Source code for langchain.agents.agent_toolkits.sql.toolkit
"""Toolkit for interacting with a SQL database."""
from typing import List
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.base_language import BaseLanguageModel
from langchain.sql_database import SQLDatabase
from langchain.tools import BaseTool
from langchain.tools.sql_database.tool import (
    InfoSQLDatabaseTool,
    ListSQLDatabaseTool,
    QueryCheckerTool,
    QuerySQLDataBaseTool,
)
[docs]class SQLDatabaseToolkit(BaseToolkit):
    """Toolkit for interacting with SQL databases."""
    db: SQLDatabase = Field(exclude=True)
    llm: BaseLanguageModel = Field(exclude=True)
    @property
    def dialect(self) -> str:
        """Return string representation of dialect to use."""
        return self.db.dialect
    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
[docs]    def get_tools(self) -> List[BaseTool]:
        """Get the tools in the toolkit."""
        return [
            QuerySQLDataBaseTool(db=self.db),
            InfoSQLDatabaseTool(db=self.db),
            ListSQLDatabaseTool(db=self.db),
            QueryCheckerTool(db=self.db, llm=self.llm),
        ]
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.agents.conversational.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.conversational.output_parser import ConvoOutputParser
from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
from langchain.agents.utils import validate_tools_single_input
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains import LLMChain
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"
    output_parser: AgentOutputParser = Field(default_factory=ConvoOutputParser)
    @classmethod
    def _get_default_output_parser(
        cls, ai_prefix: str = "AI", **kwargs: Any
    ) -> AgentOutputParser:
        return ConvoOutputParser(ai_prefix=ai_prefix)
    @property
    def _agent_type(self) -> str:
        """Return Identifier of agent type."""
        return AgentType.CONVERSATIONAL_REACT_DESCRIPTION
    @property
    def observation_prefix(self) -> str:
        """Prefix to append the observation with."""
        return "Observation: "
    @property
    def llm_prefix(self) -> str:
        """Prefix to append the llm call with."""
        return "Thought:"
[docs]    @classmethod
    def create_prompt(
        cls,


[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.
            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)
    @classmethod
    def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
        super()._validate_tools(tools)
        validate_tools_single_input(cls.__name__, tools)


super()._validate_tools(tools)
        validate_tools_single_input(cls.__name__, tools)
[docs]    @classmethod
    def from_llm_and_tools(
        cls,
        llm: BaseLanguageModel,
        tools: Sequence[BaseTool],
        callback_manager: Optional[BaseCallbackManager] = None,
        output_parser: Optional[AgentOutputParser] = 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]
        _output_parser = output_parser or cls._get_default_output_parser(
            ai_prefix=ai_prefix
        )
        return cls(
            llm_chain=llm_chain,
            allowed_tools=tool_names,
            ai_prefix=ai_prefix,
            output_parser=_output_parser,
            **kwargs,
        )
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.agents.react.base
"""Chain that implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf."""
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.react.output_parser import ReActOutputParser
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.agents.utils import validate_tools_single_input
from langchain.base_language import BaseLanguageModel
from langchain.docstore.base import Docstore
from langchain.docstore.document import Document
from langchain.prompts.base import BasePromptTemplate
from langchain.tools.base import BaseTool
class ReActDocstoreAgent(Agent):
    """Agent for the ReAct chain."""
    output_parser: AgentOutputParser = Field(default_factory=ReActOutputParser)
    @classmethod
    def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
        return ReActOutputParser()
    @property
    def _agent_type(self) -> str:
        """Return Identifier of agent type."""
        return AgentType.REACT_DOCSTORE
    @classmethod
    def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
        """Return default prompt."""
        return WIKI_PROMPT
    @classmethod
    def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
        validate_tools_single_input(cls.__name__, tools)
        super()._validate_tools(tools)
        if len(tools) != 2:


super()._validate_tools(tools)
        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}"
            )
    @property
    def observation_prefix(self) -> str:
        """Prefix to append the observation with."""
        return "Observation: "
    @property
    def _stop(self) -> List[str]:
        return ["\nObservation:"]
    @property
    def llm_prefix(self) -> str:
        """Prefix to append the LLM call with."""
        return "Thought:"
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.lookup_str = ""
        self.lookup_index = 0
    def search(self, term: str) -> str:
        """Search for a term in the docstore, and if found save."""
        result = self.docstore.search(term)
        if isinstance(result, Document):
            self.document = result
            return self._summary
        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")
        if term.lower() != self.lookup_str:


if term.lower() != self.lookup_str:
            self.lookup_str = term.lower()
            self.lookup_index = 0
        else:
            self.lookup_index += 1
        lookups = [p for p in self._paragraphs if self.lookup_str in p.lower()]
        if len(lookups) == 0:
            return "No Results"
        elif self.lookup_index >= len(lookups):
            return "No More Results"
        else:
            result_prefix = f"(Result {self.lookup_index + 1}/{len(lookups)})"
            return f"{result_prefix} {lookups[self.lookup_index]}"
    @property
    def _summary(self) -> str:
        return self._paragraphs[0]
    @property
    def _paragraphs(self) -> List[str]:
        if self.document is None:
            raise ValueError("Cannot get paragraphs without a document")
        return self.document.page_content.split("\n\n")
[docs]class ReActTextWorldAgent(ReActDocstoreAgent):
    """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:
        validate_tools_single_input(cls.__name__, tools)
        super()._validate_tools(tools)
        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}")


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: BaseLanguageModel, docstore: Docstore, **kwargs: Any):
        """Initialize with the LLM and a docstore."""
        docstore_explorer = DocstoreExplorer(docstore)
        tools = [
            Tool(
                name="Search",
                func=docstore_explorer.search,
                description="Search for a term in the docstore.",
            ),
            Tool(
                name="Lookup",
                func=docstore_explorer.lookup,
                description="Lookup a term in the docstore.",
            ),
        ]
        agent = ReActDocstoreAgent.from_llm_and_tools(llm, tools)
        super().__init__(agent=agent, tools=tools, **kwargs)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.spark_sql
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Iterable, List, Optional
if TYPE_CHECKING:
    from pyspark.sql import DataFrame, Row, SparkSession
[docs]class SparkSQL:
    def __init__(
        self,
        spark_session: Optional[SparkSession] = None,
        catalog: Optional[str] = None,
        schema: Optional[str] = None,
        ignore_tables: Optional[List[str]] = None,
        include_tables: Optional[List[str]] = None,
        sample_rows_in_table_info: int = 3,
    ):
        try:
            from pyspark.sql import SparkSession
        except ImportError:
            raise ValueError(
                "pyspark is not installed. Please install it with `pip install pyspark`"
            )
        self._spark = (
            spark_session if spark_session else SparkSession.builder.getOrCreate()
        )
        if catalog is not None:
            self._spark.catalog.setCurrentCatalog(catalog)
        if schema is not None:
            self._spark.catalog.setCurrentDatabase(schema)
        self._all_tables = set(self._get_all_table_names())
        self._include_tables = set(include_tables) if include_tables else set()
        if self._include_tables:
            missing_tables = self._include_tables - self._all_tables
            if missing_tables:
                raise ValueError(
                    f"include_tables {missing_tables} not found in database"
                )
        self._ignore_tables = set(ignore_tables) if ignore_tables else set()
        if self._ignore_tables:
            missing_tables = self._ignore_tables - self._all_tables
            if missing_tables:
                raise ValueError(
                    f"ignore_tables {missing_tables} not found in database"
                )


