mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-19 13:23:16 +08:00
9c525b7fa5
新功能： - 优化 PDF 文件的 OCR，过滤无意义的小图片 by @liunux4odoo #2525 - 支持 Gemini 在线模型 by @yhfgyyf #2630 - 支持 GLM4 在线模型 by @zRzRzRzRzRzRzR - elasticsearch更新https连接 by @xldistance #2390 - 增强对PPT、DOC知识库文件的OCR识别 by @596192804 #2013 - 更新 Agent 对话功能 by @zRzRzRzRzRzRzR - 每次创建对象时从连接池获取连接，避免每次执行方法时都新建连接 by @Lijia0 #2480 - 实现 ChatOpenAI 判断token有没有超过模型的context上下文长度 by @glide-the - 更新运行数据库报错和项目里程碑 by @zRzRzRzRzRzRzR #2659 - 更新配置文件/文档/依赖 by @imClumsyPanda @zRzRzRzRzRzRzR - 添加日文版 readme by @eltociear #2787 修复： - langchain 更新后，PGVector 向量库连接错误 by @HALIndex #2591 - Minimax's model worker 错误 by @xyhshen - ES库无法向量检索.添加mappings创建向量索引 by MSZheng20 #2688
179 lines
8.5 KiB
Python
179 lines
8.5 KiB
Python
import json
|
||
import asyncio
|
||
|
||
from fastapi import Body
|
||
from sse_starlette.sse import EventSourceResponse
|
||
from configs import LLM_MODELS, TEMPERATURE, HISTORY_LEN, Agent_MODEL
|
||
|
||
from langchain.chains import LLMChain
|
||
from langchain.memory import ConversationBufferWindowMemory
|
||
from langchain.agents import LLMSingleActionAgent, AgentExecutor
|
||
from typing import AsyncIterable, Optional, List
|
||
|
||
from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template
|
||
from server.knowledge_base.kb_service.base import get_kb_details
|
||
from server.agent.custom_agent.ChatGLM3Agent import initialize_glm3_agent
|
||
from server.agent.tools_select import tools, tool_names
|
||
from server.agent.callbacks import CustomAsyncIteratorCallbackHandler, Status
|
||
from server.chat.utils import History
|
||
from server.agent import model_container
|
||
from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate
|
||
|
||
|
||
async def agent_chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
|
||
history: List[History] = Body([],
|
||
description="历史对话",
|
||
examples=[[
|
||
{"role": "user", "content": "请使用知识库工具查询今天北京天气"},
|
||
{"role": "assistant",
|
||
"content": "使用天气查询工具查询到今天北京多云，10-14摄氏度，东北风2级，易感冒"}]]
|
||
),
|
||
stream: bool = Body(False, description="流式输出"),
|
||
model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
|
||
temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
|
||
max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"),
|
||
prompt_name: str = Body("default",
|
||
description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
|
||
):
|
||
history = [History.from_data(h) for h in history]
|
||
|
||
async def agent_chat_iterator(
|
||
query: str,
|
||
history: Optional[List[History]],
|
||
model_name: str = LLM_MODELS[0],
|
||
prompt_name: str = prompt_name,
|
||
) -> AsyncIterable[str]:
|
||
nonlocal max_tokens
|
||
callback = CustomAsyncIteratorCallbackHandler()
|
||
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=[callback],
|
||
)
|
||
|
||
kb_list = {x["kb_name"]: x for x in get_kb_details()}
|
||
model_container.DATABASE = {name: details['kb_info'] for name, details in kb_list.items()}
|
||
|
||
if Agent_MODEL:
|
||
model_agent = get_ChatOpenAI(
|
||
model_name=Agent_MODEL,
|
||
temperature=temperature,
|
||
max_tokens=max_tokens,
|
||
callbacks=[callback],
|
||
)
|
||
model_container.MODEL = model_agent
|
||
else:
|
||
model_container.MODEL = model
|
||
|
||
prompt_template = get_prompt_template("agent_chat", prompt_name)
|
||
prompt_template_agent = CustomPromptTemplate(
|
||
template=prompt_template,
|
||
tools=tools,
|
||
input_variables=["input", "intermediate_steps", "history"]
|
||
)
|
||
output_parser = CustomOutputParser()
|
