@property
    def input_keys(self) -> List[str]:
        """Return the input keys.
        :meta private:
        """
        return self.agent.input_keys
    @property
    def output_keys(self) -> List[str]:
        """Return the singular output key.
        :meta private:
        """
        if self.return_intermediate_steps:
            return self.agent.return_values + ["intermediate_steps"]
        else:
            return self.agent.return_values
    def _should_continue(self, iterations: int) -> bool:
        if self.max_iterations is None:
            return True
        else:
            return iterations < self.max_iterations
    def _return(self, output: AgentFinish, intermediate_steps: list) -> Dict[str, Any]:
        self.callback_manager.on_agent_finish(
            output, color="green", verbose=self.verbose
        )
        final_output = output.return_values
        if self.return_intermediate_steps:
            final_output["intermediate_steps"] = intermediate_steps
        return final_output
    async def _areturn(
        self, output: AgentFinish, intermediate_steps: list
    ) -> Dict[str, Any]:
        if self.callback_manager.is_async:
            await self.callback_manager.on_agent_finish(
                output, color="green", verbose=self.verbose
            )
        else:
            self.callback_manager.on_agent_finish(
                output, color="green", verbose=self.verbose
            )
        final_output = output.return_values
        if self.return_intermediate_steps:
            final_output["intermediate_steps"] = intermediate_steps
        return final_output
    def _take_next_step(
        self,
        name_to_tool_map: Dict[str, BaseTool],
        color_mapping: Dict[str, str],
        inputs: Dict[str, str],


color_mapping: Dict[str, str],
        inputs: Dict[str, str],
        intermediate_steps: List[Tuple[AgentAction, str]],
    ) -> Union[AgentFinish, Tuple[AgentAction, str]]:
        """Take a single step in the thought-action-observation loop.
        Override this to take control of how the agent makes and acts on choices.
        """
        # Call the LLM to see what to do.
        output = self.agent.plan(intermediate_steps, **inputs)
        # If the tool chosen is the finishing tool, then we end and return.
        if isinstance(output, AgentFinish):
            return output
        self.callback_manager.on_agent_action(
            output, verbose=self.verbose, color="green"
        )
        # Otherwise we lookup the tool
        if output.tool in name_to_tool_map:
            tool = name_to_tool_map[output.tool]
            return_direct = tool.return_direct
            color = color_mapping[output.tool]
            llm_prefix = "" if return_direct else self.agent.llm_prefix
            # We then call the tool on the tool input to get an observation
            observation = tool.run(
                output.tool_input,
                verbose=self.verbose,
                color=color,
                llm_prefix=llm_prefix,
                observation_prefix=self.agent.observation_prefix,
            )
        else:
            observation = InvalidTool().run(
                output.tool,
                verbose=self.verbose,
                color=None,
                llm_prefix="",
                observation_prefix=self.agent.observation_prefix,
            )
        return output, observation
    async def _atake_next_step(
        self,
        name_to_tool_map: Dict[str, BaseTool],
        color_mapping: Dict[str, str],
        inputs: Dict[str, str],


color_mapping: Dict[str, str],
        inputs: Dict[str, str],
        intermediate_steps: List[Tuple[AgentAction, str]],
    ) -> Union[AgentFinish, Tuple[AgentAction, str]]:
        """Take a single step in the thought-action-observation loop.
        Override this to take control of how the agent makes and acts on choices.
        """
        # Call the LLM to see what to do.
        output = await self.agent.aplan(intermediate_steps, **inputs)
        # If the tool chosen is the finishing tool, then we end and return.
        if isinstance(output, AgentFinish):
            return output
        if self.callback_manager.is_async:
            await self.callback_manager.on_agent_action(
                output, verbose=self.verbose, color="green"
            )
        else:
            self.callback_manager.on_agent_action(
                output, verbose=self.verbose, color="green"
            )
        # Otherwise we lookup the tool
        if output.tool in name_to_tool_map:
            tool = name_to_tool_map[output.tool]
            return_direct = tool.return_direct
            color = color_mapping[output.tool]
            llm_prefix = "" if return_direct else self.agent.llm_prefix
            # We then call the tool on the tool input to get an observation
            observation = await tool.arun(
                output.tool_input,
                verbose=self.verbose,
                color=color,
                llm_prefix=llm_prefix,
                observation_prefix=self.agent.observation_prefix,
            )
        else:
            observation = await InvalidTool().arun(
                output.tool,
                verbose=self.verbose,
                color=None,
                llm_prefix="",
                observation_prefix=self.agent.observation_prefix,
            )
            return_direct = False
        return output, observation


)
            return_direct = False
        return output, observation
    def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
        """Run text through and get agent response."""
        # Do any preparation necessary when receiving a new input.
        self.agent.prepare_for_new_call()
        # Construct a mapping of tool name to tool for easy lookup
        name_to_tool_map = {tool.name: tool for tool in self.tools}
        # We construct a mapping from each tool to a color, used for logging.
        color_mapping = get_color_mapping(
            [tool.name for tool in self.tools], excluded_colors=["green"]
        )
        intermediate_steps: List[Tuple[AgentAction, str]] = []
        # Let's start tracking the iterations the agent has gone through
        iterations = 0
        # We now enter the agent loop (until it returns something).
        while self._should_continue(iterations):
            next_step_output = self._take_next_step(
                name_to_tool_map, color_mapping, inputs, intermediate_steps
            )
            if isinstance(next_step_output, AgentFinish):
                return self._return(next_step_output, intermediate_steps)
            intermediate_steps.append(next_step_output)
            # See if tool should return directly
            tool_return = self._get_tool_return(next_step_output)
            if tool_return is not None:
                return self._return(tool_return, intermediate_steps)
            iterations += 1
        output = self.agent.return_stopped_response(
            self.early_stopping_method, intermediate_steps, **inputs
        )
        return self._return(output, intermediate_steps)
    async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]:
        """Run text through and get agent response."""


"""Run text through and get agent response."""
        # Do any preparation necessary when receiving a new input.
        self.agent.prepare_for_new_call()
        # Construct a mapping of tool name to tool for easy lookup
        name_to_tool_map = {tool.name: tool for tool in self.tools}
        # We construct a mapping from each tool to a color, used for logging.
        color_mapping = get_color_mapping(
            [tool.name for tool in self.tools], excluded_colors=["green"]
        )
        intermediate_steps: List[Tuple[AgentAction, str]] = []
        # Let's start tracking the iterations the agent has gone through
        iterations = 0
        # We now enter the agent loop (until it returns something).
        while self._should_continue(iterations):
            next_step_output = await self._atake_next_step(
                name_to_tool_map, color_mapping, inputs, intermediate_steps
            )
            if isinstance(next_step_output, AgentFinish):
                return await self._areturn(next_step_output, intermediate_steps)
            intermediate_steps.append(next_step_output)
            # See if tool should return directly
            tool_return = self._get_tool_return(next_step_output)
            if tool_return is not None:
                return await self._areturn(tool_return, intermediate_steps)
            iterations += 1
        output = self.agent.return_stopped_response(
            self.early_stopping_method, intermediate_steps, **inputs
        )
        return await self._areturn(output, intermediate_steps)
    def _get_tool_return(
        self, next_step_output: Tuple[AgentAction, str]
    ) -> Optional[AgentFinish]:
        """Check if the tool is a returning tool."""
        agent_action, observation = next_step_output


agent_action, observation = next_step_output
        name_to_tool_map = {tool.name: tool for tool in self.tools}
        # Invalid tools won't be in the map, so we return False.
        if agent_action.tool in name_to_tool_map:
            if name_to_tool_map[agent_action.tool].return_direct:
                return AgentFinish(
                    {self.agent.return_values[0]: observation},
                    "",
                )
        return None
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.agents.tools
"""Interface for tools."""
from inspect import signature
from typing import Any, Awaitable, Callable, Optional, Union
from langchain.tools.base import BaseTool
[docs]class Tool(BaseTool):
    """Tool that takes in function or coroutine directly."""
    description: str = ""
    func: Callable[[str], str]
    coroutine: Optional[Callable[[str], Awaitable[str]]] = None
    def _run(self, tool_input: str) -> str:
        """Use the tool."""
        return self.func(tool_input)
    async def _arun(self, tool_input: str) -> str:
        """Use the tool asynchronously."""
        if self.coroutine:
            return await self.coroutine(tool_input)
        raise NotImplementedError("Tool does not support async")
    # TODO: this is for backwards compatibility, remove in future
    def __init__(
        self, name: str, func: Callable[[str], str], description: str, **kwargs: Any
    ) -> None:
        """Initialize tool."""
        super(Tool, self).__init__(
            name=name, func=func, description=description, **kwargs
        )
class InvalidTool(BaseTool):
    """Tool that is run when invalid tool name is encountered by agent."""
    name = "invalid_tool"
    description = "Called when tool name is invalid."
    def _run(self, tool_name: str) -> str:
        """Use the tool."""
        return f"{tool_name} is not a valid tool, try another one."
    async def _arun(self, tool_name: str) -> str:
        """Use the tool asynchronously."""
        return f"{tool_name} is not a valid tool, try another one."


