initial commit

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

@@ -0,0 +1 @@
+"""Implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf."""

venv/Lib/site-packages/langchain_classic/agents/react/agent.py

@@ -0,0 +1,150 @@
+from __future__ import annotations
+
+from collections.abc import Sequence
+
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.prompts import BasePromptTemplate
+from langchain_core.runnables import Runnable, RunnablePassthrough
+from langchain_core.tools import BaseTool
+from langchain_core.tools.render import ToolsRenderer, render_text_description
+
+from langchain_classic.agents import AgentOutputParser
+from langchain_classic.agents.format_scratchpad import format_log_to_str
+from langchain_classic.agents.output_parsers import ReActSingleInputOutputParser
+
+
+def create_react_agent(
+    llm: BaseLanguageModel,
+    tools: Sequence[BaseTool],
+    prompt: BasePromptTemplate,
+    output_parser: AgentOutputParser | None = None,
+    tools_renderer: ToolsRenderer = render_text_description,
+    *,
+    stop_sequence: bool | list[str] = True,
+) -> Runnable:
+    r"""Create an agent that uses ReAct prompting.
+
+    Based on paper "ReAct: Synergizing Reasoning and Acting in Language Models"
+    (https://arxiv.org/abs/2210.03629)
+
+    !!! warning
+
+        This implementation is based on the foundational ReAct paper but is older and
+        not well-suited for production applications.
+
+        For a more robust and feature-rich implementation, we recommend using the
+        `create_agent` function from the `langchain` library.
+
+        See the
+        [reference doc](https://reference.langchain.com/python/langchain/agents/)
+        for more information.
+
+    Args:
+        llm: LLM to use as the agent.
+        tools: Tools this agent has access to.
+        prompt: The prompt to use. See Prompt section below for more.
+        output_parser: AgentOutputParser for parse the LLM output.
+        tools_renderer: This controls how the tools are converted into a string and
+            then passed into the LLM.
+        stop_sequence: bool or list of str.
+            If `True`, adds a stop token of "Observation:" to avoid hallucinates.
+            If `False`, does not add a stop token.
+            If a list of str, uses the provided list as the stop tokens.
+
+            You may to set this to False if the LLM you are using
+            does not support stop sequences.
+
+    Returns:
+        A Runnable sequence representing an agent. It takes as input all the same input
+        variables as the prompt passed in does. It returns as output either an
+        AgentAction or AgentFinish.
+
+    Examples:
+        ```python
+        from langchain_classic import hub
+        from langchain_openai import OpenAI
+        from langchain_classic.agents import AgentExecutor, create_react_agent
+
+        prompt = hub.pull("hwchase17/react")
+        model = OpenAI()
+        tools = ...
+
+        agent = create_react_agent(model, tools, prompt)
+        agent_executor = AgentExecutor(agent=agent, tools=tools)
+
+        agent_executor.invoke({"input": "hi"})
+
+        # Use with chat history
+        from langchain_core.messages import AIMessage, HumanMessage
+
+        agent_executor.invoke(
+            {
+                "input": "what's my name?",
+                # Notice that chat_history is a string
+                # since this prompt is aimed at LLMs, not chat models
+                "chat_history": "Human: My name is Bob\nAI: Hello Bob!",
+            }
+        )
+        ```
+
+    Prompt:
+
+        The prompt must have input keys:
+            * `tools`: contains descriptions and arguments for each tool.
+            * `tool_names`: contains all tool names.
+            * `agent_scratchpad`: contains previous agent actions and tool outputs as a
+                string.
+
+        Here's an example:
+
+        ```python
+        from langchain_core.prompts import PromptTemplate
+
+        template = '''Answer the following questions as best you can. You have access to the following tools:
+
+        {tools}
+
+        Use the following format:
+
+        Question: the input question you must answer
+        Thought: you should always think about what to do
+        Action: the action to take, should be one of [{tool_names}]
+        Action Input: the input to the action
+        Observation: the result of the action
+        ... (this Thought/Action/Action Input/Observation can repeat N times)
+        Thought: I now know the final answer
+        Final Answer: the final answer to the original input question
+
+        Begin!
+
+        Question: {input}
+        Thought:{agent_scratchpad}'''
+
+        prompt = PromptTemplate.from_template(template)
+        ```
+    """  # noqa: E501
+    missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference(
+        prompt.input_variables + list(prompt.partial_variables),
+    )
+    if missing_vars:
+        msg = f"Prompt missing required variables: {missing_vars}"
+        raise ValueError(msg)
+
+    prompt = prompt.partial(
+        tools=tools_renderer(list(tools)),
+        tool_names=", ".join([t.name for t in tools]),
+    )
+    if stop_sequence:
+        stop = ["\nObservation"] if stop_sequence is True else stop_sequence
+        llm_with_stop = llm.bind(stop=stop)
+    else:
+        llm_with_stop = llm
+    output_parser = output_parser or ReActSingleInputOutputParser()
+    return (
+        RunnablePassthrough.assign(
+            agent_scratchpad=lambda x: format_log_to_str(x["intermediate_steps"]),
+        )
+        | prompt
+        | llm_with_stop
+        | output_parser
+    )

