95 lines
4.0 KiB
Python
95 lines
4.0 KiB
Python
import os, sys
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if sys.stdout is None: sys.stdout = open(os.devnull, "w")
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if sys.stderr is None: sys.stderr = open(os.devnull, "w")
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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import streamlit as st
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import time, json, re
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with open('assets/tools_schema.json', 'r', encoding='utf-8') as f:
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TOOLS_SCHEMA = json.load(f)
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st.set_page_config(page_title="Cowork", layout="wide")
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from sidercall import SiderLLMSession, LLMSession, ToolClient
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from agent_loop import agent_runner_loop, StepOutcome, BaseHandler
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@st.cache_resource
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def init():
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if not os.path.exists('temp'): os.makedirs('temp')
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from sidercall import sider_cookie, oai_apikey, oai_apibase
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llm_sessions = []
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if sider_cookie: llm_sessions += [SiderLLMSession(default_model=x) for x in \
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["gemini-3.0-flash", "claude-haiku-4.5", "gpt-5-mini"]]
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if oai_apikey: llm_sessions += [LLMSession(api_key=oai_apikey, api_base=oai_apibase)]
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if len(llm_sessions) == 0:
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st.error("⚠️ 未配置任何可用的 LLM 接口，请在 mykey.py 中添加 sider_cookie 或 oai_apikey+oai_apibase 等信息后重启。")
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st.stop()
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llmclient = ToolClient([x.ask for x in llm_sessions], auto_save_tokens=True)
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return llmclient
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llmclient = init()
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from ga import GenericAgentHandler, smart_format, get_global_memory
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def get_system_prompt():
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if not os.path.exists('memory'): os.makedirs('memory')
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if not os.path.exists('memory/global_mem.txt'):
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with open('memory/global_mem.txt', 'w', encoding='utf-8') as f: f.write('')
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if not os.path.exists('memory/global_mem_insight.txt'):
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content = "## Global Memory Index (Logic)\n\n[CONSTITUTION]\n1. 改我自身源码前必须先问用户\n\n[STORES]\n- global_mem: ../memory/global_mem.txt\n\n[ACCESS]\n- global_mem: 按 TOPIC 检索索引\n\n[TOPICS.GLOBAL_MEM]"
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if os.path.exists('assets/global_mem_insight_template.txt'):
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with open('assets/global_mem_insight_template.txt', 'r', encoding='utf-8') as f: content = f.read()
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with open('memory/global_mem_insight.txt', 'w', encoding='utf-8') as f: f.write(content)
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with open('assets/sys_prompt.txt', 'r', encoding='utf-8') as f: prompt = f.read()
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prompt += get_global_memory()
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return prompt
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if "last_goal" not in st.session_state:
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st.session_state.last_goal = ""
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def agent_backend_stream(raw_query):
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history = st.session_state.get("last_history", [])
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rquery = smart_format(raw_query.replace('\n', ' '), max_str_len=200)
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history.append(f"[USER]: {rquery}")
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sys_prompt = get_system_prompt()
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handler = GenericAgentHandler(None, history, './temp')
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llmclient.last_tools = ''
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llmclient.raw_api = llmclient.raw_apis[st.session_state.get("llm_no", 0)]
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ret = yield from agent_runner_loop(llmclient,
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sys_prompt, raw_query, handler,
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TOOLS_SCHEMA, max_turns=25)
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st.session_state.last_history = handler.history_info
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return ret
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st.title("🖥️ Cowork")
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if "messages" not in st.session_state:
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st.session_state.messages = []
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for msg in st.session_state.messages:
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with st.chat_message(msg["role"]):
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st.markdown(msg["content"])
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with st.sidebar:
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current_idx = st.session_state.get("llm_no", 0)
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st.caption(f"LLM Core: {current_idx}")
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if st.button("切换备用链路"):
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st.session_state.llm_no = (st.session_state.get("llm_no", 0) + 1) % len(llmclient.raw_apis)
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st.rerun()
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if prompt := st.chat_input("请输入指令"):
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st.session_state.messages.append({"role": "user", "content": prompt})
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with st.chat_message("user"):
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st.markdown(prompt)
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with st.chat_message("assistant"):
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message_placeholder = st.empty()
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full_response = ""
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for chunk in agent_backend_stream(prompt):
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full_response += chunk
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message_placeholder.markdown(full_response + "▌")
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message_placeholder.markdown(full_response)
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st.session_state.messages.append({"role": "assistant", "content": full_response}) |