f"ignore_tables {missing_tables} not found in database"
                )
        usable_tables = self.get_usable_table_names()
        self._usable_tables = set(usable_tables) if usable_tables else self._all_tables
        if not isinstance(sample_rows_in_table_info, int):
            raise TypeError("sample_rows_in_table_info must be an integer")
        self._sample_rows_in_table_info = sample_rows_in_table_info
[docs]    @classmethod
    def from_uri(
        cls, database_uri: str, engine_args: Optional[dict] = None, **kwargs: Any
    ) -> SparkSQL:
        """Creating a remote Spark Session via Spark connect.
        For example: SparkSQL.from_uri("sc://localhost:15002")
        """
        try:
            from pyspark.sql import SparkSession
        except ImportError:
            raise ValueError(
                "pyspark is not installed. Please install it with `pip install pyspark`"
            )
        spark = SparkSession.builder.remote(database_uri).getOrCreate()
        return cls(spark, **kwargs)
[docs]    def get_usable_table_names(self) -> Iterable[str]:
        """Get names of tables available."""
        if self._include_tables:
            return self._include_tables
        # sorting the result can help LLM understanding it.
        return sorted(self._all_tables - self._ignore_tables)
    def _get_all_table_names(self) -> Iterable[str]:
        rows = self._spark.sql("SHOW TABLES").select("tableName").collect()
        return list(map(lambda row: row.tableName, rows))
    def _get_create_table_stmt(self, table: str) -> str:
        statement = (
            self._spark.sql(f"SHOW CREATE TABLE {table}").collect()[0].createtab_stmt


)
        # Ignore the data source provider and options to reduce the number of tokens.
        using_clause_index = statement.find("USING")
        return statement[:using_clause_index] + ";"
[docs]    def get_table_info(self, table_names: Optional[List[str]] = None) -> str:
        all_table_names = self.get_usable_table_names()
        if table_names is not None:
            missing_tables = set(table_names).difference(all_table_names)
            if missing_tables:
                raise ValueError(f"table_names {missing_tables} not found in database")
            all_table_names = table_names
        tables = []
        for table_name in all_table_names:
            table_info = self._get_create_table_stmt(table_name)
            if self._sample_rows_in_table_info:
                table_info += "\n\n/*"
                table_info += f"\n{self._get_sample_spark_rows(table_name)}\n"
                table_info += "*/"
            tables.append(table_info)
        final_str = "\n\n".join(tables)
        return final_str
    def _get_sample_spark_rows(self, table: str) -> str:
        query = f"SELECT * FROM {table} LIMIT {self._sample_rows_in_table_info}"
        df = self._spark.sql(query)
        columns_str = "\t".join(list(map(lambda f: f.name, df.schema.fields)))
        try:
            sample_rows = self._get_dataframe_results(df)
            # save the sample rows in string format
            sample_rows_str = "\n".join(["\t".join(row) for row in sample_rows])
        except Exception:
            sample_rows_str = ""
        return (
            f"{self._sample_rows_in_table_info} rows from {table} table:\n"


f"{self._sample_rows_in_table_info} rows from {table} table:\n"
            f"{columns_str}\n"
            f"{sample_rows_str}"
        )
    def _convert_row_as_tuple(self, row: Row) -> tuple:
        return tuple(map(str, row.asDict().values()))
    def _get_dataframe_results(self, df: DataFrame) -> list:
        return list(map(self._convert_row_as_tuple, df.collect()))
[docs]    def run(self, command: str, fetch: str = "all") -> str:
        df = self._spark.sql(command)
        if fetch == "one":
            df = df.limit(1)
        return str(self._get_dataframe_results(df))
[docs]    def get_table_info_no_throw(self, table_names: Optional[List[str]] = None) -> str:
        """Get information about specified tables.
        Follows best practices as specified in: Rajkumar et al, 2022
        (https://arxiv.org/abs/2204.00498)
        If `sample_rows_in_table_info`, the specified number of sample rows will be
        appended to each table description. This can increase performance as
        demonstrated in the paper.
        """
        try:
            return self.get_table_info(table_names)
        except ValueError as e:
            """Format the error message"""
            return f"Error: {e}"
[docs]    def run_no_throw(self, command: str, fetch: str = "all") -> str:
        """Execute a SQL command and return a string representing the results.
        If the statement returns rows, a string of the results is returned.
        If the statement returns no rows, an empty string is returned.
        If the statement throws an error, the error message is returned.


