mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-30 02:35:29 +08:00
238 lines
8.5 KiB
Python
238 lines
8.5 KiB
Python
"""
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This file is a modified version for ChatGLM3-6B the original glm3_agent.py file from the langchain repo.
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"""
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from __future__ import annotations
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import yaml
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from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser
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from langchain.memory import ConversationBufferWindowMemory
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from typing import Any, List, Sequence, Tuple, Optional, Union
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import os
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from langchain.agents.agent import Agent
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from langchain.chains.llm import LLMChain
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from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate
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import json
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import logging
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from typing import Any, List, Sequence, Tuple, Optional, Union
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from pydantic.schema import model_schema
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from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser
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from langchain.memory import ConversationBufferWindowMemory
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from langchain.agents.agent import Agent
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from langchain.chains.llm import LLMChain
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from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate
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from langchain.agents.agent import AgentOutputParser
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from langchain.output_parsers import OutputFixingParser
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from langchain.pydantic_v1 import Field
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from langchain.schema import AgentAction, AgentFinish, OutputParserException, BasePromptTemplate
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from langchain.agents.agent import AgentExecutor
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from langchain.callbacks.base import BaseCallbackManager
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from langchain.schema.language_model import BaseLanguageModel
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from langchain.tools.base import BaseTool
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from pydantic.schema import model_schema
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HUMAN_MESSAGE_TEMPLATE = "{input}\n\n{agent_scratchpad}"
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logger = logging.getLogger(__name__)
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class StructuredChatOutputParserWithRetries(AgentOutputParser):
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"""Output parser with retries for the structured chat agent."""
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base_parser: AgentOutputParser = Field(default_factory=StructuredChatOutputParser)
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"""The base parser to use."""
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output_fixing_parser: Optional[OutputFixingParser] = None
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"""The output fixing parser to use."""
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def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
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special_tokens = ["Action:", "<|observation|>"]
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first_index = min([text.find(token) if token in text else len(text) for token in special_tokens])
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text = text[:first_index]
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if "tool_call" in text:
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action_end = text.find("```")
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action = text[:action_end].strip()
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params_str_start = text.find("(") + 1
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params_str_end = text.rfind(")")
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params_str = text[params_str_start:params_str_end]
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params_pairs = [param.split("=") for param in params_str.split(",") if "=" in param]
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params = {pair[0].strip(): pair[1].strip().strip("'\"") for pair in params_pairs}
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action_json = {
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"action": action,
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"action_input": params
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}
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else:
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action_json = {
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"action": "Final Answer",
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"action_input": text
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}
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action_str = f"""
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Action:
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```
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{json.dumps(action_json, ensure_ascii=False)}
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```"""
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try:
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if self.output_fixing_parser is not None:
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parsed_obj: Union[
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AgentAction, AgentFinish
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] = self.output_fixing_parser.parse(action_str)
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else:
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parsed_obj = self.base_parser.parse(action_str)
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return parsed_obj
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except Exception as e:
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raise OutputParserException(f"Could not parse LLM output: {text}") from e
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@property
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def _type(self) -> str:
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return "structured_chat_ChatGLM3_6b_with_retries"
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class StructuredGLM3ChatAgent(Agent):
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"""Structured Chat Agent."""
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output_parser: AgentOutputParser = Field(
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default_factory=StructuredChatOutputParserWithRetries
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)
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"""Output parser for the agent."""
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@property
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def observation_prefix(self) -> str:
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"""Prefix to append the ChatGLM3-6B observation with."""
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return "Observation:"
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@property
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def llm_prefix(self) -> str:
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"""Prefix to append the llm call with."""
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return "Thought:"
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def _construct_scratchpad(
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self, intermediate_steps: List[Tuple[AgentAction, str]]
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) -> str:
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agent_scratchpad = super()._construct_scratchpad(intermediate_steps)
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if not isinstance(agent_scratchpad, str):
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raise ValueError("agent_scratchpad should be of type string.")
