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venv/Lib/site-packages/langchain_classic/agents/conversational/__init__.py

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+"""An agent designed to hold a conversation in addition to using tools."""

venv/Lib/site-packages/langchain_classic/agents/conversational/base.py

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+"""An agent designed to hold a conversation in addition to using tools."""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import Any
+
+from langchain_core._api import deprecated
+from langchain_core.callbacks import BaseCallbackManager
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.prompts import PromptTemplate
+from langchain_core.tools import BaseTool
+from pydantic import Field
+from typing_extensions import override
+
+from langchain_classic._api.deprecation import AGENT_DEPRECATION_WARNING
+from langchain_classic.agents.agent import Agent, AgentOutputParser
+from langchain_classic.agents.agent_types import AgentType
+from langchain_classic.agents.conversational.output_parser import ConvoOutputParser
+from langchain_classic.agents.conversational.prompt import (
+    FORMAT_INSTRUCTIONS,
+    PREFIX,
+    SUFFIX,
+)
+from langchain_classic.agents.utils import validate_tools_single_input
+from langchain_classic.chains import LLMChain
+
+
+@deprecated(
+    "0.1.0",
+    message=AGENT_DEPRECATION_WARNING,
+    removal="1.0",
+)
+class ConversationalAgent(Agent):
+    """An agent that holds a conversation in addition to using tools."""
+
+    ai_prefix: str = "AI"
+    """Prefix to use before AI output."""
+    output_parser: AgentOutputParser = Field(default_factory=ConvoOutputParser)
+    """Output parser for the agent."""
+
+    @classmethod
+    @override
+    def _get_default_output_parser(
+        cls,
+        ai_prefix: str = "AI",
+        **kwargs: Any,
+    ) -> AgentOutputParser:
+        return ConvoOutputParser(ai_prefix=ai_prefix)
+
+    @property
+    def _agent_type(self) -> str:
+        """Return Identifier of agent type."""
+        return AgentType.CONVERSATIONAL_REACT_DESCRIPTION
+
+    @property
+    def observation_prefix(self) -> str:
+        """Prefix to append the observation with.
+
+        Returns:
+            "Observation: "
+        """
+        return "Observation: "
+
+    @property
+    def llm_prefix(self) -> str:
+        """Prefix to append the llm call with.
+
+        Returns:
+            "Thought: "
+        """
+        return "Thought:"
+
+    @classmethod
+    def create_prompt(
+        cls,
+        tools: Sequence[BaseTool],
+        prefix: str = PREFIX,
+        suffix: str = SUFFIX,
+        format_instructions: str = FORMAT_INSTRUCTIONS,
+        ai_prefix: str = "AI",
+        human_prefix: str = "Human",
+        input_variables: list[str] | None = None,
+    ) -> PromptTemplate:
+        """Create prompt in the style of the zero-shot agent.
+
+        Args:
+            tools: List of tools the agent will have access to, used to format the
+                prompt.
+            prefix: String to put before the list of tools.
+            suffix: String to put after the list of tools.
+            format_instructions: Instructions on how to use the tools.
+            ai_prefix: String to use before AI output.
+            human_prefix: String to use before human output.
+            input_variables: List of input variables the final prompt will expect.
+                Defaults to `["input", "chat_history", "agent_scratchpad"]`.
+
+        Returns:
+            A PromptTemplate with the template assembled from the pieces here.
+        """
+        tool_strings = "\n".join(
+            [f"> {tool.name}: {tool.description}" for tool in tools],
+        )
+        tool_names = ", ".join([tool.name for tool in tools])
+        format_instructions = format_instructions.format(
+            tool_names=tool_names,
+            ai_prefix=ai_prefix,
+            human_prefix=human_prefix,
+        )
+        template = f"{prefix}\n\n{tool_strings}\n\n{format_instructions}\n\n{suffix}"
+        if input_variables is None:
+            input_variables = ["input", "chat_history", "agent_scratchpad"]
+        return PromptTemplate(template=template, input_variables=input_variables)
+
+    @classmethod
+    def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
+        super()._validate_tools(tools)
+        validate_tools_single_input(cls.__name__, tools)
+
+    @classmethod
+    def from_llm_and_tools(
+        cls,
+        llm: BaseLanguageModel,
+        tools: Sequence[BaseTool],
+        callback_manager: BaseCallbackManager | None = None,
+        output_parser: AgentOutputParser | None = None,
+        prefix: str = PREFIX,
+        suffix: str = SUFFIX,
+        format_instructions: str = FORMAT_INSTRUCTIONS,
+        ai_prefix: str = "AI",
+        human_prefix: str = "Human",
+        input_variables: list[str] | None = None,
+        **kwargs: Any,
+    ) -> Agent:
+        """Construct an agent from an LLM and tools.
+
+        Args:
+            llm: The language model to use.
+            tools: A list of tools to use.
+            callback_manager: The callback manager to use.
+            output_parser: The output parser to use.
+            prefix: The prefix to use in the prompt.
+            suffix: The suffix to use in the prompt.
+            format_instructions: The format instructions to use.
+            ai_prefix: The prefix to use before AI output.
+            human_prefix: The prefix to use before human output.
+            input_variables: The input variables to use.
+            **kwargs: Any additional keyword arguments to pass to the agent.
+
+        Returns:
+            An agent.
+        """
+        cls._validate_tools(tools)
+        prompt = cls.create_prompt(
+            tools,
+            ai_prefix=ai_prefix,
+            human_prefix=human_prefix,
+            prefix=prefix,
+            suffix=suffix,
+            format_instructions=format_instructions,
+            input_variables=input_variables,
+        )
+        llm_chain = LLMChain(
+            llm=llm,
+            prompt=prompt,
+            callback_manager=callback_manager,
+        )
+        tool_names = [tool.name for tool in tools]
+        _output_parser = output_parser or cls._get_default_output_parser(
+            ai_prefix=ai_prefix,
+        )
+        return cls(
+            llm_chain=llm_chain,
+            allowed_tools=tool_names,
+            ai_prefix=ai_prefix,
+            output_parser=_output_parser,
+            **kwargs,
+        )