return f"{tool_name} is not a valid tool, try another one."
[docs]def tool(*args: Union[str, Callable], return_direct: bool = False) -> Callable:
    """Make tools out of functions, can be used with or without arguments.
    Requires:
        - Function must be of type (str) -> str
        - Function must have a docstring
    Examples:
        .. code-block:: python
            @tool
            def search_api(query: str) -> str:
                # Searches the API for the query.
                return
            @tool("search", return_direct=True)
            def search_api(query: str) -> str:
                # Searches the API for the query.
                return
    """
    def _make_with_name(tool_name: str) -> Callable:
        def _make_tool(func: Callable[[str], str]) -> Tool:
            assert func.__doc__, "Function must have a docstring"
            # Description example:
            #   search_api(query: str) - Searches the API for the query.
            description = f"{tool_name}{signature(func)} - {func.__doc__.strip()}"
            tool_ = Tool(
                name=tool_name,
                func=func,
                description=description,
                return_direct=return_direct,
            )
            return tool_
        return _make_tool
    if len(args) == 1 and isinstance(args[0], str):
        # if the argument is a string, then we use the string as the tool name
        # Example usage: @tool("search", return_direct=True)
        return _make_with_name(args[0])
    elif len(args) == 1 and callable(args[0]):
        # if the argument is a function, then we use the function name as the tool name


# if the argument is a function, then we use the function name as the tool name
        # Example usage: @tool
        return _make_with_name(args[0].__name__)(args[0])
    elif len(args) == 0:
        # if there are no arguments, then we use the function name as the tool name
        # Example usage: @tool(return_direct=True)
        def _partial(func: Callable[[str], str]) -> BaseTool:
            return _make_with_name(func.__name__)(func)
        return _partial
    else:
        raise ValueError("Too many arguments for tool decorator")
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.agents.loading
"""Functionality for loading agents."""
import json
from pathlib import Path
from typing import Any, List, Optional, Union
import yaml
from langchain.agents.agent import Agent
from langchain.agents.chat.base import ChatAgent
from langchain.agents.conversational.base import ConversationalAgent
from langchain.agents.conversational_chat.base import ConversationalChatAgent
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.agents.react.base import ReActDocstoreAgent
from langchain.agents.self_ask_with_search.base import SelfAskWithSearchAgent
from langchain.agents.tools import Tool
from langchain.chains.loading import load_chain, load_chain_from_config
from langchain.llms.base import BaseLLM
from langchain.utilities.loading import try_load_from_hub
AGENT_TO_CLASS = {
    "zero-shot-react-description": ZeroShotAgent,
    "react-docstore": ReActDocstoreAgent,
    "self-ask-with-search": SelfAskWithSearchAgent,
    "conversational-react-description": ConversationalAgent,
    "chat-zero-shot-react-description": ChatAgent,
    "chat-conversational-react-description": ConversationalChatAgent,
}
URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/agents/"
def _load_agent_from_tools(
    config: dict, llm: BaseLLM, tools: List[Tool], **kwargs: Any
) -> Agent:
    config_type = config.pop("_type")
    if config_type not in AGENT_TO_CLASS:
        raise ValueError(f"Loading {config_type} agent not supported")
    if config_type not in AGENT_TO_CLASS:
        raise ValueError(f"Loading {config_type} agent not supported")


raise ValueError(f"Loading {config_type} agent not supported")
    agent_cls = AGENT_TO_CLASS[config_type]
    combined_config = {**config, **kwargs}
    return agent_cls.from_llm_and_tools(llm, tools, **combined_config)
def load_agent_from_config(
    config: dict,
    llm: Optional[BaseLLM] = None,
    tools: Optional[List[Tool]] = None,
    **kwargs: Any,
) -> Agent:
    """Load agent from Config Dict."""
    if "_type" not in config:
        raise ValueError("Must specify an agent Type in config")
    load_from_tools = config.pop("load_from_llm_and_tools", False)
    if load_from_tools:
        if llm is None:
            raise ValueError(
                "If `load_from_llm_and_tools` is set to True, "
                "then LLM must be provided"
            )
        if tools is None:
            raise ValueError(
                "If `load_from_llm_and_tools` is set to True, "
                "then tools must be provided"
            )
        return _load_agent_from_tools(config, llm, tools, **kwargs)
    config_type = config.pop("_type")
    if config_type not in AGENT_TO_CLASS:
        raise ValueError(f"Loading {config_type} agent not supported")
    agent_cls = AGENT_TO_CLASS[config_type]
    if "llm_chain" in config:
        config["llm_chain"] = load_chain_from_config(config.pop("llm_chain"))
    elif "llm_chain_path" in config:
        config["llm_chain"] = load_chain(config.pop("llm_chain_path"))
    else:


else:
        raise ValueError("One of `llm_chain` and `llm_chain_path` should be specified.")
    combined_config = {**config, **kwargs}
    return agent_cls(**combined_config)  # type: ignore
[docs]def load_agent(path: Union[str, Path], **kwargs: Any) -> Agent:
    """Unified method for loading a agent from LangChainHub or local fs."""
    if hub_result := try_load_from_hub(
        path, _load_agent_from_file, "agents", {"json", "yaml"}
    ):
        return hub_result
    else:
        return _load_agent_from_file(path, **kwargs)
def _load_agent_from_file(file: Union[str, Path], **kwargs: Any) -> Agent:
    """Load agent from file."""
    # Convert file to Path object.
    if isinstance(file, str):
        file_path = Path(file)
    else:
        file_path = file
    # Load from either json or yaml.
    if file_path.suffix == ".json":
        with open(file_path) as f:
            config = json.load(f)
    elif file_path.suffix == ".yaml":
        with open(file_path, "r") as f:
            config = yaml.safe_load(f)
    else:
        raise ValueError("File type must be json or yaml")
    # Load the agent from the config now.
    return load_agent_from_config(config, **kwargs)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.agents.load_tools
# flake8: noqa
"""Load tools."""
from typing import Any, List, Optional
from langchain.agents.tools import Tool
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.api import news_docs, open_meteo_docs, tmdb_docs, podcast_docs
from langchain.chains.api.base import APIChain
from langchain.chains.llm_math.base import LLMMathChain
from langchain.chains.pal.base import PALChain
from langchain.llms.base import BaseLLM
from langchain.requests import RequestsWrapper
from langchain.tools.base import BaseTool
from langchain.tools.bing_search.tool import BingSearchRun
from langchain.tools.google_search.tool import GoogleSearchResults, GoogleSearchRun
from langchain.tools.python.tool import PythonREPLTool
from langchain.tools.requests.tool import RequestsGetTool
from langchain.tools.wikipedia.tool import WikipediaQueryRun
from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
from langchain.utilities.bash import BashProcess
from langchain.utilities.bing_search import BingSearchAPIWrapper
from langchain.utilities.google_search import GoogleSearchAPIWrapper
from langchain.utilities.google_serper import GoogleSerperAPIWrapper
from langchain.utilities.searx_search import SearxSearchWrapper
from langchain.utilities.serpapi import SerpAPIWrapper
from langchain.utilities.wikipedia import WikipediaAPIWrapper
from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
def _get_python_repl() -> BaseTool:
    return PythonREPLTool()
def _get_requests() -> BaseTool:
    return RequestsGetTool(requests_wrapper=RequestsWrapper())
def _get_terminal() -> BaseTool:
    return Tool(
        name="Terminal",


def _get_terminal() -> BaseTool:
    return Tool(
        name="Terminal",
        description="Executes commands in a terminal. Input should be valid commands, and the output will be any output from running that command.",
        func=BashProcess().run,
    )
_BASE_TOOLS = {
    "python_repl": _get_python_repl,
    "requests": _get_requests,
    "terminal": _get_terminal,
}
def _get_pal_math(llm: BaseLLM) -> BaseTool:
    return Tool(
        name="PAL-MATH",
        description="A language model that is really good at solving complex word math problems. Input should be a fully worded hard word math problem.",
        func=PALChain.from_math_prompt(llm).run,
    )
def _get_pal_colored_objects(llm: BaseLLM) -> BaseTool:
    return Tool(
        name="PAL-COLOR-OBJ",
        description="A language model that is really good at reasoning about position and the color attributes of objects. Input should be a fully worded hard reasoning problem. Make sure to include all information about the objects AND the final question you want to answer.",
        func=PALChain.from_colored_object_prompt(llm).run,
    )
def _get_llm_math(llm: BaseLLM) -> BaseTool:
    return Tool(
        name="Calculator",
        description="Useful for when you need to answer questions about math.",
        func=LLMMathChain(llm=llm, callback_manager=llm.callback_manager).run,
        coroutine=LLMMathChain(llm=llm, callback_manager=llm.callback_manager).arun,
    )
def _get_open_meteo_api(llm: BaseLLM) -> BaseTool:


def _get_open_meteo_api(llm: BaseLLM) -> BaseTool:
    chain = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS)
    return Tool(
        name="Open Meteo API",
        description="Useful for when you want to get weather information from the OpenMeteo API. The input should be a question in natural language that this API can answer.",
        func=chain.run,
    )
_LLM_TOOLS = {
    "pal-math": _get_pal_math,
    "pal-colored-objects": _get_pal_colored_objects,
    "llm-math": _get_llm_math,
    "open-meteo-api": _get_open_meteo_api,
}
def _get_news_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
    news_api_key = kwargs["news_api_key"]
    chain = APIChain.from_llm_and_api_docs(
        llm, news_docs.NEWS_DOCS, headers={"X-Api-Key": news_api_key}
    )
    return Tool(
        name="News API",
        description="Use this when you want to get information about the top headlines of current news stories. The input should be a question in natural language that this API can answer.",
        func=chain.run,
    )
def _get_tmdb_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
    tmdb_bearer_token = kwargs["tmdb_bearer_token"]
    chain = APIChain.from_llm_and_api_docs(
        llm,
        tmdb_docs.TMDB_DOCS,
        headers={"Authorization": f"Bearer {tmdb_bearer_token}"},
    )
    return Tool(