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if agent_scratchpad:
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return (
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f"This was your previous work "
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f"(but I haven't seen any of it! I only see what "
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f"you return as final answer):\n{agent_scratchpad}"
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)
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else:
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return agent_scratchpad
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@classmethod
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def _get_default_output_parser(
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cls, llm: Optional[BaseLanguageModel] = None, **kwargs: Any
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) -> AgentOutputParser:
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return StructuredChatOutputParserWithRetries(llm=llm)
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@property
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def _stop(self) -> List[str]:
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return ["<|observation|>"]
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@classmethod
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def create_prompt(
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cls,
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tools: Sequence[BaseTool],
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prompt: str = None,
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input_variables: Optional[List[str]] = None,
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memory_prompts: Optional[List[BasePromptTemplate]] = None,
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) -> BasePromptTemplate:
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tools_json = []
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tool_names = []
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for tool in tools:
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tool_schema = model_schema(tool.args_schema) if tool.args_schema else {}
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simplified_config_langchain = {
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"name": tool.name,
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"description": tool.description,
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"parameters": tool_schema.get("properties", {})
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}
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tools_json.append(simplified_config_langchain)
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tool_names.append(tool.name)
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formatted_tools = "\n".join([
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f"{tool['name']}: {tool['description']}, args: {tool['parameters']}"
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for tool in tools_json
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])
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formatted_tools = formatted_tools.replace("'", "\\'").replace("{", "{{").replace("}", "}}")
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template = prompt.format(tool_names=tool_names,
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tools=formatted_tools,
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history="None",
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input="{input}",
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agent_scratchpad="{agent_scratchpad}")
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if input_variables is None:
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input_variables = ["input", "agent_scratchpad"]
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_memory_prompts = memory_prompts or []
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messages = [
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SystemMessagePromptTemplate.from_template(template),
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*_memory_prompts,
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]
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return ChatPromptTemplate(input_variables=input_variables, messages=messages)
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@classmethod
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def from_llm_and_tools(
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cls,
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llm: BaseLanguageModel,
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tools: Sequence[BaseTool],
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prompt: str = None,
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callback_manager: Optional[BaseCallbackManager] = None,
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output_parser: Optional[AgentOutputParser] = None,
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human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
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input_variables: Optional[List[str]] = None,
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memory_prompts: Optional[List[BasePromptTemplate]] = None,
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**kwargs: Any,
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) -> Agent:
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"""Construct an agent from an LLM and tools."""
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cls._validate_tools(tools)
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prompt = cls.create_prompt(
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tools,
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prompt=prompt,
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input_variables=input_variables,
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memory_prompts=memory_prompts,
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)
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llm_chain = LLMChain(
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llm=llm,
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prompt=prompt,
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callback_manager=callback_manager,
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)
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tool_names = [tool.name for tool in tools]
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_output_parser = output_parser or cls._get_default_output_parser(llm=llm)
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return cls(
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llm_chain=llm_chain,
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allowed_tools=tool_names,
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output_parser=_output_parser,
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**kwargs,
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)
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@property
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def _agent_type(self) -> str:
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raise ValueError
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def initialize_glm3_agent(
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tools: Sequence[BaseTool],
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llm: BaseLanguageModel,
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prompt: str = None,
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memory: Optional[ConversationBufferWindowMemory] = None,
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agent_kwargs: Optional[dict] = None,
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*,
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tags: Optional[Sequence[str]] = None,
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**kwargs: Any,
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) -> AgentExecutor:
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tags_ = list(tags) if tags else []
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agent_kwargs = agent_kwargs or {}
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agent_obj = StructuredGLM3ChatAgent.from_llm_and_tools(
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llm=llm,
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tools=tools,
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prompt=prompt,
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**agent_kwargs
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)
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return AgentExecutor.from_agent_and_tools(
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agent=agent_obj,
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tools=tools,
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memory=memory,
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tags=tags_,
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**kwargs,
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)
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