mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-23 23:40:03 +08:00
d316efe8d3
## 🛠 新增功能 - 支持百川在线模型 (@hzg0601 @liunux4odoo in #1623) - 支持 Azure OpenAI 与 claude 等 Langchain 自带模型 (@zRzRzRzRzRzRzR in #1808) - Agent 功能大量更新，支持更多的工具、更换提示词、检索知识库 (@zRzRzRzRzRzRzR in #1626 #1666 #1785) - 加长 32k 模型的历史记录 (@zRzRzRzRzRzRzR in #1629 #1630) - *_chat 接口支持 max_tokens 参数 (@liunux4odoo in #1744) - 实现 API 和 WebUI 的前后端分离 (@liunux4odoo in #1772) - 支持 zlilliz 向量库 (@zRzRzRzRzRzRzR in #1785) - 支持 metaphor 搜索引擎 (@liunux4odoo in #1792) - 支持 p-tuning 模型 (@hzg0601 in #1810) - 更新完善文档和 Wiki (@imClumsyPanda @zRzRzRzRzRzRzR @glide-the in #1680 #1811) ## 🐞 问题修复 - 修复 bge-* 模型匹配超过 1 的问题 (@zRzRzRzRzRzRzR in #1652) - 修复系统代理为空的问题 (@glide-the in #1654) - 修复重建知识库时 `d == self.d assert error` (@liunux4odoo in #1766) - 修复对话历史消息错误 (@liunux4odoo in #1801) - 修复 OpenAI 无法调用的 bug (@zRzRzRzRzRzRzR in #1808) - 修复 windows下 BIND_HOST=0.0.0.0 时对话出错的问题 (@hzg0601 in #1810)
96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
from __future__ import annotations
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from langchain.agents import Tool, AgentOutputParser
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from langchain.prompts import StringPromptTemplate
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from typing import List
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from langchain.schema import AgentAction, AgentFinish
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from server.agent import model_container
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class CustomPromptTemplate(StringPromptTemplate):
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# The template to use
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template: str
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# The list of tools available
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tools: List[Tool]
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def format(self, **kwargs) -> str:
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# Get the intermediate steps (AgentAction, Observation tuples)
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# Format them in a particular way
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intermediate_steps = kwargs.pop("intermediate_steps")
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thoughts = ""
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for action, observation in intermediate_steps:
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thoughts += action.log
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thoughts += f"\nObservation: {observation}\nThought: "
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# Set the agent_scratchpad variable to that value
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kwargs["agent_scratchpad"] = thoughts
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# Create a tools variable from the list of tools provided
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kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
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# Create a list of tool names for the tools provided
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kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
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# Return the formatted templatepr
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# print( self.template.format(**kwargs), end="\n\n")
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return self.template.format(**kwargs)
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class CustomOutputParser(AgentOutputParser):
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begin: bool = False
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def __init__(self):
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super().__init__()
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self.begin = True
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def parse(self, llm_output: str) -> AgentFinish | tuple[dict[str, str], str] | AgentAction:
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# Check if agent should finish
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support_agent = ["Azure-OpenAI", "OpenAI", "Anthropic", "Qwen", "qwen-api", "baichuan-api"] # 目前支持agent的模型
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if not any(agent in model_container.MODEL for agent in support_agent) and self.begin:
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self.begin = False
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stop_words = ["Observation:"]
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min_index = len(llm_output)
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for stop_word in stop_words:
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index = llm_output.find(stop_word)
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if index != -1 and index < min_index:
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min_index = index
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llm_output = llm_output[:min_index]
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if "Final Answer:" in llm_output:
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self.begin = True
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return AgentFinish(
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return_values={"output": llm_output.split("Final Answer:", 1)[-1].strip()},
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log=llm_output,
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)
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# Parse out the action and action input
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parts = llm_output.split("Action:")
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if len(parts) < 2:
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return AgentFinish(
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return_values={"output": f"调用agent失败: `{llm_output}`"},
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log=llm_output,
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)
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action = parts[1].split("Action Input:")[0].strip()
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action_input = parts[1].split("Action Input:")[1].strip()
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# 原来的正则化检查方式
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# regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
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# print("llm_output",llm_output)
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# match = re.search(regex, llm_output, re.DOTALL)
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# print("match",match)
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# if not match:
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# return AgentFinish(
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# return_values={"output": f"调用agent失败: `{llm_output}`"},
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# log=llm_output,
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# )
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# action = match.group(1).strip()
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# action_input = match.group(2)
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# Return the action and action input
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try:
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ans = AgentAction(
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tool=action,
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tool_input=action_input.strip(" ").strip('"'),
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log=llm_output
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)
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return ans
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except:
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return AgentFinish(
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return_values={"output": f"调用agent失败: `{llm_output}`"},
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log=llm_output,
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)
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