)
    return Tool(
        name="TMDB API",
        description="Useful for when you want to get information from The Movie Database. The input should be a question in natural language that this API can answer.",
        func=chain.run,
    )
def _get_podcast_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
    listen_api_key = kwargs["listen_api_key"]
    chain = APIChain.from_llm_and_api_docs(
        llm,
        podcast_docs.PODCAST_DOCS,
        headers={"X-ListenAPI-Key": listen_api_key},
    )
    return Tool(
        name="Podcast API",
        description="Use the Listen Notes Podcast API to search all podcasts or episodes. The input should be a question in natural language that this API can answer.",
        func=chain.run,
    )
def _get_wolfram_alpha(**kwargs: Any) -> BaseTool:
    return WolframAlphaQueryRun(api_wrapper=WolframAlphaAPIWrapper(**kwargs))
def _get_google_search(**kwargs: Any) -> BaseTool:
    return GoogleSearchRun(api_wrapper=GoogleSearchAPIWrapper(**kwargs))
def _get_wikipedia(**kwargs: Any) -> BaseTool:
    return WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(**kwargs))
def _get_google_serper(**kwargs: Any) -> BaseTool:
    return Tool(
        name="Serper Search",
        func=GoogleSerperAPIWrapper(**kwargs).run,
        description="A low-cost Google Search API. Useful for when you need to answer questions about current events. Input should be a search query.",
    )
def _get_google_search_results_json(**kwargs: Any) -> BaseTool:
    return GoogleSearchResults(api_wrapper=GoogleSearchAPIWrapper(**kwargs))


return GoogleSearchResults(api_wrapper=GoogleSearchAPIWrapper(**kwargs))
def _get_serpapi(**kwargs: Any) -> BaseTool:
    return Tool(
        name="Search",
        description="A search engine. Useful for when you need to answer questions about current events. Input should be a search query.",
        func=SerpAPIWrapper(**kwargs).run,
        coroutine=SerpAPIWrapper(**kwargs).arun,
    )
def _get_searx_search(**kwargs: Any) -> BaseTool:
    return Tool(
        name="SearX Search",
        description="A meta search engine. Useful for when you need to answer questions about current events. Input should be a search query.",
        func=SearxSearchWrapper(**kwargs).run,
    )
def _get_bing_search(**kwargs: Any) -> BaseTool:
    return BingSearchRun(api_wrapper=BingSearchAPIWrapper(**kwargs))
_EXTRA_LLM_TOOLS = {
    "news-api": (_get_news_api, ["news_api_key"]),
    "tmdb-api": (_get_tmdb_api, ["tmdb_bearer_token"]),
    "podcast-api": (_get_podcast_api, ["listen_api_key"]),
}
_EXTRA_OPTIONAL_TOOLS = {
    "wolfram-alpha": (_get_wolfram_alpha, ["wolfram_alpha_appid"]),
    "google-search": (_get_google_search, ["google_api_key", "google_cse_id"]),
    "google-search-results-json": (
        _get_google_search_results_json,
        ["google_api_key", "google_cse_id", "num_results"],
    ),
    "bing-search": (_get_bing_search, ["bing_subscription_key", "bing_search_url"]),
    "google-serper": (_get_google_serper, ["serper_api_key"]),


"google-serper": (_get_google_serper, ["serper_api_key"]),
    "serpapi": (_get_serpapi, ["serpapi_api_key", "aiosession"]),
    "searx-search": (_get_searx_search, ["searx_host"]),
    "wikipedia": (_get_wikipedia, ["top_k_results"]),
}
[docs]def load_tools(
    tool_names: List[str],
    llm: Optional[BaseLLM] = None,
    callback_manager: Optional[BaseCallbackManager] = None,
    **kwargs: Any,
) -> List[BaseTool]:
    """Load tools based on their name.
    Args:
        tool_names: name of tools to load.
        llm: Optional language model, may be needed to initialize certain tools.
        callback_manager: Optional callback manager. If not provided, default global callback manager will be used.
    Returns:
        List of tools.
    """
    tools = []
    for name in tool_names:
        if name in _BASE_TOOLS:
            tools.append(_BASE_TOOLS[name]())
        elif name in _LLM_TOOLS:
            if llm is None:
                raise ValueError(f"Tool {name} requires an LLM to be provided")
            tool = _LLM_TOOLS[name](llm)
            if callback_manager is not None:
                tool.callback_manager = callback_manager
            tools.append(tool)
        elif name in _EXTRA_LLM_TOOLS:
            if llm is None:
                raise ValueError(f"Tool {name} requires an LLM to be provided")
            _get_llm_tool_func, extra_keys = _EXTRA_LLM_TOOLS[name]
            missing_keys = set(extra_keys).difference(kwargs)


missing_keys = set(extra_keys).difference(kwargs)
            if missing_keys:
                raise ValueError(
                    f"Tool {name} requires some parameters that were not "
                    f"provided: {missing_keys}"
                )
            sub_kwargs = {k: kwargs[k] for k in extra_keys}
            tool = _get_llm_tool_func(llm=llm, **sub_kwargs)
            if callback_manager is not None:
                tool.callback_manager = callback_manager
            tools.append(tool)
        elif name in _EXTRA_OPTIONAL_TOOLS:
            _get_tool_func, extra_keys = _EXTRA_OPTIONAL_TOOLS[name]
            sub_kwargs = {k: kwargs[k] for k in extra_keys if k in kwargs}
            tool = _get_tool_func(**sub_kwargs)
            if callback_manager is not None:
                tool.callback_manager = callback_manager
            tools.append(tool)
        else:
            raise ValueError(f"Got unknown tool {name}")
    return tools
[docs]def get_all_tool_names() -> List[str]:
    """Get a list of all possible tool names."""
    return (
        list(_BASE_TOOLS)
        + list(_EXTRA_OPTIONAL_TOOLS)
        + list(_EXTRA_LLM_TOOLS)
        + list(_LLM_TOOLS)
    )
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.agents.self_ask_with_search.base
"""Chain that does self ask with search."""
from typing import Any, Optional, Sequence, Tuple, Union
from langchain.agents.agent import Agent, AgentExecutor
from langchain.agents.self_ask_with_search.prompt import PROMPT
from langchain.agents.tools import Tool
from langchain.llms.base import BaseLLM
from langchain.prompts.base import BasePromptTemplate
from langchain.tools.base import BaseTool
from langchain.utilities.google_serper import GoogleSerperAPIWrapper
from langchain.utilities.serpapi import SerpAPIWrapper
class SelfAskWithSearchAgent(Agent):
    """Agent for the self-ask-with-search paper."""
    @property
    def _agent_type(self) -> str:
        """Return Identifier of agent type."""
        return "self-ask-with-search"
    @classmethod
    def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
        """Prompt does not depend on tools."""
        return PROMPT
    @classmethod
    def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
        if len(tools) != 1:
            raise ValueError(f"Exactly one tool must be specified, but got {tools}")
        tool_names = {tool.name for tool in tools}
        if tool_names != {"Intermediate Answer"}:
            raise ValueError(
                f"Tool name should be Intermediate Answer, got {tool_names}"
            )
    def _extract_tool_and_input(self, text: str) -> Optional[Tuple[str, str]]:
        followup = "Follow up:"
        last_line = text.split("\n")[-1]
        if followup not in last_line:
            finish_string = "So the final answer is: "
            if finish_string not in last_line:


if finish_string not in last_line:
                return None
            return "Final Answer", last_line[len(finish_string) :]
        after_colon = text.split(":")[-1]
        if " " == after_colon[0]:
            after_colon = after_colon[1:]
        return "Intermediate Answer", after_colon
    def _fix_text(self, text: str) -> str:
        return f"{text}\nSo the final answer is:"
    @property
    def observation_prefix(self) -> str:
        """Prefix to append the observation with."""
        return "Intermediate answer: "
    @property
    def llm_prefix(self) -> str:
        """Prefix to append the LLM call with."""
        return ""
    @property
    def starter_string(self) -> str:
        """Put this string after user input but before first LLM call."""
        return "Are follow up questions needed here:"
[docs]class SelfAskWithSearchChain(AgentExecutor):
    """Chain that does self ask with search.
    Example:
        .. code-block:: python
            from langchain import SelfAskWithSearchChain, OpenAI, GoogleSerperAPIWrapper
            search_chain = GoogleSerperAPIWrapper()
            self_ask = SelfAskWithSearchChain(llm=OpenAI(), search_chain=search_chain)
    """
    def __init__(
        self,
        llm: BaseLLM,
        search_chain: Union[GoogleSerperAPIWrapper, SerpAPIWrapper],
        **kwargs: Any,
    ):
        """Initialize with just an LLM and a search chain."""
        search_tool = Tool(
            name="Intermediate Answer", func=search_chain.run, description="Search"
        )


name="Intermediate Answer", func=search_chain.run, description="Search"
        )
        agent = SelfAskWithSearchAgent.from_llm_and_tools(llm, [search_tool])
        super().__init__(agent=agent, tools=[search_tool], **kwargs)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.agents.mrkl.base
"""Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf."""
from __future__ import annotations
import re
from typing import Any, Callable, List, NamedTuple, Optional, Sequence, Tuple
from langchain.agents.agent import Agent, AgentExecutor
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
from langchain.agents.tools import Tool
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains import LLMChain
from langchain.llms.base import BaseLLM
from langchain.prompts import PromptTemplate
from langchain.tools.base import BaseTool
FINAL_ANSWER_ACTION = "Final Answer:"
class ChainConfig(NamedTuple):
    """Configuration for chain to use in MRKL system.
    Args:
        action_name: Name of the action.
        action: Action function to call.
        action_description: Description of the action.
    """
    action_name: str
    action: Callable
    action_description: str
def get_action_and_input(llm_output: str) -> Tuple[str, str]:
    """Parse out the action and input from the LLM output.
    Note: if you're specifying a custom prompt for the ZeroShotAgent,
    you will need to ensure that it meets the following Regex requirements.
    The string starting with "Action:" and the following string starting
    with "Action Input:" should be separated by a newline.
    """
    if FINAL_ANSWER_ACTION in llm_output:
        return "Final Answer", llm_output.split(FINAL_ANSWER_ACTION)[-1].strip()
    regex = r"Action: (.*?)[\n]*Action Input: (.*)"


regex = r"Action: (.*?)[\n]*Action Input: (.*)"
    match = re.search(regex, llm_output, re.DOTALL)
    if not match:
        raise ValueError(f"Could not parse LLM output: `{llm_output}`")
    action = match.group(1).strip()
    action_input = match.group(2)
    return action, action_input.strip(" ").strip('"')
[docs]class ZeroShotAgent(Agent):
    """Agent for the MRKL chain."""
    @property
    def _agent_type(self) -> str:
        """Return Identifier of agent type."""
        return "zero-shot-react-description"
    @property
    def observation_prefix(self) -> str:
        """Prefix to append the observation with."""
        return "Observation: "
    @property
    def llm_prefix(self) -> str:
        """Prefix to append the llm call with."""
        return "Thought:"
[docs]    @classmethod
    def create_prompt(
        cls,
        tools: Sequence[BaseTool],
        prefix: str = PREFIX,
        suffix: str = SUFFIX,
        format_instructions: str = FORMAT_INSTRUCTIONS,
        input_variables: Optional[List[str]] = 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.
            input_variables: List of input variables the final prompt will expect.
        Returns:
            A PromptTemplate with the template assembled from the pieces here.
        """