venv/Lib/site-packages/langchain_classic/agents/conversational/output_parser.py

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+import re
+
+from langchain_core.agents import AgentAction, AgentFinish
+from langchain_core.exceptions import OutputParserException
+
+from langchain_classic.agents.agent import AgentOutputParser
+from langchain_classic.agents.conversational.prompt import FORMAT_INSTRUCTIONS
+
+
+class ConvoOutputParser(AgentOutputParser):
+    """Output parser for the conversational agent."""
+
+    ai_prefix: str = "AI"
+    """Prefix to use before AI output."""
+
+    format_instructions: str = FORMAT_INSTRUCTIONS
+    """Default formatting instructions"""
+
+    def get_format_instructions(self) -> str:
+        """Returns formatting instructions for the given output parser."""
+        return self.format_instructions
+
+    def parse(self, text: str) -> AgentAction | AgentFinish:
+        """Parse the output from the agent into an AgentAction or AgentFinish object.
+
+        Args:
+            text: The text to parse.
+
+        Returns:
+            An AgentAction or AgentFinish object.
+        """
+        if f"{self.ai_prefix}:" in text:
+            return AgentFinish(
+                {"output": text.rsplit(f"{self.ai_prefix}:", maxsplit=1)[-1].strip()},
+                text,
+            )
+        regex = r"Action: (.*?)[\n]*Action Input: ([\s\S]*)"
+        match = re.search(regex, text, re.DOTALL)
+        if not match:
+            msg = f"Could not parse LLM output: `{text}`"
+            raise OutputParserException(msg)
+        action = match.group(1)
+        action_input = match.group(2)
+        return AgentAction(action.strip(), action_input.strip(" ").strip('"'), text)
+
+    @property
+    def _type(self) -> str:
+        return "conversational"

venv/Lib/site-packages/langchain_classic/agents/conversational/prompt.py

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+PREFIX = """Assistant is a large language model trained by OpenAI.
+
+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.
+
+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.
+
+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.
+
+TOOLS:
+------
+
+Assistant has access to the following tools:"""  # noqa: E501
+FORMAT_INSTRUCTIONS = """To use a tool, please use the following format:
+
+```
+Thought: Do I need to use a tool? Yes
+Action: the action to take, should be one of [{tool_names}]
+Action Input: the input to the action
+Observation: the result of the action
+```
+
+When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:
+
+```
+Thought: Do I need to use a tool? No
+{ai_prefix}: [your response here]
+```"""  # noqa: E501
+
+SUFFIX = """Begin!
+
+Previous conversation history:
+{chat_history}
+
+New input: {input}
+{agent_scratchpad}"""