OpenAI API rate limit exceeded. Please try again later.")
else:
raise ValueError(f"OpenAI API request failed: {e.response.status_code} - {e.response.text}")
def embed(self, text: str) -> List[float]:
"""Generate embedding for text"""

17
agent/prompt/builder.py
View File

@@ -279,11 +279,16 @@ def _build_skills_section(skill_manager: Any, tools: Optional[List[Any]], langua
# 添加技能列表（通过skill_manager获取）
try:
skills_prompt = skill_manager.build_skills_prompt()
logger.debug(f"[PromptBuilder] Skills prompt length: {len(skills_prompt) if skills_prompt else 0}")
if skills_prompt:
lines.append(skills_prompt.strip())
lines.append("")
else:
logger.warning("[PromptBuilder] No skills prompt generated - skills_prompt is empty")
except Exception as e:
logger.warning(f"Failed to build skills prompt: {e}")
import traceback
logger.debug(f"Skills prompt error traceback: {traceback.format_exc()}")
return lines
@@ -404,15 +409,17 @@ def _build_workspace_section(workspace_dir: str, language: str, is_first_convers
"这是你的第一次对话！进行以下流程：",
"",
"1. **表达初次启动的感觉** - 像是第一次睁开眼看到世界，带着好奇和期待",
"2. **简短打招呼后，询问核心问题**：",
"2. **简短介绍能力**：一行说明你能帮助解答问题、管理计算机、创造技能，且拥有长期记忆能不断成长",
"3. **询问核心问题**：",
" - 你希望给我起个什么名字？",
" - 我该怎么称呼你？",
" - 你希望我们是什么样的交流风格？（需要举例，如：专业严谨、轻松幽默、温暖友好等）",
"3. **语言风格**：温暖但不过度诗意，带点科技感，保持清晰",
"4. **问题格式**：用分点或换行，让问题清晰易读",
" - 你希望我们是什么样的交流风格？（一行列举选项：如专业严谨、轻松幽默、温暖友好、简洁高效等）",
"4. **风格要求**：温暖自然、简洁清晰，整体控制在 100 字以内",
"5. 收到回复后，用 `write` 工具保存到 USER.md 和 SOUL.md",
"",
"**注意事项**:",
"**重要提醒**:",
"- SOUL.md 和 USER.md 已经在系统提示词中加载，无需再次读取",
"- 能力介绍和交流风格选项都只要一行，保持精简",
"- 不要问太多其他信息（职业、时区等可以后续自然了解）",
"",
])