If the statement throws an error, the error message is returned.
        """
        try:
            from pyspark.errors import PySparkException
        except ImportError:
            raise ValueError(
                "pyspark is not installed. Please install it with `pip install pyspark`"
            )
        try:
            return self.run(command, fetch)
        except PySparkException as e:
            """Format the error message"""
            return f"Error: {e}"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.python
import sys
from io import StringIO
from typing import Dict, Optional
from pydantic import BaseModel, Field
[docs]class PythonREPL(BaseModel):
    """Simulates a standalone Python REPL."""
    globals: Optional[Dict] = Field(default_factory=dict, alias="_globals")
    locals: Optional[Dict] = Field(default_factory=dict, alias="_locals")
[docs]    def run(self, command: str) -> str:
        """Run command with own globals/locals and returns anything printed."""
        old_stdout = sys.stdout
        sys.stdout = mystdout = StringIO()
        try:
            exec(command, self.globals, self.locals)
            sys.stdout = old_stdout
            output = mystdout.getvalue()
        except Exception as e:
            sys.stdout = old_stdout
            output = repr(e)
        return output
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.bash
"""Wrapper around subprocess to run commands."""
from __future__ import annotations
import platform
import re
import subprocess
from typing import TYPE_CHECKING, List, Union
from uuid import uuid4
if TYPE_CHECKING:
    import pexpect
def _lazy_import_pexpect() -> pexpect:
    """Import pexpect only when needed."""
    if platform.system() == "Windows":
        raise ValueError("Persistent bash processes are not yet supported on Windows.")
    try:
        import pexpect
    except ImportError:
        raise ImportError(
            "pexpect required for persistent bash processes."
            " To install, run `pip install pexpect`."
        )
    return pexpect
[docs]class BashProcess:
    """Executes bash commands and returns the output."""
    def __init__(
        self,
        strip_newlines: bool = False,
        return_err_output: bool = False,
        persistent: bool = False,
    ):
        """Initialize with stripping newlines."""
        self.strip_newlines = strip_newlines
        self.return_err_output = return_err_output
        self.prompt = ""
        self.process = None
        if persistent:
            self.prompt = str(uuid4())
            self.process = self._initialize_persistent_process(self.prompt)
    @staticmethod
    def _initialize_persistent_process(prompt: str) -> pexpect.spawn:
        # Start bash in a clean environment
        # Doesn't work on windows
        pexpect = _lazy_import_pexpect()
        process = pexpect.spawn(
            "env", ["-i", "bash", "--norc", "--noprofile"], encoding="utf-8"
        )
        # Set the custom prompt
        process.sendline("PS1=" + prompt)


# Set the custom prompt
        process.sendline("PS1=" + prompt)
        process.expect_exact(prompt, timeout=10)
        return process
[docs]    def run(self, commands: Union[str, List[str]]) -> str:
        """Run commands and return final output."""
        if isinstance(commands, str):
            commands = [commands]
        commands = ";".join(commands)
        if self.process is not None:
            return self._run_persistent(
                commands,
            )
        else:
            return self._run(commands)
    def _run(self, command: str) -> str:
        """Run commands and return final output."""
        try:
            output = subprocess.run(
                command,
                shell=True,
                check=True,
                stdout=subprocess.PIPE,
                stderr=subprocess.STDOUT,
            ).stdout.decode()
        except subprocess.CalledProcessError as error:
            if self.return_err_output:
                return error.stdout.decode()
            return str(error)
        if self.strip_newlines:
            output = output.strip()
        return output
[docs]    def process_output(self, output: str, command: str) -> str:
        # Remove the command from the output using a regular expression
        pattern = re.escape(command) + r"\s*\n"
        output = re.sub(pattern, "", output, count=1)
        return output.strip()
    def _run_persistent(self, command: str) -> str:
        """Run commands and return final output."""
        pexpect = _lazy_import_pexpect()
        if self.process is None:
            raise ValueError("Process not initialized")
        self.process.sendline(command)
        # Clear the output with an empty string
        self.process.expect(self.prompt, timeout=10)


