447 lines
17 KiB
Python
447 lines
17 KiB
Python
import json
|
||
import os
|
||
import time
|
||
import datetime
|
||
import uuid
|
||
from pathlib import Path
|
||
from typing import Any, Dict, Iterable, List, Optional, Sequence, Union
|
||
|
||
import requests
|
||
from qwen_agent.agents import Assistant
|
||
from qwen_agent.llm.schema import ContentItem, Message
|
||
|
||
import agent_runtime # noqa: F401
|
||
from agent_runtime import readonly_tools # noqa: F401
|
||
|
||
DEFAULT_SYSTEM_PROMPT = (
|
||
'你是 Qwen3.5，本地部署的多模态中文助手。\n'
|
||
'默认中文回答。\n'
|
||
'当用户只是打招呼或闲聊时，自然回应即可，不要主动枚举全部工具。\n'
|
||
'你的目标是先使用可用工具获得可验证信息，再给出结论。\n'
|
||
'规则:\n'
|
||
'1. 对最新信息先用 web_search，再按需用 web_fetch 或 web_extractor 抓取正文。\n'
|
||
'2. 对人名、作品名、小众概念等不确定知识先 web_search，若结果歧义则改写关键词再检索一次。\n'
|
||
'3. 核心规则：你已具备 filesystem 的读写能力。你可以读取文件，如果用户有需求，你也可以调用 write_file 工具进行写入。\n'
|
||
'4. 图片问题先看整图，细节再用 image_zoom_in_tool，使用相对坐标。\n'
|
||
'5. 工具失败时必须明确说明原因，不得伪造结果。\n'
|
||
'6. 联网任务要控制上下文预算，优先少量高质量来源。\n'
|
||
'7. 严禁在未获授权的情况下使用 filesystem 工具查看助手自身的源代码或运行目录。\n'
|
||
'8. 联网任务要控制上下文预算，优先少量高质量来源，避免搬运大段无关正文。\n'
|
||
'9. 长期记忆（主动意识）：你拥有 manage_memory 工具。当你从对话中识别出以下内容时，必须【主动】调用 add 操作：\n - 用户的明确偏好（如：喜欢 MD 格式、不喜欢繁琐说明）。\n - 重要的个人事实（如：职业、项目代号、系统配置路径）。\n - 约定的工作习惯（如：每段代码都要加注释）。\n 执行后，在回复中自然地告知用户“我已记下此习惯/信息”。当用户问“你了解我什么”或要求修改时，配合 list 和 delete 操作。\n'
|
||
)
|
||
|
||
DEFAULT_FUNCTION_LIST = [
|
||
'web_search',
|
||
'web_fetch',
|
||
'web_extractor',
|
||
'image_search',
|
||
'image_zoom_in_tool',
|
||
'filesystem',
|
||
'manage_memory', # 👈 检查这里，确保引号和逗号都齐了
|
||
]
|
||
TIMINGS_EMIT_INTERVAL_SEC = 0.8
|
||
MAX_FALLBACK_PART_TEXT_CHARS = 512
|
||
|
||
# --- 注入逻辑开始 ---
|
||
def get_injected_memory() -> str:
|
||
"""从环境变量指定的路径动态加载记忆"""
|
||
path_str = os.getenv('MEMORY_FILE_PATH', './memory.json')
|
||
memory_path = Path(path_str).resolve()
|
||
|
||
if not memory_path.exists():
|
||
return ""
|
||
try:
|
||
with open(memory_path, 'r', encoding='utf-8') as f:
|
||
memories = json.load(f)
|
||
if not isinstance(memories, list) or not memories:
|
||
return ""
|
||
memory_str = "\n".join([f"- {m}" for m in memories])
|
||
return f"\n【长期记忆库（已自动加载）】:\n{memory_str}\n"
|
||
except Exception as e:
|
||
print(f"注入记忆失败: {e}")
|
||
return ""
|
||
|
||
def fetch_model_id(model_server: str, timeout_sec: int) -> str:
|
||
response = requests.get(f'{model_server}/models', timeout=timeout_sec)
|
||
response.raise_for_status()
|
||
return response.json()['data'][0]['id']
|
||
|
||
|
||
def _extract_image_uri(part: Dict[str, Any]) -> Optional[str]:
|
||
keys = ('image_url', 'image', 'url', 'input_image', 'image_uri')
|
||
for key in keys:
|
||
value = part.get(key)
|
||
if isinstance(value, str) and value.strip():
|
||
return value.strip()
|
||
if isinstance(value, dict):
|
||
nested = value.get('url') or value.get('image_url') or value.get('image')
|
||
if isinstance(nested, str) and nested.strip():
|
||
return nested.strip()
|
||
return None
|
||
|
||
|
||
def _build_compact_part_text(part: Dict[str, Any], part_type: Any) -> str:
|
||
part_keys = sorted(str(k) for k in part.keys())
|
||
payload = {'type': str(part_type or 'unknown'), 'keys': part_keys[:12]}
|
||
text = part.get('text')