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

@@ -0,0 +1,189 @@
+"""Chain that implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf."""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Any
+
+from langchain_core._api import deprecated
+from langchain_core.documents import Document
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.prompts import BasePromptTemplate
+from langchain_core.tools import BaseTool, Tool
+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, AgentExecutor, AgentOutputParser
+from langchain_classic.agents.agent_types import AgentType
+from langchain_classic.agents.react.output_parser import ReActOutputParser
+from langchain_classic.agents.react.textworld_prompt import TEXTWORLD_PROMPT
+from langchain_classic.agents.react.wiki_prompt import WIKI_PROMPT
+from langchain_classic.agents.utils import validate_tools_single_input
+
+if TYPE_CHECKING:
+    from langchain_community.docstore.base import Docstore
+
+
+_LOOKUP_AND_SEARCH_TOOLS = {"Lookup", "Search"}
+
+
+@deprecated(
+    "0.1.0",
+    message=AGENT_DEPRECATION_WARNING,
+    removal="1.0",
+)
+class ReActDocstoreAgent(Agent):
+    """Agent for the ReAct chain."""
+
+    output_parser: AgentOutputParser = Field(default_factory=ReActOutputParser)
+
+    @classmethod
+    @override
+    def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
+        return ReActOutputParser()
+
+    @property
+    def _agent_type(self) -> str:
+        """Return Identifier of an agent type."""
+        return AgentType.REACT_DOCSTORE
+
+    @classmethod
+    @override
+    def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
+        """Return default prompt."""
+        return WIKI_PROMPT
+
+    @classmethod
+    def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
+        validate_tools_single_input(cls.__name__, tools)
+        super()._validate_tools(tools)
+        if len(tools) != len(_LOOKUP_AND_SEARCH_TOOLS):
+            msg = f"Exactly two tools must be specified, but got {tools}"
+            raise ValueError(msg)
+        tool_names = {tool.name for tool in tools}
+        if tool_names != _LOOKUP_AND_SEARCH_TOOLS:
+            msg = f"Tool names should be Lookup and Search, got {tool_names}"
+            raise ValueError(msg)
+
+    @property
+    def observation_prefix(self) -> str:
+        """Prefix to append the observation with."""
+        return "Observation: "
+
+    @property
+    def _stop(self) -> list[str]:
+        return ["\nObservation:"]
+
+    @property
+    def llm_prefix(self) -> str:
+        """Prefix to append the LLM call with."""
+        return "Thought:"
+
+
+@deprecated(
+    "0.1.0",
+    message=AGENT_DEPRECATION_WARNING,
+    removal="1.0",
+)
+class DocstoreExplorer:
+    """Class to assist with exploration of a document store."""
+
+    def __init__(self, docstore: Docstore):
+        """Initialize with a docstore, and set initial document to None."""
+        self.docstore = docstore
+        self.document: Document | None = None
+        self.lookup_str = ""
+        self.lookup_index = 0
+
+    def search(self, term: str) -> str:
+        """Search for a term in the docstore, and if found save."""
+        result = self.docstore.search(term)
+        if isinstance(result, Document):
+            self.document = result
+            return self._summary
+        self.document = None
+        return result
+
+    def lookup(self, term: str) -> str:
+        """Lookup a term in document (if saved)."""
+        if self.document is None:
+            msg = "Cannot lookup without a successful search first"
+            raise ValueError(msg)
+        if term.lower() != self.lookup_str:
+            self.lookup_str = term.lower()
+            self.lookup_index = 0
+        else:
+            self.lookup_index += 1