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)
        template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
        if input_variables is None:
            input_variables = ["input", "agent_scratchpad"]
        return PromptTemplate(template=template, input_variables=input_variables)
[docs]    @classmethod
    def from_llm_and_tools(
        cls,
        llm: BaseLLM,
        tools: Sequence[BaseTool],
        callback_manager: Optional[BaseCallbackManager] = None,
        prefix: str = PREFIX,
        suffix: str = SUFFIX,
        format_instructions: str = FORMAT_INSTRUCTIONS,
        input_variables: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> Agent:
        """Construct an agent from an LLM and tools."""
        cls._validate_tools(tools)
        prompt = cls.create_prompt(
            tools,
            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]
        return cls(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
    @classmethod
    def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
        for tool in tools:


for tool in tools:
            if tool.description is None:
                raise ValueError(
                    f"Got a tool {tool.name} without a description. For this agent, "
                    f"a description must always be provided."
                )
    def _extract_tool_and_input(self, text: str) -> Optional[Tuple[str, str]]:
        return get_action_and_input(text)
[docs]class MRKLChain(AgentExecutor):
    """Chain that implements the MRKL system.
    Example:
        .. code-block:: python
            from langchain import OpenAI, MRKLChain
            from langchain.chains.mrkl.base import ChainConfig
            llm = OpenAI(temperature=0)
            prompt = PromptTemplate(...)
            chains = [...]
            mrkl = MRKLChain.from_chains(llm=llm, prompt=prompt)
    """
[docs]    @classmethod
    def from_chains(
        cls, llm: BaseLLM, chains: List[ChainConfig], **kwargs: Any
    ) -> AgentExecutor:
        """User friendly way to initialize the MRKL chain.
        This is intended to be an easy way to get up and running with the
        MRKL chain.
        Args:
            llm: The LLM to use as the agent LLM.
            chains: The chains the MRKL system has access to.
            **kwargs: parameters to be passed to initialization.
        Returns:
            An initialized MRKL chain.
        Example:
            .. code-block:: python
                from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, MRKLChain
                from langchain.chains.mrkl.base import ChainConfig
                llm = OpenAI(temperature=0)
                search = SerpAPIWrapper()


llm = OpenAI(temperature=0)
                search = SerpAPIWrapper()
                llm_math_chain = LLMMathChain(llm=llm)
                chains = [
                    ChainConfig(
                        action_name = "Search",
                        action=search.search,
                        action_description="useful for searching"
                    ),
                    ChainConfig(
                        action_name="Calculator",
                        action=llm_math_chain.run,
                        action_description="useful for doing math"
                    )
                ]
                mrkl = MRKLChain.from_chains(llm, chains)
        """
        tools = [
            Tool(
                name=c.action_name,
                func=c.action,
                description=c.action_description,
            )
            for c in chains
        ]
        agent = ZeroShotAgent.from_llm_and_tools(llm, tools)
        return cls(agent=agent, tools=tools, **kwargs)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.agents.agent_toolkits.openapi.base
"""OpenAPI spec agent."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.openapi.prompt import (
    OPENAPI_PREFIX,
    OPENAPI_SUFFIX,
)
from langchain.agents.agent_toolkits.openapi.toolkit import OpenAPIToolkit
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM
[docs]def create_openapi_agent(
    llm: BaseLLM,
    toolkit: OpenAPIToolkit,
    callback_manager: Optional[BaseCallbackManager] = None,
    prefix: str = OPENAPI_PREFIX,
    suffix: str = OPENAPI_SUFFIX,
    format_instructions: str = FORMAT_INSTRUCTIONS,
    input_variables: Optional[List[str]] = None,
    verbose: bool = False,
    **kwargs: Any,
) -> AgentExecutor:
    """Construct a json agent from an LLM and tools."""
    tools = toolkit.get_tools()
    prompt = ZeroShotAgent.create_prompt(
        tools,
        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]
    agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
    return AgentExecutor.from_agent_and_tools(


return AgentExecutor.from_agent_and_tools(
        agent=agent, tools=toolkit.get_tools(), verbose=verbose
    )
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.agents.agent_toolkits.json.base
"""Json agent."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM
[docs]def create_json_agent(
    llm: BaseLLM,
    toolkit: JsonToolkit,
    callback_manager: Optional[BaseCallbackManager] = None,
    prefix: str = JSON_PREFIX,
    suffix: str = JSON_SUFFIX,
    format_instructions: str = FORMAT_INSTRUCTIONS,
    input_variables: Optional[List[str]] = None,
    verbose: bool = False,
    **kwargs: Any,
) -> AgentExecutor:
    """Construct a json agent from an LLM and tools."""
    tools = toolkit.get_tools()
    prompt = ZeroShotAgent.create_prompt(
        tools,
        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]
    agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
    return AgentExecutor.from_agent_and_tools(
        agent=agent, tools=toolkit.get_tools(), verbose=verbose
    )
By Harrison Chase


)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.agents.agent_toolkits.csv.base
"""Agent for working with csvs."""
from typing import Any, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain.llms.base import BaseLLM
[docs]def create_csv_agent(
    llm: BaseLLM, path: str, pandas_kwargs: Optional[dict] = None, **kwargs: Any
) -> AgentExecutor:
    """Create csv agent by loading to a dataframe and using pandas agent."""
    import pandas as pd
    _kwargs = pandas_kwargs or {}
    df = pd.read_csv(path, **_kwargs)
    return create_pandas_dataframe_agent(llm, df, **kwargs)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.agents.agent_toolkits.pandas.base
"""Agent for working with pandas objects."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.prompt import PREFIX, SUFFIX
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM
from langchain.tools.python.tool import PythonAstREPLTool
[docs]def create_pandas_dataframe_agent(
    llm: BaseLLM,
    df: Any,
    callback_manager: Optional[BaseCallbackManager] = None,
    prefix: str = PREFIX,
    suffix: str = SUFFIX,
    input_variables: Optional[List[str]] = None,
    verbose: bool = False,
    **kwargs: Any,
) -> AgentExecutor:
    """Construct a pandas agent from an LLM and dataframe."""
    import pandas as pd
    if not isinstance(df, pd.DataFrame):
        raise ValueError(f"Expected pandas object, got {type(df)}")
    if input_variables is None:
        input_variables = ["df", "input", "agent_scratchpad"]
    tools = [PythonAstREPLTool(locals={"df": df})]
    prompt = ZeroShotAgent.create_prompt(
        tools, prefix=prefix, suffix=suffix, input_variables=input_variables
    )
    partial_prompt = prompt.partial(df=str(df.head()))
    llm_chain = LLMChain(
        llm=llm,
        prompt=partial_prompt,
        callback_manager=callback_manager,
    )
    tool_names = [tool.name for tool in tools]


)
    tool_names = [tool.name for tool in tools]
    agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
    return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=verbose)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.agents.agent_toolkits.vectorstore.base
"""VectorStore agent."""
from typing import Any, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.vectorstore.prompt import PREFIX, ROUTER_PREFIX
from langchain.agents.agent_toolkits.vectorstore.toolkit import (
    VectorStoreRouterToolkit,
    VectorStoreToolkit,
)
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM
[docs]def create_vectorstore_agent(
    llm: BaseLLM,
    toolkit: VectorStoreToolkit,
    callback_manager: Optional[BaseCallbackManager] = None,
    prefix: str = PREFIX,
    verbose: bool = False,
    **kwargs: Any,
) -> AgentExecutor:
    """Construct a vectorstore agent from an LLM and tools."""
    tools = toolkit.get_tools()
    prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
    llm_chain = LLMChain(
        llm=llm,
        prompt=prompt,
        callback_manager=callback_manager,
    )
    tool_names = [tool.name for tool in tools]
    agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
    return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=verbose)
[docs]def create_vectorstore_router_agent(
    llm: BaseLLM,
    toolkit: VectorStoreRouterToolkit,
    callback_manager: Optional[BaseCallbackManager] = None,
    prefix: str = ROUTER_PREFIX,
    verbose: bool = False,


prefix: str = ROUTER_PREFIX,
    verbose: bool = False,
    **kwargs: Any,
) -> AgentExecutor:
    """Construct a vectorstore router agent from an LLM and tools."""
    tools = toolkit.get_tools()
    prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
    llm_chain = LLMChain(
        llm=llm,
        prompt=prompt,
        callback_manager=callback_manager,
    )
    tool_names = [tool.name for tool in tools]
    agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
    return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=verbose)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.agents.agent_toolkits.sql.base
"""SQL agent."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX
from langchain.agents.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM
[docs]def create_sql_agent(
    llm: BaseLLM,
    toolkit: SQLDatabaseToolkit,
    callback_manager: Optional[BaseCallbackManager] = None,
    prefix: str = SQL_PREFIX,
    suffix: str = SQL_SUFFIX,
    format_instructions: str = FORMAT_INSTRUCTIONS,
    input_variables: Optional[List[str]] = None,
    top_k: int = 10,
    verbose: bool = False,
    **kwargs: Any,
) -> AgentExecutor:
    """Construct a sql agent from an LLM and tools."""
    tools = toolkit.get_tools()
    prefix = prefix.format(dialect=toolkit.dialect, top_k=top_k)
    prompt = ZeroShotAgent.create_prompt(
        tools,
        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]
    agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)