|
||
if isinstance(text, str) and text.strip():
|
||
payload['text'] = text.strip()[:MAX_FALLBACK_PART_TEXT_CHARS]
|
||
return json.dumps(payload, ensure_ascii=False)
|
||
|
||
|
||
def extract_generate_cfg(payload: Dict[str, Any]) -> Dict[str, Any]:
|
||
cfg: Dict[str, Any] = {}
|
||
keys = ('temperature', 'top_p', 'top_k', 'presence_penalty', 'frequency_penalty')
|
||
for key in keys:
|
||
value = payload.get(key)
|
||
if value is not None:
|
||
cfg[key] = value
|
||
repeat_penalty = payload.get('repeat_penalty')
|
||
if repeat_penalty is not None:
|
||
cfg['repetition_penalty'] = repeat_penalty
|
||
extra_body = payload.get('extra_body')
|
||
if not isinstance(extra_body, dict):
|
||
extra_body = {}
|
||
chat_template_kwargs = extra_body.get('chat_template_kwargs')
|
||
if not isinstance(chat_template_kwargs, dict):
|
||
chat_template_kwargs = {}
|
||
# 默认开启思考，若上层显式传入 false 则保持用户值。
|
||
chat_template_kwargs.setdefault('enable_thinking', True)
|
||
extra_body['chat_template_kwargs'] = chat_template_kwargs
|
||
|
||
requested_reasoning_format = payload.get('reasoning_format')
|
||
if isinstance(requested_reasoning_format, str) and requested_reasoning_format.strip():
|
||
extra_body.setdefault('reasoning_format', requested_reasoning_format.strip())
|
||
else:
|
||
extra_body.setdefault('reasoning_format', 'deepseek')
|
||
extra_body.setdefault('reasoning_budget', -1)
|
||
cfg['extra_body'] = extra_body
|
||
max_tokens = payload.get('max_tokens')
|
||
if isinstance(max_tokens, int) and max_tokens > 0:
|
||
cfg['max_tokens'] = max_tokens
|
||
if not cfg:
|
||
cfg = {'temperature': 0.7, 'top_p': 0.9, 'max_tokens': 512}
|
||
return cfg
|
||
|
||
|
||
def build_agent(
|
||
model_server: str,
|
||
timeout_sec: int,
|
||
generate_cfg: Dict[str, Any],
|
||
model_id: Optional[str] = None,
|
||
system_prompt: str = DEFAULT_SYSTEM_PROMPT,
|
||
) -> Assistant:
|
||
if not model_id:
|
||
model_id = fetch_model_id(model_server, timeout_sec)
|
||
llm_cfg = {
|
||
'model': model_id,
|
||
'model_server': model_server,
|
||
'api_key': os.getenv('OPENAI_API_KEY', 'EMPTY'),
|
||
'model_type': 'qwenvl_oai',
|
||
'generate_cfg': generate_cfg,
|
||
}
|
||
|
||
# === 核心改造 1：动态组装功能列表 ===
|
||
actual_function_list = list(DEFAULT_FUNCTION_LIST)
|
||
if os.getenv('ENABLE_FILE_WRITE', 'False').lower() == 'true':
|
||
import agent_runtime.write_tools # 触发 register_tool 注册
|
||
actual_function_list.append('write_file')
|
||
|
||
# === 核心改造 2：动态注入记忆与实时时间 ===
|
||
# 1. 先从物理文件加载记忆
|
||
persistent_memory = get_injected_memory()
|
||
|
||
# 2. 获取实时时间
|
||
now = datetime.datetime.now()
|
||
current_time = now.strftime("%Y年%m月%d日 %H:%M:%S")
|
||
weekdays = ["一", "二", "三", "四", "五", "六", "日"]
|
||
dynamic_context = f"【系统实时状态】\n当前时间：{current_time}，星期{weekdays[now.weekday()]}。\n"
|
||
|
||
# 3. 按照：时间 -> 长期记忆 -> 原始指令 的顺序拼接
|
||
actual_system_prompt = dynamic_context + persistent_memory + system_prompt
|
||
|
||
return Assistant(
|
||
name='Qwen3.5-9B-ToolHub-8080',
|
||
description='8080 网页工具代理',
|
||
llm=llm_cfg,
|
||
function_list=actual_function_list,
|
||
system_message=actual_system_prompt,
|
||
)
|
||
|
||
def to_content_items(content: Any) -> Union[str, List[ContentItem]]:
|
||
if isinstance(content, str):
|
||
return content
|
||
if not isinstance(content, list):
|
||
return str(content)
|
||
|
||
items: List[ContentItem] = []
|
||
for part in content:
|
||
if not isinstance(part, dict):
|
||
items.append(ContentItem(text=str(part)))
|
||
continue
|
||
part_type = part.get('type')
|
||
if part_type in (None, 'text', 'input_text'):
|
||
text = part.get('text', '')
|
||
if text:
|
||
items.append(ContentItem(text=str(text)))
|
||
continue
|
||
image_uri = _extract_image_uri(part)
|
||
if image_uri:
|
||
items.append(ContentItem(image=image_uri))
|
||
continue
|
||
items.append(ContentItem(text=_build_compact_part_text(part, part_type)))
|
||
return items if items else ''
|
||
|
||
|
||
def to_qwen_messages(openai_messages: Sequence[Dict[str, Any]]) -> List[Message]:
|
||
qwen_messages: List[Message] = []
|
||
for item in openai_messages:
|
||
role = str(item.get('role', '')).strip()
|
||
if role not in {'system', 'user', 'assistant'}:
|
||
continue
|
||
qwen_messages.append(Message(role=role, content=to_content_items(item.get('content', ''))))
|
||
if not qwen_messages:
|
||
raise ValueError('messages 为空或不包含可用角色')
|
||
return qwen_messages
|
||
|
||
|
||
def content_to_text(content: Any) -> str:
|
||
if isinstance(content, str):
|
||
return content
|
||
if not isinstance(content, list):
|
||
return str(content)
|
||
|
||
texts: List[str] = []
|
||
for item in content:
|
||
if isinstance(item, str):
|
||
texts.append(item)
|
||
continue
|
||
if isinstance(item, dict) and item.get('text'):
|
||
texts.append(str(item['text']))
|
||
continue
|
||
text = getattr(item, 'text', None)
|
||
if text:
|
||
texts.append(str(text))
|
||
return '\n'.join(texts).strip()
|
||
|
||
|
||
def extract_answer_and_reasoning(messages: Sequence[Message]) -> Dict[str, str]:
|
||
answer = ''
|
||
reasoning_parts: List[str] = []
|
||
for message in messages:
|
||
if getattr(message, 'role', '') != 'assistant':
|
||
continue
|
||
content_text = content_to_text(message.get('content', ''))
|
||
if content_text:
|
||
answer = content_text
|
||
reasoning_text = content_to_text(message.get('reasoning_content', ''))
|
||
if reasoning_text:
|
||
reasoning_parts.append(reasoning_text)
|
||
return {'answer': answer, 'reasoning': '\n'.join(reasoning_parts).strip()}
|
||
|
||
|
||
def run_chat_completion(payload: Dict[str, Any], model_server: str, timeout_sec: int) -> Dict[str, str]:
|
||
openai_messages = payload.get('messages')
|
||
if not isinstance(openai_messages, list):
|
||
raise ValueError('messages 必须是数组')
|
||
|
||
model_id = fetch_model_id(model_server, timeout_sec)
|
||
agent = build_agent(model_server, timeout_sec, extract_generate_cfg(payload), model_id=model_id)
|
||
qwen_messages = to_qwen_messages(openai_messages)
|
||
final_batch = None
|
||
for batch in agent.run(qwen_messages):
|
||
final_batch = batch
|
||
if not final_batch:
|
||
raise RuntimeError('未收到模型输出')
|
||
|
||
texts = extract_answer_and_reasoning(final_batch)
|
||
answer = texts['answer']
|
||
reasoning = texts['reasoning']
|
||
return {'model': model_id, 'answer': answer, 'reasoning': reasoning}
|
||
|
||
|
||
