forked from Archives/langchain
RFC: conversational agent (#464)
Co-authored-by: Bruno Bornsztein <bruno.bornsztein@gmail.com>harrison/pinecone-try-except
Harrison Chase
1 year ago
committed by
GitHub
parent
330a5b42d4
commit
74932f2516
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"""An agent designed to hold a conversation in addition to using tools."""
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"""An agent designed to hold a conversation in addition to using tools."""
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from __future__ import annotations
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import re
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from typing import Any, List, Optional, Tuple
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from langchain.agents.agent import Agent
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from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
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from langchain.agents.tools import Tool
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from langchain.callbacks.base import BaseCallbackManager
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from langchain.chains import LLMChain
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from langchain.llms import BaseLLM
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from langchain.prompts import PromptTemplate
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class ConversationalAgent(Agent):
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"""An agent designed to hold a conversation in addition to using tools."""
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ai_prefix: str = "AI"
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@property
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def observation_prefix(self) -> str:
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"""Prefix to append the 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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@classmethod
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def create_prompt(
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cls,
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tools: List[Tool],
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prefix: str = PREFIX,
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suffix: str = SUFFIX,
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ai_prefix: str = "AI",
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human_prefix: str = "Human",
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input_variables: Optional[List[str]] = None,
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) -> PromptTemplate:
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"""Create prompt in the style of the zero shot agent.
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Args:
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tools: List of tools the agent will have access to, used to format the
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prompt.
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prefix: String to put before the list of tools.
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suffix: String to put after the list of tools.
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ai_prefix: String to use before AI output.
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human_prefix: String to use before human output.
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input_variables: List of input variables the final prompt will expect.
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Returns:
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A PromptTemplate with the template assembled from the pieces here.
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"""
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tool_strings = "\n".join(
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[f"> {tool.name}: {tool.description}" for tool in tools]
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)
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tool_names = ", ".join([tool.name for tool in tools])
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format_instructions = FORMAT_INSTRUCTIONS.format(
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tool_names=tool_names, ai_prefix=ai_prefix, human_prefix=human_prefix
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)
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template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
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if input_variables is None:
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input_variables = ["input", "chat_history", "agent_scratchpad"]
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return PromptTemplate(template=template, input_variables=input_variables)
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@property
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def finish_tool_name(self) -> str:
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"""Name of the tool to use to finish the chain."""
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return self.ai_prefix
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def _extract_tool_and_input(self, llm_output: str) -> Optional[Tuple[str, str]]:
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if f"{self.ai_prefix}: " in llm_output:
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return self.ai_prefix, llm_output.split(f"{self.ai_prefix}: ")[-1]
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regex = r"Action: (.*?)\nAction Input: (.*)"
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match = re.search(regex, llm_output)
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if not match:
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raise ValueError(f"Could not parse LLM output: `{llm_output}`")
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action = match.group(1)
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action_input = match.group(2)
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return action, action_input.strip(" ").strip('"')
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@classmethod
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def from_llm_and_tools(
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cls,
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llm: BaseLLM,
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tools: List[Tool],
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callback_manager: Optional[BaseCallbackManager] = None,
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ai_prefix: str = "AI",
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human_prefix: str = "Human",
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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, ai_prefix=ai_prefix, human_prefix=human_prefix
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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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return cls(llm_chain=llm_chain, ai_prefix=ai_prefix, **kwargs)
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# flake8: noqa
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PREFIX = """Assistant is a large language model trained by OpenAI.
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Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
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Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
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Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
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TOOLS:
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------
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Assistant has access to the following tools:"""
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FORMAT_INSTRUCTIONS = """To use a tool, please use the following format:
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```
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Thought: Do I need to use a tool? Yes
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Action: the action to take, should be one of [{tool_names}]
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Action Input: the input to the action
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Observation: the result of the action
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```
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When you have a response to say to the Human, or if you do not need to use a too, you MUST use the format:
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```
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Thought: Do I need to use a tool? No
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{ai_prefix}: [your response here]
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```"""
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SUFFIX = """Begin!
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Previous conversation history:
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{chat_history}
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New input: {input}
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{agent_scratchpad}"""
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