[全量] 初始化项目代码、配置、文档及Agent协同harness

2026-04-02 11:36:05 +08:00
parent 0553309cdf
commit 87e571d9ec
1133 changed files with 221948 additions and 0 deletions
--- a/langchain-chat/server/agent/agent_chat.py
+++ b/langchain-chat/server/agent/agent_chat.py
@@ -0,0 +1,101 @@
+import uuid
+from fastapi import Body
+from langchain.memory import (
+    CombinedMemory,
+    ConversationBufferMemory,
+    ConversationSummaryMemory,
+    ConversationBufferWindowMemory
+)
+from sse_starlette.sse import EventSourceResponse
+from configs import LLM_MODELS, TEMPERATURE, HISTORY_LEN
+from server.utils import wrap_done, get_ChatOpenAI
+from langchain.chains import LLMChain, ConversationChain
+from langchain.callbacks import AsyncIteratorCallbackHandler
+from typing import AsyncIterable
+import asyncio
+import json
+from langchain.prompts.chat import ChatPromptTemplate
+from typing import List, Optional, Union
+from server.chat.utils import History
+from langchain.prompts import PromptTemplate
+from server.utils import get_prompt_template, get_format_template
+from server.memory.conversation_db_buffer_memory import ConversationBufferDBMemory
+from server.db.repository import add_message_to_db
+from server.callback_handler.conversation_callback_handler import ConversationCallbackHandler
+from datetime import datetime
+from langchain_core.messages import SystemMessage
+import time as t
+from server.utils import replace_variables
+from configs.basic_config import *
+from configs.outline_config import outlines
+
+async def agent_chat_new(
+    user_prompt_name: Optional[str] = Body(None, description="用户输入"),
+    query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
+    conversation_id: str = Body("", description="对话框ID"),
+    history: Union[int, List[History]] = Body([], description="历史对话"),
+    model_name: str = Body("default_model", description="LLM 模型名称。"),
+    temperature: float = Body(0.7, description="LLM 采样温度", ge=0.0, le=2.0),
+    max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量"),
+    prompt_template: str = Body("default", description="使用的prompt模板内容"),
+    stream: bool = Body(False, description="流式输出")
+) -> AsyncIterable[str]:
+    callback = AsyncIteratorCallbackHandler()
+    callbacks = [callback]
+    time = datetime.now().strftime("%Y年%m月%d日")
+    message_id = str(uuid.uuid1())+"q"
+
+    if isinstance(max_tokens, int) and max_tokens <= 0:
+        max_tokens = None
+
+    model = get_ChatOpenAI(
+        model_name=model_name,
+        temperature=temperature,
+        max_tokens=max_tokens,
+        callbacks=callbacks,
+    )
+
+
+
+    history = [History.from_data(h) for h in history]
+    chat_prompt =  PromptTemplate.from_template(prompt_template)
+    # 把history转成memory
+    buff_memory = ConversationBufferMemory(human_prefix='user', ai_prefix='assistant', memory_key="history", input_key="input")
+    if len(history)>0:
+        for message in history:
+            # 检查消息的角色
+            if message.role == 'user':
+                # 添加用户消息
+                buff_memory.chat_memory.add_user_message(message.content)
+            elif message.role == 'assistant':
+                # 添加AI消息
+                buff_memory.chat_memory.add_ai_message(message.content)
+    else:
+        buff_memory.chat_memory.add_user_message("无")
+        buff_memory.chat_memory.add_ai_message("无")
+    background_memory = ConversationBufferMemory(human_prefix='user', ai_prefix='assistant', memory_key="time", input_key="input")
+    message = SystemMessage(content = f'当前的时间是：{time}')
+    background_memory.chat_memory.add_message(message)
+    memory = CombinedMemory(memories=[background_memory, buff_memory])
+    chain = ConversationChain(llm=model, verbose=True, memory=memory, prompt=chat_prompt)
+    task = asyncio.create_task(wrap_done(
+            chain.acall({"input": query, "time": time}),
+            callback.done),
+        )
+
+    answer = ""
+    async for token in callback.aiter():
+        if stream:
+            yield json.dumps({"text": token}, ensure_ascii=False)
+        else:
+            answer += token
+            logger.info(f'solve_problem: {str(answer)}')
+
+    await task
+    if stream:
+        return 
+    else:
+        yield json.dumps(
+            {"text": answer, "message_id": message_id},
+            ensure_ascii=False
+            )