Source code for langchain.agents.conversational.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
import re
from typing import Any, List, Optional, Sequence, Tuple
from langchain.agents.agent import Agent
from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains import LLMChain
from langchain.llms import BaseLLM
from langchain.prompts import PromptTemplate
from langchain.tools.base import BaseTool
[docs]class ConversationalAgent(Agent):
    """An agent designed to hold a conversation in addition to using tools."""
    ai_prefix: str = "AI"
    @property
    def _agent_type(self) -> str:
        """Return Identifier of agent type."""
        return "conversational-react-description"
    @property
    def observation_prefix(self) -> str:
        """Prefix to append the observation with."""
        return "Observation: "
    @property
    def llm_prefix(self) -> str:
        """Prefix to append the llm call with."""
        return "Thought:"
[docs]    @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: Optional[List[str]] = 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.


prompt.
            prefix: String to put before the list of tools.
            suffix: String to put after the list of 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.
        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 = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
        if input_variables is None:
            input_variables = ["input", "chat_history", "agent_scratchpad"]
        return PromptTemplate(template=template, input_variables=input_variables)
    @property
    def finish_tool_name(self) -> str:
        """Name of the tool to use to finish the chain."""
        return self.ai_prefix
    def _extract_tool_and_input(self, llm_output: str) -> Optional[Tuple[str, str]]:
        if f"{self.ai_prefix}:" in llm_output:
            return self.ai_prefix, llm_output.split(f"{self.ai_prefix}:")[-1].strip()
        regex = r"Action: (.*?)[\n]*Action Input: (.*)"
        match = re.search(regex, llm_output)
        if not match:
            raise ValueError(f"Could not parse LLM output: `{llm_output}`")
        action = match.group(1)


action = match.group(1)
        action_input = match.group(2)
        return action.strip(), action_input.strip(" ").strip('"')
[docs]    @classmethod
    def from_llm_and_tools(
        cls,
        llm: BaseLLM,
        tools: Sequence[BaseTool],
        callback_manager: Optional[BaseCallbackManager] = None,
        prefix: str = PREFIX,
        suffix: str = SUFFIX,
        format_instructions: str = FORMAT_INSTRUCTIONS,
        ai_prefix: str = "AI",
        human_prefix: str = "Human",
        input_variables: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> Agent:
        """Construct an agent from an LLM and tools."""
        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]
        return cls(
            llm_chain=llm_chain, allowed_tools=tool_names, ai_prefix=ai_prefix, **kwargs
        )
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.agents.react.base
"""Chain that implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf."""
import re
from typing import Any, List, Optional, Sequence, Tuple
from pydantic import BaseModel
from langchain.agents.agent import Agent, AgentExecutor
from langchain.agents.react.textworld_prompt import TEXTWORLD_PROMPT
from langchain.agents.react.wiki_prompt import WIKI_PROMPT
from langchain.agents.tools import Tool
from langchain.docstore.base import Docstore
from langchain.docstore.document import Document
from langchain.llms.base import BaseLLM
from langchain.prompts.base import BasePromptTemplate
from langchain.tools.base import BaseTool
class ReActDocstoreAgent(Agent, BaseModel):
    """Agent for the ReAct chain."""
    @property
    def _agent_type(self) -> str:
        """Return Identifier of agent type."""
        return "react-docstore"
    @classmethod
    def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
        """Return default prompt."""
        return WIKI_PROMPT
    i: int = 1
    @classmethod
    def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
        if len(tools) != 2:
            raise ValueError(f"Exactly two tools must be specified, but got {tools}")
        tool_names = {tool.name for tool in tools}
        if tool_names != {"Lookup", "Search"}:
            raise ValueError(
                f"Tool names should be Lookup and Search, got {tool_names}"
            )
    def _prepare_for_new_call(self) -> None:
        self.i = 1
    def _fix_text(self, text: str) -> str:


def _fix_text(self, text: str) -> str:
        return text + f"\nAction {self.i}:"
    def _extract_tool_and_input(self, text: str) -> Optional[Tuple[str, str]]:
        action_prefix = f"Action {self.i}: "
        if not text.split("\n")[-1].startswith(action_prefix):
            return None
        self.i += 1
        action_block = text.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:
            raise ValueError(f"Could not parse action directive: {action_str}")
        return re_matches.group(1), re_matches.group(2)
    @property
    def finish_tool_name(self) -> str:
        """Name of the tool of when to finish the chain."""
        return "Finish"
    @property
    def observation_prefix(self) -> str:
        """Prefix to append the observation with."""
        return f"Observation {self.i - 1}: "
    @property
    def _stop(self) -> List[str]:
        return [f"\nObservation {self.i}:"]
    @property
    def llm_prefix(self) -> str:
        """Prefix to append the LLM call with."""
        return f"Thought {self.i}:"
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: Optional[Document] = None


self.docstore = docstore
        self.document: Optional[Document] = None
    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.document.summary
        else:
            self.document = None
            return result
    def lookup(self, term: str) -> str:
        """Lookup a term in document (if saved)."""
        if self.document is None:
            raise ValueError("Cannot lookup without a successful search first")
        return self.document.lookup(term)
[docs]class ReActTextWorldAgent(ReActDocstoreAgent, BaseModel):
    """Agent for the ReAct TextWorld chain."""
[docs]    @classmethod
    def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
        """Return default prompt."""
        return TEXTWORLD_PROMPT
    @classmethod
    def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
        if len(tools) != 1:
            raise ValueError(f"Exactly one tool must be specified, but got {tools}")
        tool_names = {tool.name for tool in tools}
        if tool_names != {"Play"}:
            raise ValueError(f"Tool name should be Play, got {tool_names}")
[docs]class ReActChain(AgentExecutor):
    """Chain that implements the ReAct paper.
    Example:
        .. code-block:: python
            from langchain import ReActChain, OpenAI
            react = ReAct(llm=OpenAI())
    """
    def __init__(self, llm: BaseLLM, 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)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.utilities.searx_search
"""Utility for using SearxNG meta search API.
SearxNG is a privacy-friendly free metasearch engine that aggregates results from
`multiple search engines
<https://docs.searxng.org/admin/engines/configured_engines.html>`_ and databases and
supports the `OpenSearch
<https://github.com/dewitt/opensearch/blob/master/opensearch-1-1-draft-6.md>`_
specification.
More detailes on the installtion instructions `here. <../../ecosystem/searx.html>`_
For the search API refer to https://docs.searxng.org/dev/search_api.html
Quick Start
-----------
In order to use this tool you need to provide the searx host. This can be done
by passing the named parameter :attr:`searx_host <SearxSearchWrapper.searx_host>`
or exporting the environment variable SEARX_HOST.
Note: this is the only required parameter.
Then create a searx search instance like this:
    .. code-block:: python
        from langchain.utilities import SearxSearchWrapper
        # when the host starts with `http` SSL is disabled and the connection
        # is assumed to be on a private network
        searx_host='http://self.hosted'
        search = SearxSearchWrapper(searx_host=searx_host)
You can now use the ``search`` instance to query the searx API.
Searching
---------
Use the :meth:`run() <SearxSearchWrapper.run>` and
:meth:`results() <SearxSearchWrapper.results>` methods to query the searx API.
Other methods are are available for convenience.


Other methods are are available for convenience.
:class:`SearxResults` is a convenience wrapper around the raw json result.
Example usage of the ``run`` method to make a search:
    .. code-block:: python
        s.run(query="what is the best search engine?")
Engine Parameters
-----------------
You can pass any `accepted searx search API
<https://docs.searxng.org/dev/search_api.html>`_ parameters to the
:py:class:`SearxSearchWrapper` instance.
In the following example we are using the
:attr:`engines <SearxSearchWrapper.engines>` and the ``language`` parameters:
    .. code-block:: python
        # assuming the searx host is set as above or exported as an env variable
        s = SearxSearchWrapper(engines=['google', 'bing'],
                            language='es')
Search Tips
-----------
Searx offers a special
`search syntax <https://docs.searxng.org/user/index.html#search-syntax>`_
that can also be used instead of passing engine parameters.
For example the following query:
    .. code-block:: python
        s = SearxSearchWrapper("langchain library", engines=['github'])
        # can also be written as:
        s = SearxSearchWrapper("langchain library !github")
        # or even:
        s = SearxSearchWrapper("langchain library !gh")
In some situations you might want to pass an extra string to the search query.
For example when the `run()` method is called by an agent. The search suffix can
also be used as a way to pass extra parameters to searx or the underlying search
engines.
    .. code-block:: python
        # select the github engine and pass the search suffix