self.process.expect(self.prompt, timeout=10)
        self.process.sendline("")
        try:
            self.process.expect([self.prompt, pexpect.EOF], timeout=10)
        except pexpect.TIMEOUT:
            return f"Timeout error while executing command {command}"
        if self.process.after == pexpect.EOF:
            return f"Exited with error status: {self.process.exitstatus}"
        output = self.process.before
        output = self.process_output(output, command)
        if self.strip_newlines:
            return output.strip()
        return output
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.google_places_api
"""Chain that calls Google Places API.
"""
import logging
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class GooglePlacesAPIWrapper(BaseModel):
    """Wrapper around Google Places API.
    To use, you should have the ``googlemaps`` python package installed,
     **an API key for the google maps platform**,
     and the enviroment variable ''GPLACES_API_KEY''
     set with your API key , or pass 'gplaces_api_key'
     as a named parameter to the constructor.
    By default, this will return the all the results on the input query.
     You can use the top_k_results argument to limit the number of results.
    Example:
        .. code-block:: python
            from langchain import GooglePlacesAPIWrapper
            gplaceapi = GooglePlacesAPIWrapper()
    """
    gplaces_api_key: Optional[str] = None
    google_map_client: Any  #: :meta private:
    top_k_results: Optional[int] = 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 is in your environment variable."""
        gplaces_api_key = get_from_dict_or_env(
            values, "gplaces_api_key", "GPLACES_API_KEY"
        )
        values["gplaces_api_key"] = gplaces_api_key
        try:
            import googlemaps
            values["google_map_client"] = googlemaps.Client(gplaces_api_key)
        except ImportError:
            raise ImportError(


except ImportError:
            raise ImportError(
                "Could not import googlemaps python package. "
                "Please install it with `pip install googlemaps`."
            )
        return values
[docs]    def run(self, query: str) -> str:
        """Run Places search and get k number of places that exists that match."""
        search_results = self.google_map_client.places(query)["results"]
        num_to_return = len(search_results)
        places = []
        if num_to_return == 0:
            return "Google Places did not find any places that match the description"
        num_to_return = (
            num_to_return
            if self.top_k_results is None
            else min(num_to_return, self.top_k_results)
        )
        for i in range(num_to_return):
            result = search_results[i]
            details = self.fetch_place_details(result["place_id"])
            if details is not None:
                places.append(details)
        return "\n".join([f"{i+1}. {item}" for i, item in enumerate(places)])
[docs]    def fetch_place_details(self, place_id: str) -> Optional[str]:
        try:
            place_details = self.google_map_client.place(place_id)
            formatted_details = self.format_place_details(place_details)
            return formatted_details
        except Exception as e:
            logging.error(f"An Error occurred while fetching place details: {e}")
            return None
[docs]    def format_place_details(self, place_details: Dict[str, Any]) -> Optional[str]:
        try:
            name = place_details.get("result", {}).get("name", "Unkown")
            address = place_details.get("result", {}).get(
                "formatted_address", "Unknown"
            )


