113 lines
4.2 KiB
Python
113 lines
4.2 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('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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mainllm = SiderLLMSession(multiturns=6)
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llmclient = ToolClient(mainllm.ask, 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
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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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with open('memory/global_mem_insight.txt', 'w', encoding='utf-8') as f: f.write('')
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with open('sys_prompt.txt', 'r', encoding='utf-8') as f:
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prompt = f.read()
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try:
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with open('memory/global_mem_insight.txt', 'r', encoding='utf-8') as f:
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insight = f.read()
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prompt += f"\n\n[Global Memory Insight]\n"
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prompt += 'IMPORTANT PATHS: ../memory/global_mem.txt (Facts), ../memory/global_mem_insight.txt (Logic), ../ (Your Code Root).\n'
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prompt += 'MEM_RULE: Insight is the index of Facts. Sync Insight whenever Facts change. For details, read Facts.\n'
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prompt += "EXT: ../memory/ may contain other task-specific memories.\n"
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prompt += insight + "\n"
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except FileNotFoundError: pass
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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 refine_user_goal(raw_query, last_goal):
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"""通过 LLM 提炼用户真实意图"""
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if not last_goal:
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return raw_query
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decide_prompt = f"""
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用户之前的目标是: "{last_goal}"
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用户现在输入了: "{raw_query}"
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请判断：
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1. 如果用户提供补充信息、或者是接续之前的任务，请输出合并后的【最终目标】。
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2. 如果用户只是指出之前做法有错而非变更目标，那么请输出原目标不做修改。
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3. 如果用户开启了一个完全不相关的新话题，请直接输出用户现在的输入内容。
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请直接输出目标描述，不要包含任何多余的文字、解释或标点。
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"""
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try:
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refined = llmclient.llm_func(decide_prompt).strip()
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return refined if refined else raw_query
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except:
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return raw_query
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def agent_backend_stream(raw_query):
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#final_goal = refine_user_goal(raw_query, st.session_state.last_goal)
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#if final_goal != raw_query: yield f"[Goal Refined] {final_goal}\n"
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history = st.session_state.get("last_history", [])
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rquery = smart_format(raw_query.replace('\n', ' '))
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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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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_goal = final_goal
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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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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}) |