+        lookups = [p for p in self._paragraphs if self.lookup_str in p.lower()]
+        if len(lookups) == 0:
+            return "No Results"
+        if self.lookup_index >= len(lookups):
+            return "No More Results"
+        result_prefix = f"(Result {self.lookup_index + 1}/{len(lookups)})"
+        return f"{result_prefix} {lookups[self.lookup_index]}"
+
+    @property
+    def _summary(self) -> str:
+        return self._paragraphs[0]
+
+    @property
+    def _paragraphs(self) -> list[str]:
+        if self.document is None:
+            msg = "Cannot get paragraphs without a document"
+            raise ValueError(msg)
+        return self.document.page_content.split("\n\n")
+
+
+@deprecated(
+    "0.1.0",
+    message=AGENT_DEPRECATION_WARNING,
+    removal="1.0",
+)
+class ReActTextWorldAgent(ReActDocstoreAgent):
+    """Agent for the ReAct TextWorld chain."""
+
+    @classmethod
+    @override
+    def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
+        """Return default prompt."""
+        return TEXTWORLD_PROMPT
+
+    @classmethod
+    def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
+        validate_tools_single_input(cls.__name__, tools)
+        super()._validate_tools(tools)
+        if len(tools) != 1:
+            msg = f"Exactly one tool must be specified, but got {tools}"
+            raise ValueError(msg)
+        tool_names = {tool.name for tool in tools}
+        if tool_names != {"Play"}:
+            msg = f"Tool name should be Play, got {tool_names}"
+            raise ValueError(msg)
+
+
+@deprecated(
+    "0.1.0",
+    message=AGENT_DEPRECATION_WARNING,
+    removal="1.0",
+)
+class ReActChain(AgentExecutor):
+    """[Deprecated] Chain that implements the ReAct paper."""
+
+    def __init__(self, llm: BaseLanguageModel, docstore: Docstore, **kwargs: Any):
+        """Initialize with the LLM and a docstore."""
+        docstore_explorer = DocstoreExplorer(docstore)
+        tools = [
+            Tool(
+                name="Search",
+                func=docstore_explorer.search,
+                description="Search for a term in the docstore.",
+            ),
+            Tool(
+                name="Lookup",
+                func=docstore_explorer.lookup,
+                description="Lookup a term in the docstore.",
+            ),
+        ]
+        agent = ReActDocstoreAgent.from_llm_and_tools(llm, tools)
+        super().__init__(agent=agent, tools=tools, **kwargs)

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

@@ -0,0 +1,34 @@
+import re
+
+from langchain_core.agents import AgentAction, AgentFinish
+from langchain_core.exceptions import OutputParserException
+from typing_extensions import override
+
+from langchain_classic.agents.agent import AgentOutputParser
+
+
+class ReActOutputParser(AgentOutputParser):
+    """Output parser for the ReAct agent."""
+
+    @override
+    def parse(self, text: str) -> AgentAction | AgentFinish:
+        action_prefix = "Action: "
+        if not text.strip().split("\n")[-1].startswith(action_prefix):
+            msg = f"Could not parse LLM Output: {text}"
+            raise OutputParserException(msg)
+        action_block = text.strip().split("\n")[-1]
+
+        action_str = action_block[len(action_prefix) :]
+        # Parse out the action and the directive.
+        re_matches = re.search(r"(.*?)\[(.*?)\]", action_str)
+        if re_matches is None:
+            msg = f"Could not parse action directive: {action_str}"
+            raise OutputParserException(msg)
+        action, action_input = re_matches.group(1), re_matches.group(2)
+        if action == "Finish":
+            return AgentFinish({"output": action_input}, text)
+        return AgentAction(action, action_input, text)
+
+    @property
+    def _type(self) -> str:
+        return "react"