.. code-block:: python
        # select the github engine and pass the search suffix
        s = SearchWrapper("langchain library", query_suffix="!gh")
        s = SearchWrapper("langchain library")
        # select github the conventional google search syntax
        s.run("large language models", query_suffix="site:github.com")
*NOTE*: A search suffix can be defined on both the instance and the method level.
The resulting query will be the concatenation of the two with the former taking
precedence.
See `SearxNG Configured Engines
<https://docs.searxng.org/admin/engines/configured_engines.html>`_ and
`SearxNG Search Syntax <https://docs.searxng.org/user/index.html#id1>`_
for more details.
Notes
-----
This wrapper is based on the SearxNG fork https://github.com/searxng/searxng which is
better maintained than the original Searx project and offers more features.
Public searxNG instances often use a rate limiter for API usage, so you might want to
use a self hosted instance and disable the rate limiter.
If you are self-hosting an instance you can customize the rate limiter for your
own network as described `here <https://github.com/searxng/searxng/pull/2129>`_.
For a list of public SearxNG instances see https://searx.space/
"""
import json
from typing import Any, Dict, List, Optional
import requests
from pydantic import BaseModel, Extra, Field, PrivateAttr, root_validator, validator
from langchain.utils import get_from_dict_or_env
def _get_default_params() -> dict:
    return {"language": "en", "format": "json"}


return {"language": "en", "format": "json"}
[docs]class SearxResults(dict):
    """Dict like wrapper around search api results."""
    _data = ""
    def __init__(self, data: str):
        """Take a raw result from Searx and make it into a dict like object."""
        json_data = json.loads(data)
        super().__init__(json_data)
        self.__dict__ = self
    def __str__(self) -> str:
        """Text representation of searx result."""
        return self._data
    @property
    def results(self) -> Any:
        """Silence mypy for accessing this field.
        :meta private:
        """
        return self.get("results")
    @property
    def answers(self) -> Any:
        """Helper accessor on the json result."""
        return self.get("answers")
[docs]class SearxSearchWrapper(BaseModel):
    """Wrapper for Searx API.
    To use you need to provide the searx host by passing the named parameter
    ``searx_host`` or exporting the environment variable ``SEARX_HOST``.
    In some situations you might want to disable SSL verification, for example
    if you are running searx locally. You can do this by passing the named parameter
    ``unsecure``. You can also pass the host url scheme as ``http`` to disable SSL.
    Example:
        .. code-block:: python
            from langchain.utilities import SearxSearchWrapper
            searx = SearxSearchWrapper(searx_host="http://localhost:8888")
    Example with SSL disabled:
        .. code-block:: python
            from langchain.utilities import SearxSearchWrapper


.. code-block:: python
            from langchain.utilities import SearxSearchWrapper
            # note the unsecure parameter is not needed if you pass the url scheme as
            # http
            searx = SearxSearchWrapper(searx_host="http://localhost:8888",
                                                    unsecure=True)
    """
    _result: SearxResults = PrivateAttr()
    searx_host: str = ""
    unsecure: bool = False
    params: dict = Field(default_factory=_get_default_params)
    headers: Optional[dict] = None
    engines: Optional[List[str]] = []
    query_suffix: Optional[str] = ""
    k: int = 10
    @validator("unsecure")
    def disable_ssl_warnings(cls, v: bool) -> bool:
        """Disable SSL warnings."""
        if v:
            # requests.urllib3.disable_warnings()
            try:
                import urllib3
                urllib3.disable_warnings()
            except ImportError as e:
                print(e)
        return v
    @root_validator()
    def validate_params(cls, values: Dict) -> Dict:
        """Validate that custom searx params are merged with default ones."""
        user_params = values["params"]
        default = _get_default_params()
        values["params"] = {**default, **user_params}
        engines = values.get("engines")
        if engines:
            values["params"]["engines"] = ",".join(engines)
        searx_host = get_from_dict_or_env(values, "searx_host", "SEARX_HOST")
        if not searx_host.startswith("http"):
            print(
                f"Warning: missing the url scheme on host \


print(
                f"Warning: missing the url scheme on host \
                ! assuming secure https://{searx_host} "
            )
            searx_host = "https://" + searx_host
        elif searx_host.startswith("http://"):
            values["unsecure"] = True
            cls.disable_ssl_warnings(True)
        values["searx_host"] = searx_host
        return values
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    def _searx_api_query(self, params: dict) -> SearxResults:
        """Actual request to searx API."""
        raw_result = requests.get(
            self.searx_host,
            headers=self.headers,
            params=params,
            verify=not self.unsecure,
        )
        # test if http result is ok
        if not raw_result.ok:
            raise ValueError("Searx API returned an error: ", raw_result.text)
        res = SearxResults(raw_result.text)
        self._result = res
        return res
[docs]    def run(
        self,
        query: str,
        engines: Optional[List[str]] = None,
        query_suffix: Optional[str] = "",
        **kwargs: Any,
    ) -> str:
        """Run query through Searx API and parse results.
        You can pass any other params to the searx query API.
        Args:
            query: The query to search for.
            query_suffix: Extra suffix appended to the query.
            engines: List of engines to use for the query.
            **kwargs: extra parameters to pass to the searx API.
        Example:
            This will make a query to the qwant engine:


Example:
            This will make a query to the qwant engine:
            .. code-block:: python
                from langchain.utilities import SearxSearchWrapper
                searx = SearxSearchWrapper(searx_host="http://my.searx.host")
                searx.run("what is the weather in France ?", engine="qwant")
                # the same result can be achieved using the `!` syntax of searx
                # to select the engine using `query_suffix`
                searx.run("what is the weather in France ?", query_suffix="!qwant")
        """
        _params = {
            "q": query,
        }
        params = {**self.params, **_params, **kwargs}
        if self.query_suffix and len(self.query_suffix) > 0:
            params["q"] += " " + self.query_suffix
        if isinstance(query_suffix, str) and len(query_suffix) > 0:
            params["q"] += " " + query_suffix
        if isinstance(engines, list) and len(engines) > 0:
            params["engines"] = ",".join(engines)
        res = self._searx_api_query(params)
        if len(res.answers) > 0:
            toret = res.answers[0]
        # only return the content of the results list
        elif len(res.results) > 0:
            toret = "\n\n".join([r.get("content", "") for r in res.results[: self.k]])
        else:
            toret = "No good search result found"
        return toret
[docs]    def results(
        self,
        query: str,
        num_results: int,
        engines: Optional[List[str]] = None,


num_results: int,
        engines: Optional[List[str]] = None,
        query_suffix: Optional[str] = "",
        **kwargs: Any,
    ) -> List[Dict]:
        """Run query through Searx API and returns the results with metadata.
        Args:
            query: The query to search for.
            query_suffix: Extra suffix appended to the query.
            num_results: Limit the number of results to return.
            engines: List of engines to use for the query.
            **kwargs: extra parameters to pass to the searx API.
        Returns:
            Dict with the following keys:
            {
                snippet:  The description of the result.
                title:  The title of the result.
                link: The link to the result.
                engines: The engines used for the result.
                category: Searx category of the result.
            }
        """
        _params = {
            "q": query,
        }
        params = {**self.params, **_params, **kwargs}
        if self.query_suffix and len(self.query_suffix) > 0:
            params["q"] += " " + self.query_suffix
        if isinstance(query_suffix, str) and len(query_suffix) > 0:
            params["q"] += " " + query_suffix
        if isinstance(engines, list) and len(engines) > 0:
            params["engines"] = ",".join(engines)
        results = self._searx_api_query(params).results[:num_results]
        if len(results) == 0:
            return [{"Result": "No good Search Result was found"}]
        return [
            {
                "snippet": result.get("content", ""),
                "title": result["title"],


"snippet": result.get("content", ""),
                "title": result["title"],
                "link": result["url"],
                "engines": result["engines"],
                "category": result["category"],
            }
            for result in results
        ]
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.utilities.serpapi
"""Chain that calls SerpAPI.
Heavily borrowed from https://github.com/ofirpress/self-ask
"""
import os
import sys
from typing import Any, Dict, Optional, Tuple
import aiohttp
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.utils import get_from_dict_or_env
class HiddenPrints:
    """Context manager to hide prints."""
    def __enter__(self) -> None:
        """Open file to pipe stdout to."""
        self._original_stdout = sys.stdout
        sys.stdout = open(os.devnull, "w")
    def __exit__(self, *_: Any) -> None:
        """Close file that stdout was piped to."""
        sys.stdout.close()
        sys.stdout = self._original_stdout
[docs]class SerpAPIWrapper(BaseModel):
    """Wrapper around SerpAPI.
    To use, you should have the ``google-search-results`` python package installed,
    and the environment variable ``SERPAPI_API_KEY`` set with your API key, or pass
    `serpapi_api_key` as a named parameter to the constructor.
    Example:
        .. code-block:: python
            from langchain import SerpAPIWrapper
            serpapi = SerpAPIWrapper()
    """
    search_engine: Any  #: :meta private:
    params: dict = Field(
        default={
            "engine": "google",
            "google_domain": "google.com",
            "gl": "us",
            "hl": "en",
        }
    )
    serpapi_api_key: Optional[str] = None
    aiosession: Optional[aiohttp.ClientSession] = None
    class Config:


aiosession: Optional[aiohttp.ClientSession] = None
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        serpapi_api_key = get_from_dict_or_env(
            values, "serpapi_api_key", "SERPAPI_API_KEY"
        )
        values["serpapi_api_key"] = serpapi_api_key
        try:
            from serpapi import GoogleSearch
            values["search_engine"] = GoogleSearch
        except ImportError:
            raise ValueError(
                "Could not import serpapi python package. "
                "Please it install it with `pip install google-search-results`."
            )
        return values
[docs]    async def arun(self, query: str) -> str:
        """Use aiohttp to run query through SerpAPI and parse result."""
        def construct_url_and_params() -> Tuple[str, Dict[str, str]]:
            params = self.get_params(query)
            params["source"] = "python"
            if self.serpapi_api_key:
                params["serp_api_key"] = self.serpapi_api_key
            params["output"] = "json"
            url = "https://serpapi.com/search"
            return url, params
        url, params = construct_url_and_params()
        if not self.aiosession:
            async with aiohttp.ClientSession() as session:
                async with session.get(url, params=params) as response:
                    res = await response.json()
        else:
            async with self.aiosession.get(url, params=params) as response:


else:
            async with self.aiosession.get(url, params=params) as response:
                res = await response.json()
        return self._process_response(res)
[docs]    def run(self, query: str) -> str:
        """Run query through SerpAPI and parse result."""
        return self._process_response(self.results(query))
[docs]    def results(self, query: str) -> dict:
        """Run query through SerpAPI and return the raw result."""
        params = self.get_params(query)
        with HiddenPrints():
            search = self.search_engine(params)
            res = search.get_dict()
        return res
[docs]    def get_params(self, query: str) -> Dict[str, str]:
        """Get parameters for SerpAPI."""
        _params = {
            "api_key": self.serpapi_api_key,
            "q": query,
        }
        params = {**self.params, **_params}
        return params
    @staticmethod
    def _process_response(res: dict) -> str:
        """Process response from SerpAPI."""
        if "error" in res.keys():
            raise ValueError(f"Got error from SerpAPI: {res['error']}")
        if "answer_box" in res.keys() and "answer" in res["answer_box"].keys():
            toret = res["answer_box"]["answer"]
        elif "answer_box" in res.keys() and "snippet" in res["answer_box"].keys():
            toret = res["answer_box"]["snippet"]
        elif (
            "answer_box" in res.keys()
            and "snippet_highlighted_words" in res["answer_box"].keys()
        ):
            toret = res["answer_box"]["snippet_highlighted_words"][0]


):
            toret = res["answer_box"]["snippet_highlighted_words"][0]
        elif (
            "sports_results" in res.keys()
            and "game_spotlight" in res["sports_results"].keys()
        ):
            toret = res["sports_results"]["game_spotlight"]
        elif (
            "knowledge_graph" in res.keys()
            and "description" in res["knowledge_graph"].keys()
        ):
            toret = res["knowledge_graph"]["description"]
        elif "snippet" in res["organic_results"][0].keys():
            toret = res["organic_results"][0]["snippet"]
        else:
            toret = "No good search result found"
        return toret
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.vectorstores.opensearch_vector_search
"""Wrapper around OpenSearch vector database."""
from __future__ import annotations
import uuid
from typing import Any, Dict, Iterable, List, Optional
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore
IMPORT_OPENSEARCH_PY_ERROR = (
    "Could not import OpenSearch. Please install it with `pip install opensearch-py`."
)
SCRIPT_SCORING_SEARCH = "script_scoring"
PAINLESS_SCRIPTING_SEARCH = "painless_scripting"
MATCH_ALL_QUERY = {"match_all": {}}  # type: Dict
def _import_opensearch() -> Any:
    """Import OpenSearch if available, otherwise raise error."""
    try:
        from opensearchpy import OpenSearch
    except ImportError:
        raise ValueError(IMPORT_OPENSEARCH_PY_ERROR)
    return OpenSearch
def _import_bulk() -> Any:
    """Import bulk if available, otherwise raise error."""
    try:
        from opensearchpy.helpers import bulk
    except ImportError:
        raise ValueError(IMPORT_OPENSEARCH_PY_ERROR)
    return bulk
def _get_opensearch_client(opensearch_url: str) -> Any:
    """Get OpenSearch client from the opensearch_url, otherwise raise error."""
    try:
        opensearch = _import_opensearch()
        client = opensearch(opensearch_url)
    except ValueError as e:
        raise ValueError(
            f"OpenSearch client string provided is not in proper format. "
            f"Got error: {e} "
        )
    return client


f"Got error: {e} "
        )
    return client
def _validate_embeddings_and_bulk_size(embeddings_length: int, bulk_size: int) -> None:
    """Validate Embeddings Length and Bulk Size."""
    if embeddings_length == 0:
        raise RuntimeError("Embeddings size is zero")
    if bulk_size < embeddings_length:
        raise RuntimeError(
            f"The embeddings count, {embeddings_length} is more than the "
            f"[bulk_size], {bulk_size}. Increase the value of [bulk_size]."
        )
def _bulk_ingest_embeddings(
    client: Any,
    index_name: str,
    embeddings: List[List[float]],
    texts: Iterable[str],
    metadatas: Optional[List[dict]] = None,
) -> List[str]:
    """Bulk Ingest Embeddings into given index."""
    bulk = _import_bulk()
    requests = []
    ids = []
    for i, text in enumerate(texts):
        metadata = metadatas[i] if metadatas else {}
        _id = str(uuid.uuid4())
        request = {
            "_op_type": "index",
            "_index": index_name,
            "vector_field": embeddings[i],
            "text": text,
            "metadata": metadata,
            "_id": _id,
        }
        requests.append(request)
        ids.append(_id)
    bulk(client, requests)
    client.indices.refresh(index=index_name)
    return ids
def _default_scripting_text_mapping(dim: int) -> Dict:
    """For Painless Scripting or Script Scoring,the default mapping to create index."""
    return {
        "mappings": {
            "properties": {


return {
        "mappings": {
            "properties": {
                "vector_field": {"type": "knn_vector", "dimension": dim},
            }
        }
    }
def _default_text_mapping(
    dim: int,
    engine: str = "nmslib",
    space_type: str = "l2",
    ef_search: int = 512,
    ef_construction: int = 512,
    m: int = 16,
) -> Dict:
    """For Approximate k-NN Search, this is the default mapping to create index."""
    return {
        "settings": {"index": {"knn": True, "knn.algo_param.ef_search": ef_search}},
        "mappings": {
            "properties": {
                "vector_field": {
                    "type": "knn_vector",
                    "dimension": dim,
                    "method": {
                        "name": "hnsw",
                        "space_type": space_type,
                        "engine": engine,
                        "parameters": {"ef_construction": ef_construction, "m": m},
                    },
                }
            }
        },
    }
def _default_approximate_search_query(
    query_vector: List[float], size: int = 4, k: int = 4
) -> Dict:
    """For Approximate k-NN Search, this is the default query."""
    return {
        "size": size,
        "query": {"knn": {"vector_field": {"vector": query_vector, "k": k}}},
    }
def _default_script_query(
    query_vector: List[float],
    space_type: str = "l2",
    pre_filter: Dict = MATCH_ALL_QUERY,
) -> Dict:


pre_filter: Dict = MATCH_ALL_QUERY,
) -> Dict:
    """For Script Scoring Search, this is the default query."""
    return {
        "query": {
            "script_score": {
                "query": pre_filter,
                "script": {
                    "source": "knn_score",
                    "lang": "knn",
                    "params": {
                        "field": "vector_field",
                        "query_value": query_vector,
                        "space_type": space_type,
                    },
                },
            }
        }
    }
def __get_painless_scripting_source(space_type: str, query_vector: List[float]) -> str:
    """For Painless Scripting, it returns the script source based on space type."""
    source_value = (
        "(1.0 + " + space_type + "(" + str(query_vector) + ", doc['vector_field']))"
    )
    if space_type == "cosineSimilarity":
        return source_value
    else:
        return "1/" + source_value
def _default_painless_scripting_query(
    query_vector: List[float],
    space_type: str = "l2Squared",
    pre_filter: Dict = MATCH_ALL_QUERY,
) -> Dict:
    """For Painless Scripting Search, this is the default query."""
    source = __get_painless_scripting_source(space_type, query_vector)
    return {
        "query": {
            "script_score": {
                "query": pre_filter,
                "script": {
                    "source": source,
                    "params": {
                        "field": "vector_field",
                        "query_value": query_vector,
                    },
                },
            }
        }
    }


},
                },
            }
        }
    }
def _get_kwargs_value(kwargs: Any, key: str, default_value: Any) -> Any:
    """Get the value of the key if present. Else get the default_value."""
    if key in kwargs:
        return kwargs.get(key)
    return default_value
[docs]class OpenSearchVectorSearch(VectorStore):
    """Wrapper around OpenSearch as a vector database.
    Example:
        .. code-block:: python
            from langchain import OpenSearchVectorSearch
            opensearch_vector_search = OpenSearchVectorSearch(
                "http://localhost:9200",
                "embeddings",
                embedding_function
            )
    """
    def __init__(
        self, opensearch_url: str, index_name: str, embedding_function: Embeddings
    ):
        """Initialize with necessary components."""
        self.embedding_function = embedding_function
        self.index_name = index_name
        self.client = _get_opensearch_client(opensearch_url)
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        bulk_size: int = 500,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        Args:
            texts: Iterable of strings to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
            bulk_size: Bulk API request count; Default: 500
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        embeddings = [
            self.embedding_function.embed_documents(list(text))[0] for text in texts
        ]


self.embedding_function.embed_documents(list(text))[0] for text in texts
        ]
        _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
        return _bulk_ingest_embeddings(
            self.client, self.index_name, embeddings, texts, metadatas
        )
[docs]    def similarity_search(
        self, query: str, k: int = 4, **kwargs: Any
    ) -> List[Document]:
        """Return docs most similar to query.
        By default supports Approximate Search.
        Also supports Script Scoring and Painless Scripting.
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
        Returns:
            List of Documents most similar to the query.
        Optional Args for Approximate Search:
            search_type: "approximate_search"; default: "approximate_search"
            size: number of results the query actually returns; default: 4
        Optional Args for Script Scoring Search:
            search_type: "script_scoring"; default: "approximate_search"
            space_type: "l2", "l1", "linf", "cosinesimil", "innerproduct",
            "hammingbit"; default: "l2"
            pre_filter: script_score query to pre-filter documents before identifying
            nearest neighbors; default: {"match_all": {}}
        Optional Args for Painless Scripting Search:
            search_type: "painless_scripting"; default: "approximate_search"
            space_type: "l2Squared", "l1Norm", "cosineSimilarity"; default: "l2Squared"
            pre_filter: script_score query to pre-filter documents before identifying
            nearest neighbors; default: {"match_all": {}}
        """