"formatted_address", "Unknown"
            )
            phone_number = place_details.get("result", {}).get(
                "formatted_phone_number", "Unknown"
            )
            website = place_details.get("result", {}).get("website", "Unknown")
            formatted_details = (
                f"{name}\nAddress: {address}\n"
                f"Phone: {phone_number}\nWebsite: {website}\n\n"
            )
            return formatted_details
        except Exception as e:
            logging.error(f"An error occurred while formatting place details: {e}")
            return None
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.graphql
import json
from typing import Any, Callable, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
[docs]class GraphQLAPIWrapper(BaseModel):
    """Wrapper around GraphQL API.
    To use, you should have the ``gql`` python package installed.
    This wrapper will use the GraphQL API to conduct queries.
    """
    custom_headers: Optional[Dict[str, str]] = None
    graphql_endpoint: str
    gql_client: Any  #: :meta private:
    gql_function: Callable[[str], Any]  #: :meta private:
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator(pre=True)
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that the python package exists in the environment."""
        try:
            from gql import Client, gql
            from gql.transport.requests import RequestsHTTPTransport
        except ImportError as e:
            raise ImportError(
                "Could not import gql python package. "
                f"Try installing it with `pip install gql`. Received error: {e}"
            )
        headers = values.get("custom_headers")
        transport = RequestsHTTPTransport(
            url=values["graphql_endpoint"],
            headers=headers,
        )
        client = Client(transport=transport, fetch_schema_from_transport=True)
        values["gql_client"] = client
        values["gql_function"] = gql
        return values
[docs]    def run(self, query: str) -> str:
        """Run a GraphQL query and get the results."""
        result = self._execute_query(query)
        return json.dumps(result, indent=2)


result = self._execute_query(query)
        return json.dumps(result, indent=2)
    def _execute_query(self, query: str) -> Dict[str, Any]:
        """Execute a GraphQL query and return the results."""
        document_node = self.gql_function(query)
        result = self.gql_client.execute(document_node)
        return result
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.duckduckgo_search
"""Util that calls DuckDuckGo Search.
No setup required. Free.
https://pypi.org/project/duckduckgo-search/
"""
from typing import Dict, List, Optional
from pydantic import BaseModel, Extra
from pydantic.class_validators import root_validator
[docs]class DuckDuckGoSearchAPIWrapper(BaseModel):
    """Wrapper for DuckDuckGo Search API.
    Free and does not require any setup
    """
    k: int = 10
    region: Optional[str] = "wt-wt"
    safesearch: str = "moderate"
    time: Optional[str] = "y"
    max_results: int = 5
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that python package exists in environment."""
        try:
            from duckduckgo_search import ddg  # noqa: F401
        except ImportError:
            raise ValueError(
                "Could not import duckduckgo-search python package. "
                "Please install it with `pip install duckduckgo-search`."
            )
        return values
[docs]    def get_snippets(self, query: str) -> List[str]:
        """Run query through DuckDuckGo and return concatenated results."""
        from duckduckgo_search import ddg
        results = ddg(
            query,
            region=self.region,
            safesearch=self.safesearch,
            time=self.time,
            max_results=self.max_results,
        )
        if results is None or len(results) == 0:


)
        if results is None or len(results) == 0:
            return ["No good DuckDuckGo Search Result was found"]
        snippets = [result["body"] for result in results]
        return snippets
[docs]    def run(self, query: str) -> str:
        snippets = self.get_snippets(query)
        return " ".join(snippets)
[docs]    def results(self, query: str, num_results: int) -> List[Dict[str, str]]:
        """Run query through DuckDuckGo and return metadata.
        Args:
            query: The query to search for.
            num_results: The number of results to return.
        Returns:
            A list of dictionaries with the following keys:
                snippet - The description of the result.
                title - The title of the result.
                link - The link to the result.
        """
        from duckduckgo_search import ddg
        results = ddg(
            query,
            region=self.region,
            safesearch=self.safesearch,
            time=self.time,
            max_results=num_results,
        )
        if results is None or len(results) == 0:
            return [{"Result": "No good DuckDuckGo Search Result was found"}]
        def to_metadata(result: Dict) -> Dict[str, str]:
            return {
                "snippet": result["body"],
                "title": result["title"],
                "link": result["href"],
            }
        return [to_metadata(result) for result in results]
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.apify
from typing import Any, Callable, Dict, Optional
from pydantic import BaseModel, root_validator
from langchain.document_loaders import ApifyDatasetLoader
from langchain.document_loaders.base import Document
from langchain.utils import get_from_dict_or_env
[docs]class ApifyWrapper(BaseModel):
    """Wrapper around Apify.
    To use, you should have the ``apify-client`` python package installed,
    and the environment variable ``APIFY_API_TOKEN`` set with your API key, or pass
    `apify_api_token` as a named parameter to the constructor.
    """
    apify_client: Any
    apify_client_async: Any
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate environment.
        Validate that an Apify API token is set and the apify-client
        Python package exists in the current environment.
        """
        apify_api_token = get_from_dict_or_env(
            values, "apify_api_token", "APIFY_API_TOKEN"
        )
        try:
            from apify_client import ApifyClient, ApifyClientAsync
            values["apify_client"] = ApifyClient(apify_api_token)
            values["apify_client_async"] = ApifyClientAsync(apify_api_token)
        except ImportError:
            raise ValueError(
                "Could not import apify-client Python package. "
                "Please install it with `pip install apify-client`."
            )
        return values
[docs]    def call_actor(
        self,
        actor_id: str,
        run_input: Dict,
        dataset_mapping_function: Callable[[Dict], Document],
        *,
        build: Optional[str] = None,