def build_sse_chunk(
|
||
chat_id: str,
|
||
created: int,
|
||
model: str,
|
||
delta: Dict[str, Any],
|
||
finish_reason: Optional[str] = None,
|
||
timings: Optional[Dict[str, Any]] = None,
|
||
) -> bytes:
|
||
chunk = {
|
||
'id': chat_id,
|
||
'object': 'chat.completion.chunk',
|
||
'created': created,
|
||
'model': model,
|
||
'choices': [{'index': 0, 'delta': delta, 'finish_reason': finish_reason}],
|
||
}
|
||
if timings:
|
||
chunk['timings'] = timings
|
||
return f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n".encode('utf-8')
|
||
|
||
|
||
def text_delta(previous: str, current: str) -> str:
|
||
if not current:
|
||
return ''
|
||
if current.startswith(previous):
|
||
return current[len(previous):]
|
||
return current
|
||
|
||
|
||
def model_base_url(model_server: str) -> str:
|
||
if model_server.endswith('/v1'):
|
||
return model_server[:-3]
|
||
return model_server.rstrip('/')
|
||
|
||
|
||
def count_text_tokens(model_server: str, timeout_sec: int, text: str) -> int:
|
||
if not text:
|
||
return 0
|
||
url = f'{model_base_url(model_server)}/tokenize'
|
||
response = requests.post(url, json={'content': text}, timeout=timeout_sec)
|
||
response.raise_for_status()
|
||
data = response.json()
|
||
tokens = data.get('tokens')
|
||
if not isinstance(tokens, list):
|
||
raise ValueError('tokenize 返回格式异常')
|
||
return len(tokens)
|
||
|
||
|
||
def build_live_timings(token_count: int, elapsed_sec: float) -> Dict[str, Any]:
|
||
safe_elapsed = elapsed_sec if elapsed_sec > 0 else 1e-6
|
||
return {
|
||
'prompt_n': 0,
|
||
'prompt_ms': 0,
|
||
'predicted_n': token_count,
|
||
'predicted_ms': safe_elapsed * 1000.0,
|
||
'predicted_per_second': token_count / safe_elapsed,
|
||
'cache_n': 0,
|
||
}
|
||
|
||
|
||
def merge_generated_text(reasoning: str, answer: str) -> str:
|
||
if reasoning and answer:
|
||
return f'{reasoning}\n{answer}'
|
||
return reasoning or answer
|
||
|
||
|
||
def stream_chat_completion(payload: Dict[str, Any], model_server: str, timeout_sec: int) -> Iterable[bytes]:
|
||
openai_messages = payload.get('messages')
|
||
if not isinstance(openai_messages, list):
|
||
raise ValueError('messages 必须是数组')
|
||
|
||
model_id = fetch_model_id(model_server, timeout_sec)
|
||
agent = build_agent(model_server, timeout_sec, extract_generate_cfg(payload), model_id=model_id)
|
||
qwen_messages = to_qwen_messages(openai_messages)
|
||
|
||
now = int(time.time())
|
||
chat_id = f'chatcmpl-{uuid.uuid4().hex}'
|
||
yield build_sse_chunk(chat_id, now, model_id, {'role': 'assistant'})
|
||
|
||
previous_answer = ''
|
||
previous_reasoning = ''
|
||
started_at = time.perf_counter()
|
||
last_timing_at = started_at
|
||
last_reported_tokens = -1
|
||
last_counted_text = ''
|
||
for batch in agent.run(qwen_messages):
|
||
texts = extract_answer_and_reasoning(batch)
|
||
answer = texts['answer']
|
||
reasoning = texts['reasoning']
|
||
|
||
reasoning_inc = text_delta(previous_reasoning, reasoning)
|
||
if reasoning_inc:
|
||
yield build_sse_chunk(chat_id, now, model_id, {'reasoning_content': reasoning_inc})
|
||
|
||
answer_inc = text_delta(previous_answer, answer)
|
||
if answer_inc:
|
||