venv/Lib/site-packages/langchain_classic/agents/react/textworld_prompt.py

@@ -0,0 +1,51 @@
+from langchain_core.prompts.prompt import PromptTemplate
+
+EXAMPLES = [
+    """Setup: You are now playing a fast paced round of TextWorld! Here is your task for
+today. First of all, you could, like, try to travel east. After that, take the
+binder from the locker. With the binder, place the binder on the mantelpiece.
+Alright, thanks!
+
+-= Vault =-
+You've just walked into a vault. You begin to take stock of what's here.
+
+An open safe is here. What a letdown! The safe is empty! You make out a shelf.
+But the thing hasn't got anything on it. What, you think everything in TextWorld
+should have stuff on it?
+
+You don't like doors? Why not try going east, that entranceway is unguarded.
+
+Thought: I need to travel east
+Action: Play[go east]
+Observation: -= Office =-
+You arrive in an office. An ordinary one.
+
+You can make out a locker. The locker contains a binder. You see a case. The
+case is empty, what a horrible day! You lean against the wall, inadvertently
+pressing a secret button. The wall opens up to reveal a mantelpiece. You wonder
+idly who left that here. The mantelpiece is standard. The mantelpiece appears to
+be empty. If you haven't noticed it already, there seems to be something there
+by the wall, it's a table. Unfortunately, there isn't a thing on it. Hm. Oh well
+There is an exit to the west. Don't worry, it is unguarded.
+
+Thought: I need to take the binder from the locker
+Action: Play[take binder]
+Observation: You take the binder from the locker.
+
+Thought: I need to place the binder on the mantelpiece
+Action: Play[put binder on mantelpiece]
+
+Observation: You put the binder on the mantelpiece.
+Your score has just gone up by one point.
+*** The End ***
+Thought: The End has occurred
+Action: Finish[yes]
+
+"""
+]
+SUFFIX = """\n\nSetup: {input}
+{agent_scratchpad}"""
+
+TEXTWORLD_PROMPT = PromptTemplate.from_examples(
+    EXAMPLES, SUFFIX, ["input", "agent_scratchpad"]
+)