45
agent/protocol/agent_stream.py
View File

@@ -248,9 +248,14 @@ class AgentStreamExecutor:
# Log tool calls with arguments
tool_calls_str = []
for tc in tool_calls:
args_str = ', '.join([f"{k}={v}" for k, v in tc['arguments'].items()])
if args_str:
tool_calls_str.append(f"{tc['name']}({args_str})")
# Safely handle None or missing arguments
args = tc.get('arguments') or {}
if isinstance(args, dict):
args_str = ', '.join([f"{k}={v}" for k, v in args.items()])
if args_str:
tool_calls_str.append(f"{tc['name']}({args_str})")
else:
tool_calls_str.append(tc['name'])
else:
tool_calls_str.append(tc['name'])
logger.info(f"🔧 {', '.join(tool_calls_str)}")
@@ -511,13 +516,13 @@ class AgentStreamExecutor:
stop_reason = finish_reason
# Handle text content
if "content" in delta and delta["content"]:
content_delta = delta["content"]
content_delta = delta.get("content") or ""
if content_delta:
full_content += content_delta
self._emit_event("message_update", {"delta": content_delta})
# Handle tool calls
if "tool_calls" in delta:
if "tool_calls" in delta and delta["tool_calls"]:
for tc_delta in delta["tool_calls"]:
index = tc_delta.get("index", 0)
@@ -577,7 +582,10 @@ class AgentStreamExecutor:
"抱歉，之前的对话出现了问题。我已清空历史记录，请重新发送你的消息。"
)
# Check if error is retryable (timeout, connection, rate limit, server busy, etc.)
# Check if error is rate limit (429)
is_rate_limit = '429' in error_str_lower or 'rate limit' in error_str_lower
# Check if error is retryable (timeout, connection, server busy, etc.)
is_retryable = any(keyword in error_str_lower for keyword in [
'timeout', 'timed out', 'connection', 'network',
'rate limit', 'overloaded', 'unavailable', 'busy', 'retry',
@@ -585,7 +593,12 @@ class AgentStreamExecutor:
])
if is_retryable and retry_count < max_retries:
wait_time = (retry_count + 1) * 2 # Exponential backoff: 2s, 4s, 6s
# Rate limit needs longer wait time
if is_rate_limit:
wait_time = 30 + (retry_count * 15) # 30s, 45s, 60s for rate limit
else:
wait_time = (retry_count + 1) * 2 # 2s, 4s, 6s for other errors
logger.warning(f"⚠️ LLM API error (attempt {retry_count + 1}/{max_retries}): {e}")
logger.info(f"Retrying in {wait_time}s...")
time.sleep(wait_time)
@@ -606,11 +619,15 @@ class AgentStreamExecutor:
for idx in sorted(tool_calls_buffer.keys()):
tc = tool_calls_buffer[idx]
try:
arguments = json.loads(tc["arguments"]) if tc["arguments"] else {}
# Safely get arguments, handle None case
args_str = tc.get("arguments") or ""
arguments = json.loads(args_str) if args_str else {}
except json.JSONDecodeError as e:
args_preview = tc['arguments'][:200] if len(tc['arguments']) > 200 else tc['arguments']
# Handle None or invalid arguments safely
args_str = tc.get('arguments') or ""
args_preview = args_str[:200] if len(args_str) > 200 else args_str
logger.error(f"Failed to parse tool arguments for {tc['name']}")
logger.error(f"Arguments length: {len(tc['arguments'])} chars")
logger.error(f"Arguments length: {len(args_str)} chars")
logger.error(f"Arguments preview: {args_preview}...")
logger.error(f"JSON decode error: {e}")
@@ -661,9 +678,9 @@ class AgentStreamExecutor:
for tc in tool_calls:
assistant_msg["content"].append({
"type": "tool_use",
"id": tc["id"],
"name": tc["name"],
"input": tc["arguments"]
"id": tc.get("id", ""),
"name": tc.get("name", ""),
"input": tc.get("arguments", {})
})
# Only append if content is not empty

12
agent/skills/loader.py
View File

@@ -137,6 +137,18 @@ class SkillLoader:
name = frontmatter.get('name', parent_dir_name)
description = frontmatter.get('description', '')
# Normalize name (handle both string and list)
if isinstance(name, list):
name = name[0] if name else parent_dir_name
elif not isinstance(name, str):
name = str(name) if name else parent_dir_name
# Normalize description (handle both string and list)
if isinstance(description, list):
description = ' '.join(str(d) for d in description if d)
elif not isinstance(description, str):
description = str(description) if description else ''
# Special handling for linkai-agent: dynamically load apps from config.json
if name == 'linkai-agent':
description = self._load_linkai_agent_description(skill_dir, description)

25
agent/skills/manager.py
View File

@@ -103,7 +103,21 @@ class SkillManager:
# Apply skill filter
if skill_filter is not None:
normalized = [name.strip() for name in skill_filter if name.strip()]
# Flatten and normalize skill names (handle both strings and nested lists)
normalized = []
for item in skill_filter:
if isinstance(item, str):
name = item.strip()
if name:
normalized.append(name)
elif isinstance(item, list):
# Handle nested lists
for subitem in item:
if isinstance(subitem, str):
name = subitem.strip()
if name:
normalized.append(name)
if normalized:
entries = [e for e in entries if e.skill.name in normalized]
@@ -123,8 +137,15 @@ class SkillManager:
:param skill_filter: Optional list of skill names to include
:return: Formatted skills prompt
"""
from common.log import logger
entries = self.filter_skills(skill_filter=skill_filter, include_disabled=False)
return format_skill_entries_for_prompt(entries)
logger.debug(f"[SkillManager] Filtered {len(entries)} skills for prompt (total: {len(self.skills)})")
if entries:
skill_names = [e.skill.name for e in entries]
logger.debug(f"[SkillManager] Skills to include: {skill_names}")
result = format_skill_entries_for_prompt(entries)
logger.debug(f"[SkillManager] Generated prompt length: {len(result)}")
return result
def build_skill_snapshot(
self,