yield build_sse_chunk(chat_id, now, model_id, {'content': answer_inc})
|
||
|
||
generated_text = merge_generated_text(reasoning, answer)
|
||
current_time = time.perf_counter()
|
||
should_emit_timing = (
|
||
generated_text
|
||
and generated_text != last_counted_text
|
||
and (current_time - last_timing_at) >= TIMINGS_EMIT_INTERVAL_SEC
|
||
)
|
||
if should_emit_timing:
|
||
token_count = count_text_tokens(model_server, timeout_sec, generated_text)
|
||
if token_count != last_reported_tokens:
|
||
timings = build_live_timings(token_count, current_time - started_at)
|
||
yield build_sse_chunk(chat_id, now, model_id, {}, timings=timings)
|
||
last_reported_tokens = token_count
|
||
last_counted_text = generated_text
|
||
last_timing_at = current_time
|
||
|
||
previous_reasoning = reasoning
|
||
previous_answer = answer
|
||
|
||
final_generated_text = merge_generated_text(previous_reasoning, previous_answer)
|
||
if final_generated_text and final_generated_text != last_counted_text:
|
||
final_time = time.perf_counter()
|
||
token_count = count_text_tokens(model_server, timeout_sec, final_generated_text)
|
||
if token_count != last_reported_tokens:
|
||
timings = build_live_timings(token_count, final_time - started_at)
|
||
yield build_sse_chunk(chat_id, now, model_id, {}, timings=timings)
|
||
|
||
yield build_sse_chunk(chat_id, now, model_id, {}, 'stop')
|
||
yield b'data: [DONE]\n\n'
|
||
|
||
|
||
def build_non_stream_response(answer: str, model: str, reasoning: str = '') -> Dict[str, Any]:
|
||
now = int(time.time())
|
||
message = {'role': 'assistant', 'content': answer}
|
||
if reasoning:
|
||
message['reasoning_content'] = reasoning
|
||
return {
|
||
'id': f'chatcmpl-{uuid.uuid4().hex}',
|
||
'object': 'chat.completion',
|
||
'created': now,
|
||
'model': model,
|
||
'choices': [{
|
||
'index': 0,
|
||
'message': message,
|
||
'finish_reason': 'stop',
|
||
}],
|
||
'usage': {'prompt_tokens': 0, 'completion_tokens': 0, 'total_tokens': 0},
|
||
}
|
||
|
||
|
||
def sse_lines(answer: str, model: str, reasoning: str = '') -> Iterable[bytes]:
|
||
now = int(time.time())
|
||
chat_id = f'chatcmpl-{uuid.uuid4().hex}'
|
||
chunks = [
|
||
{
|
||
'id': chat_id,
|
||
'object': 'chat.completion.chunk',
|
||
'created': now,
|
||
'model': model,
|
||
'choices': [{'index': 0, 'delta': {'role': 'assistant'}, 'finish_reason': None}],
|
||
},
|
||
]
|
||
if reasoning:
|
||
chunks.append({
|
||
'id': chat_id,
|
||
'object': 'chat.completion.chunk',
|
||
'created': now,
|
||
'model': model,
|
||
'choices': [{'index': 0, 'delta': {'reasoning_content': reasoning}, 'finish_reason': None}],
|
||
})
|
||
chunks.append({
|
||
'id': chat_id,
|
||
'object': 'chat.completion.chunk',
|
||
'created': now,
|
||
'model': model,
|
||
'choices': [{'index': 0, 'delta': {'content': answer}, 'finish_reason': None}],
|
||
})
|
||
chunks.append({
|
||
'id': chat_id,
|
||
'object': 'chat.completion.chunk',
|
||
'created': now,
|
||
'model': model,
|
||
'choices': [{'index': 0, 'delta': {}, 'finish_reason': 'stop'}],
|
||
})
|
||
for chunk in chunks:
|
||
yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n".encode('utf-8')
|
||
yield b'data: [DONE]\n\n'
|