||
llm_chain = LLMChain(llm=model, prompt=prompt_template_agent)
|
||
memory = ConversationBufferWindowMemory(k=HISTORY_LEN * 2)
|
||
for message in history:
|
||
if message.role == 'user':
|
||
memory.chat_memory.add_user_message(message.content)
|
||
else:
|
||
memory.chat_memory.add_ai_message(message.content)
|
||
if "chatglm3" in model_container.MODEL.model_name or "zhipu-api" in model_container.MODEL.model_name:
|
||
agent_executor = initialize_glm3_agent(
|
||
llm=model,
|
||
tools=tools,
|
||
callback_manager=None,
|
||
prompt=prompt_template,
|
||
input_variables=["input", "intermediate_steps", "history"],
|
||
memory=memory,
|
||
verbose=True,
|
||
)
|
||
else:
|
||
agent = LLMSingleActionAgent(
|
||
llm_chain=llm_chain,
|
||
output_parser=output_parser,
|
||
stop=["\nObservation:", "Observation"],
|
||
allowed_tools=tool_names,
|
||
)
|
||
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent,
|
||
tools=tools,
|
||
verbose=True,
|
||
memory=memory,
|
||
)
|
||
while True:
|
||
try:
|
||
task = asyncio.create_task(wrap_done(
|
||
agent_executor.acall(query, callbacks=[callback], include_run_info=True),
|
||
callback.done))
|
||
break
|
||
except:
|
||
pass
|
||
|
||
if stream:
|
||
async for chunk in callback.aiter():
|
||
tools_use = []
|
||
# Use server-sent-events to stream the response
|
||
data = json.loads(chunk)
|
||
if data["status"] == Status.start or data["status"] == Status.complete:
|
||
continue
|
||
elif data["status"] == Status.error:
|
||
tools_use.append("\n```\n")
|
||
tools_use.append("工具名称: " + data["tool_name"])
|
||
tools_use.append("工具状态: " + "调用失败")
|
||
tools_use.append("错误信息: " + data["error"])
|
||
tools_use.append("重新开始尝试")
|
||
tools_use.append("\n```\n")
|
||
yield json.dumps({"tools": tools_use}, ensure_ascii=False)
|
||
elif data["status"] == Status.tool_finish:
|
||
tools_use.append("\n```\n")
|
||
tools_use.append("工具名称: " + data["tool_name"])
|
||
tools_use.append("工具状态: " + "调用成功")
|
||
tools_use.append("工具输入: " + data["input_str"])
|
||
tools_use.append("工具输出: " + data["output_str"])
|
||
tools_use.append("\n```\n")
|
||
yield json.dumps({"tools": tools_use}, ensure_ascii=False)
|
||
elif data["status"] == Status.agent_finish:
|
||
yield json.dumps({"final_answer": data["final_answer"]}, ensure_ascii=False)
|
||
else:
|
||
yield json.dumps({"answer": data["llm_token"]}, ensure_ascii=False)
|
||
|
||
|
||
else:
|
||
answer = ""
|
||
final_answer = ""
|
||
async for chunk in callback.aiter():
|
||
data = json.loads(chunk)
|
||
if data["status"] == Status.start or data["status"] == Status.complete:
|
||
continue
|
||
if data["status"] == Status.error:
|
||
answer += "\n```\n"
|
||
answer += "工具名称: " + data["tool_name"] + "\n"
|
||
answer += "工具状态: " + "调用失败" + "\n"
|
||
answer += "错误信息: " + data["error"] + "\n"
|
||
answer += "\n```\n"
|
||
if data["status"] == Status.tool_finish:
|
||
answer += "\n```\n"
|
||
answer += "工具名称: " + data["tool_name"] + "\n"
|
||
answer += "工具状态: " + "调用成功" + "\n"
|
||
answer += "工具输入: " + data["input_str"] + "\n"
|
||
answer += "工具输出: " + data["output_str"] + "\n"
|
||
answer += "\n```\n"
|
||
if data["status"] == Status.agent_finish:
|
||
final_answer = data["final_answer"]
|
||
else:
|
||
answer += data["llm_token"]
|
||
|
||
yield json.dumps({"answer": answer, "final_answer": final_answer}, ensure_ascii=False)
|
||
await task
|
||
|
||
return EventSourceResponse(agent_chat_iterator(query=query,
|
||
history=history,
|
||
model_name=model_name,
|
||
prompt_name=prompt_name),
|
||
)
|