nearest neighbors; default: {"match_all": {}}
        """
        embedding = self.embedding_function.embed_query(query)
        search_type = _get_kwargs_value(kwargs, "search_type", "approximate_search")
        if search_type == "approximate_search":
            size = _get_kwargs_value(kwargs, "size", 4)
            search_query = _default_approximate_search_query(embedding, size, k)
        elif search_type == SCRIPT_SCORING_SEARCH:
            space_type = _get_kwargs_value(kwargs, "space_type", "l2")
            pre_filter = _get_kwargs_value(kwargs, "pre_filter", MATCH_ALL_QUERY)
            search_query = _default_script_query(embedding, space_type, pre_filter)
        elif search_type == PAINLESS_SCRIPTING_SEARCH:
            space_type = _get_kwargs_value(kwargs, "space_type", "l2Squared")
            pre_filter = _get_kwargs_value(kwargs, "pre_filter", MATCH_ALL_QUERY)
            search_query = _default_painless_scripting_query(
                embedding, space_type, pre_filter
            )
        else:
            raise ValueError("Invalid `search_type` provided as an argument")
        response = self.client.search(index=self.index_name, body=search_query)
        hits = [hit["_source"] for hit in response["hits"]["hits"][:k]]
        documents = [
            Document(page_content=hit["text"], metadata=hit["metadata"]) for hit in hits
        ]
        return documents
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        bulk_size: int = 500,
        **kwargs: Any,
    ) -> OpenSearchVectorSearch:


**kwargs: Any,
    ) -> OpenSearchVectorSearch:
        """Construct OpenSearchVectorSearch wrapper from raw documents.
        Example:
            .. code-block:: python
                from langchain import OpenSearchVectorSearch
                from langchain.embeddings import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                opensearch_vector_search = OpenSearchVectorSearch.from_texts(
                    texts,
                    embeddings,
                    opensearch_url="http://localhost:9200"
                )
        OpenSearch by default supports Approximate Search powered by nmslib, faiss
        and lucene engines recommended for large datasets. Also supports brute force
        search through Script Scoring and Painless Scripting.
        Optional Keyword Args for Approximate Search:
            engine: "nmslib", "faiss", "hnsw"; default: "nmslib"
            space_type: "l2", "l1", "cosinesimil", "linf", "innerproduct"; default: "l2"
            ef_search: Size of the dynamic list used during k-NN searches. Higher values
            lead to more accurate but slower searches; default: 512
            ef_construction: Size of the dynamic list used during k-NN graph creation.
            Higher values lead to more accurate graph but slower indexing speed;
            default: 512
            m: Number of bidirectional links created for each new element. Large impact
            on memory consumption. Between 2 and 100; default: 16
        Keyword Args for Script Scoring or Painless Scripting:
            is_appx_search: False
        """
        opensearch_url = get_from_dict_or_env(
            kwargs, "opensearch_url", "OPENSEARCH_URL"
        )


kwargs, "opensearch_url", "OPENSEARCH_URL"
        )
        client = _get_opensearch_client(opensearch_url)
        embeddings = embedding.embed_documents(texts)
        _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
        dim = len(embeddings[0])
        index_name = uuid.uuid4().hex
        is_appx_search = _get_kwargs_value(kwargs, "is_appx_search", True)
        if is_appx_search:
            engine = _get_kwargs_value(kwargs, "engine", "nmslib")
            space_type = _get_kwargs_value(kwargs, "space_type", "l2")
            ef_search = _get_kwargs_value(kwargs, "ef_search", 512)
            ef_construction = _get_kwargs_value(kwargs, "ef_construction", 512)
            m = _get_kwargs_value(kwargs, "m", 16)
            mapping = _default_text_mapping(
                dim, engine, space_type, ef_search, ef_construction, m
            )
        else:
            mapping = _default_scripting_text_mapping(dim)
        client.indices.create(index=index_name, body=mapping)
        _bulk_ingest_embeddings(client, index_name, embeddings, texts, metadatas)
        return cls(opensearch_url, index_name, embedding)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Mar 22, 2023.


Source code for langchain.vectorstores.faiss
"""Wrapper around FAISS vector database."""
from __future__ import annotations
import pickle
import uuid
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.base import AddableMixin, Docstore
from langchain.docstore.document import Document
from langchain.docstore.in_memory import InMemoryDocstore
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
def dependable_faiss_import() -> Any:
    """Import faiss if available, otherwise raise error."""
    try:
        import faiss
    except ImportError:
        raise ValueError(
            "Could not import faiss python package. "
            "Please install it with `pip install faiss` "
            "or `pip install faiss-cpu` (depending on Python version)."
        )
    return faiss
[docs]class FAISS(VectorStore):
    """Wrapper around FAISS vector database.
    To use, you should have the ``faiss`` python package installed.
    Example:
        .. code-block:: python
            from langchain import FAISS
            faiss = FAISS(embedding_function, index, docstore, index_to_docstore_id)
    """
    def __init__(
        self,
        embedding_function: Callable,
        index: Any,
        docstore: Docstore,
        index_to_docstore_id: Dict[int, str],
    ):
        """Initialize with necessary components."""
        self.embedding_function = embedding_function
        self.index = index
        self.docstore = docstore
        self.index_to_docstore_id = index_to_docstore_id


self.index_to_docstore_id = index_to_docstore_id
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        Args:
            texts: Iterable of strings to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        if not isinstance(self.docstore, AddableMixin):
            raise ValueError(
                "If trying to add texts, the underlying docstore should support "
                f"adding items, which {self.docstore} does not"
            )
        # Embed and create the documents.
        embeddings = [self.embedding_function(text) for text in texts]
        documents = []
        for i, text in enumerate(texts):
            metadata = metadatas[i] if metadatas else {}
            documents.append(Document(page_content=text, metadata=metadata))
        # Add to the index, the index_to_id mapping, and the docstore.
        starting_len = len(self.index_to_docstore_id)
        self.index.add(np.array(embeddings, dtype=np.float32))
        # Get list of index, id, and docs.
        full_info = [
            (starting_len + i, str(uuid.uuid4()), doc)
            for i, doc in enumerate(documents)
        ]
        # Add information to docstore and index.
        self.docstore.add({_id: doc for _, _id, doc in full_info})


self.docstore.add({_id: doc for _, _id, doc in full_info})
        index_to_id = {index: _id for index, _id, _ in full_info}
        self.index_to_docstore_id.update(index_to_id)
        return [_id for _, _id, _ in full_info]
[docs]    def similarity_search_with_score_by_vector(
        self, embedding: List[float], k: int = 4
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to query.
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
        Returns:
            List of Documents most similar to the query and score for each
        """
        scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
        docs = []
        for j, i in enumerate(indices[0]):
            if i == -1:
                # This happens when not enough docs are returned.
                continue
            _id = self.index_to_docstore_id[i]
            doc = self.docstore.search(_id)
            if not isinstance(doc, Document):
                raise ValueError(f"Could not find document for id {_id}, got {doc}")
            docs.append((doc, scores[0][j]))
        return docs
[docs]    def similarity_search_with_score(
        self, query: str, k: int = 4
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to query.
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
        Returns:
            List of Documents most similar to the query and score for each


Returns:
            List of Documents most similar to the query and score for each
        """
        embedding = self.embedding_function(query)
        docs = self.similarity_search_with_score_by_vector(embedding, k)
        return docs
[docs]    def similarity_search_by_vector(
        self, embedding: List[float], k: int = 4, **kwargs: Any
    ) -> List[Document]:
        """Return docs most similar to embedding vector.
        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
        Returns:
            List of Documents most similar to the embedding.
        """
        docs_and_scores = self.similarity_search_with_score_by_vector(embedding, k)
        return [doc for doc, _ in docs_and_scores]
[docs]    def similarity_search(
        self, query: str, k: int = 4, **kwargs: Any
    ) -> List[Document]:
        """Return docs most similar to query.
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
        Returns:
            List of Documents most similar to the query.
        """
        docs_and_scores = self.similarity_search_with_score(query, k)
        return [doc for doc, _ in docs_and_scores]
[docs]    def max_marginal_relevance_search_by_vector(
        self, embedding: List[float], k: int = 4, fetch_k: int = 20
    ) -> List[Document]:
        """Return docs selected using the maximal marginal relevance.
        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.
        Args:


among selected documents.
        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        _, indices = self.index.search(np.array([embedding], dtype=np.float32), fetch_k)
        # -1 happens when not enough docs are returned.
        embeddings = [self.index.reconstruct(int(i)) for i in indices[0] if i != -1]
        mmr_selected = maximal_marginal_relevance(
            np.array([embedding], dtype=np.float32), embeddings, k=k
        )
        selected_indices = [indices[0][i] for i in mmr_selected]
        docs = []
        for i in selected_indices:
            if i == -1:
                # This happens when not enough docs are returned.
                continue
            _id = self.index_to_docstore_id[i]
            doc = self.docstore.search(_id)
            if not isinstance(doc, Document):
                raise ValueError(f"Could not find document for id {_id}, got {doc}")
            docs.append(doc)
        return docs
[docs]    def max_marginal_relevance_search(
        self, query: str, k: int = 4, fetch_k: int = 20
    ) -> List[Document]:
        """Return docs selected using the maximal marginal relevance.
        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.