venv/Lib/site-packages/langchain_classic/agents/react/wiki_prompt.py

@@ -0,0 +1,70 @@
+from langchain_core.prompts.prompt import PromptTemplate
+
+EXAMPLES = [
+    """Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?
+Thought: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of the area.
+Action: Search[Colorado orogeny]
+Observation: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas.
+Thought: It does not mention the eastern sector. So I need to look up eastern sector.
+Action: Lookup[eastern sector]
+Observation: (Result 1 / 1) The eastern sector extends into the High Plains and is called the Central Plains orogeny.
+Thought: The eastern sector of Colorado orogeny extends into the High Plains. So I need to search High Plains and find its elevation range.
+Action: Search[High Plains]
+Observation: High Plains refers to one of two distinct land regions
+Thought: I need to instead search High Plains (United States).
+Action: Search[High Plains (United States)]
+Observation: The High Plains are a subregion of the Great Plains. From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130 m).[3]
+Thought: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.
+Action: Finish[1,800 to 7,000 ft]""",  # noqa: E501
+    """Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
+Thought: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
+Action: Search[Milhouse]
+Observation: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
+Thought: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
+Action: Lookup[named after]
+Observation: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
+Thought: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
+Action: Finish[Richard Nixon]""",  # noqa: E501
+    """Question: Which documentary is about Finnish rock groups, Adam Clayton Powell or The Saimaa Gesture?
+Thought: I need to search Adam Clayton Powell and The Saimaa Gesture, and find which documentary is about Finnish rock groups.
+Action: Search[Adam Clayton Powell]
+Observation: Could not find [Adam Clayton Powell]. Similar: ['Adam Clayton Powell III', 'Seventh Avenue (Manhattan)', 'Adam Clayton Powell Jr. State Office Building', 'Isabel Washington Powell', 'Adam Powell', 'Adam Clayton Powell (film)', 'Giancarlo Esposito'].
+Thought: To find the documentary, I can search Adam Clayton Powell (film).
+Action: Search[Adam Clayton Powell (film)]
+Observation: Adam Clayton Powell is a 1989 American documentary film directed by Richard Kilberg. The film is about the rise and fall of influential African-American politician Adam Clayton Powell Jr.[3][4] It was later aired as part of the PBS series The American Experience.
+Thought: Adam Clayton Powell (film) is a documentary about an African-American politician, not Finnish rock groups. So the documentary about Finnish rock groups must instead be The Saimaa Gesture.
+Action: Finish[The Saimaa Gesture]""",  # noqa: E501
+    """Question: What profession does Nicholas Ray and Elia Kazan have in common?
+Thought: I need to search Nicholas Ray and Elia Kazan, find their professions, then find the profession they have in common.
+Action: Search[Nicholas Ray]
+Observation: Nicholas Ray (born Raymond Nicholas Kienzle Jr., August 7, 1911 - June 16, 1979) was an American film director, screenwriter, and actor best known for the 1955 film Rebel Without a Cause.
+Thought: Professions of Nicholas Ray are director, screenwriter, and actor. I need to search Elia Kazan next and find his professions.
+Action: Search[Elia Kazan]
+Observation: Elia Kazan was an American film and theatre director, producer, screenwriter and actor.
+Thought: Professions of Elia Kazan are director, producer, screenwriter, and actor. So profession Nicholas Ray and Elia Kazan have in common is director, screenwriter, and actor.
+Action: Finish[director, screenwriter, actor]""",  # noqa: E501
+    """Question: Which magazine was started first Arthur's Magazine or First for Women?
+Thought: I need to search Arthur's Magazine and First for Women, and find which was started first.
+Action: Search[Arthur's Magazine]
+Observation: Arthur's Magazine (1844-1846) was an American literary periodical published in Philadelphia in the 19th century.
+Thought: Arthur's Magazine was started in 1844. I need to search First for Women next.
+Action: Search[First for Women]
+Observation: First for Women is a woman's magazine published by Bauer Media Group in the USA.[1] The magazine was started in 1989.
+Thought: First for Women was started in 1989. 1844 (Arthur's Magazine) < 1989 (First for Women), so Arthur's Magazine was started first.
+Action: Finish[Arthur's Magazine]""",  # noqa: E501
+    """Question: Were Pavel Urysohn and Leonid Levin known for the same type of work?
+Thought: I need to search Pavel Urysohn and Leonid Levin, find their types of work, then find if they are the same.
+Action: Search[Pavel Urysohn]
+Observation: Pavel Samuilovich Urysohn (February 3, 1898 - August 17, 1924) was a Soviet mathematician who is best known for his contributions in dimension theory.
+Thought: Pavel Urysohn is a mathematician. I need to search Leonid Levin next and find its type of work.
+Action: Search[Leonid Levin]
+Observation: Leonid Anatolievich Levin is a Soviet-American mathematician and computer scientist.
+Thought: Leonid Levin is a mathematician and computer scientist. So Pavel Urysohn and Leonid Levin have the same type of work.
+Action: Finish[yes]""",  # noqa: E501
+]
+SUFFIX = """\nQuestion: {input}
+{agent_scratchpad}"""
+
+WIKI_PROMPT = PromptTemplate.from_examples(
+    EXAMPLES, SUFFIX, ["input", "agent_scratchpad"]
+)