*,
        build: Optional[str] = None,
        memory_mbytes: Optional[int] = None,
        timeout_secs: Optional[int] = None,
    ) -> ApifyDatasetLoader:
        """Run an Actor on the Apify platform and wait for results to be ready.
        Args:
            actor_id (str): The ID or name of the Actor on the Apify platform.
            run_input (Dict): The input object of the Actor that you're trying to run.
            dataset_mapping_function (Callable): A function that takes a single
                dictionary (an Apify dataset item) and converts it to an
                instance of the Document class.
            build (str, optional): Optionally specifies the actor build to run.
                It can be either a build tag or build number.
            memory_mbytes (int, optional): Optional memory limit for the run,
                in megabytes.
            timeout_secs (int, optional): Optional timeout for the run, in seconds.
        Returns:
            ApifyDatasetLoader: A loader that will fetch the records from the
                Actor run's default dataset.
        """
        actor_call = self.apify_client.actor(actor_id).call(
            run_input=run_input,
            build=build,
            memory_mbytes=memory_mbytes,
            timeout_secs=timeout_secs,
        )
        return ApifyDatasetLoader(
            dataset_id=actor_call["defaultDatasetId"],
            dataset_mapping_function=dataset_mapping_function,
        )
[docs]    async def acall_actor(
        self,
        actor_id: str,
        run_input: Dict,
        dataset_mapping_function: Callable[[Dict], Document],
        *,
        build: Optional[str] = None,
        memory_mbytes: Optional[int] = None,


memory_mbytes: Optional[int] = None,
        timeout_secs: Optional[int] = None,
    ) -> ApifyDatasetLoader:
        """Run an Actor on the Apify platform and wait for results to be ready.
        Args:
            actor_id (str): The ID or name of the Actor on the Apify platform.
            run_input (Dict): The input object of the Actor that you're trying to run.
            dataset_mapping_function (Callable): A function that takes a single
                dictionary (an Apify dataset item) and converts it to
                an instance of the Document class.
            build (str, optional): Optionally specifies the actor build to run.
                It can be either a build tag or build number.
            memory_mbytes (int, optional): Optional memory limit for the run,
                in megabytes.
            timeout_secs (int, optional): Optional timeout for the run, in seconds.
        Returns:
            ApifyDatasetLoader: A loader that will fetch the records from the
                Actor run's default dataset.
        """
        actor_call = await self.apify_client_async.actor(actor_id).call(
            run_input=run_input,
            build=build,
            memory_mbytes=memory_mbytes,
            timeout_secs=timeout_secs,
        )
        return ApifyDatasetLoader(
            dataset_id=actor_call["defaultDatasetId"],
            dataset_mapping_function=dataset_mapping_function,
        )
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 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. <../../integrations/searx.html>`_
For the search API refer to https://docs.searxng.org/dev/search_api.html
Quick Start
-----------
In order to use this utility 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 aiohttp
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]] = []
    categories: Optional[List[str]] = []
    query_suffix: Optional[str] = ""
    k: int = 10
    aiosession: Optional[Any] = None
    @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)
        categories = values.get("categories")
        if categories:
            values["params"]["categories"] = ",".join(categories)


if categories:
            values["params"]["categories"] = ",".join(categories)
        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 \
                ! 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
    async def _asearx_api_query(self, params: dict) -> SearxResults:
        if not self.aiosession:
            async with aiohttp.ClientSession() as session:
                async with session.get(
                    self.searx_host,
                    headers=self.headers,
                    params=params,
                    ssl=(lambda: False if self.unsecure else None)(),
                ) as response:
                    if not response.ok:


) as response:
                    if not response.ok:
                        raise ValueError("Searx API returned an error: ", response.text)
                    result = SearxResults(await response.text())
                    self._result = result
        else:
            async with self.aiosession.get(
                self.searx_host,
                headers=self.headers,
                params=params,
                verify=not self.unsecure,
            ) as response:
                if not response.ok:
                    raise ValueError("Searx API returned an error: ", response.text)
                result = SearxResults(await response.text())
                self._result = result
        return result
[docs]    def run(
        self,
        query: str,
        engines: Optional[List[str]] = None,
        categories: 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.
            categories: List of categories to use for the query.
            **kwargs: extra parameters to pass to the searx API.
        Returns:
            str: The result of the query.
        Raises:
            ValueError: If an error occured with the query.
        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)
        if isinstance(categories, list) and len(categories) > 0:
            params["categories"] = ",".join(categories)
        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]    async def arun(
        self,
        query: str,
        engines: Optional[List[str]] = None,
        query_suffix: Optional[str] = "",
        **kwargs: Any,
    ) -> str:
        """Asynchronously version of `run`."""


) -> str:
        """Asynchronously version of `run`."""
        _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 = await self._asearx_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,
        categories: 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.
            categories: List of categories to use for the query.


categories: List of categories 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)
        if isinstance(categories, list) and len(categories) > 0:
            params["categories"] = ",".join(categories)
        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"],
                "link": result["url"],
                "engines": result["engines"],
                "category": result["category"],
            }
            for result in results
        ]
[docs]    async def aresults(
        self,
        query: str,
        num_results: int,


self,
        query: str,
        num_results: int,
        engines: Optional[List[str]] = None,
        query_suffix: Optional[str] = "",
        **kwargs: Any,
    ) -> List[Dict]:
        """Asynchronously query with json results.
        Uses aiohttp. See `results` for more info.
        """
        _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 = (await self._asearx_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"],
                "link": result["url"],
                "engines": result["engines"],
                "category": result["category"],
            }
            for result in results
        ]
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.google_search
"""Util that calls Google Search."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class GoogleSearchAPIWrapper(BaseModel):
    """Wrapper for Google Search API.
    Adapted from: Instructions adapted from https://stackoverflow.com/questions/
    37083058/
    programmatically-searching-google-in-python-using-custom-search
    TODO: DOCS for using it
    1. Install google-api-python-client
    - If you don't already have a Google account, sign up.
    - If you have never created a Google APIs Console project,
    read the Managing Projects page and create a project in the Google API Console.
    - Install the library using pip install google-api-python-client
    The current version of the library is 2.70.0 at this time
    2. To create an API key:
    - Navigate to the APIs & Services→Credentials panel in Cloud Console.
    - Select Create credentials, then select API key from the drop-down menu.
    - The API key created dialog box displays your newly created key.
    - You now have an API_KEY
    3. Setup Custom Search Engine so you can search the entire web
    - Create a custom search engine in this link.
    - In Sites to search, add any valid URL (i.e. www.stackoverflow.com).
    - That’s all you have to fill up, the rest doesn’t matter.
    In the left-side menu, click Edit search engine → {your search engine name}
    → Setup Set Search the entire web to ON. Remove the URL you added from
     the list of Sites to search.
    - Under Search engine ID you’ll find the search-engine-ID.


- Under Search engine ID you’ll find the search-engine-ID.
    4. Enable the Custom Search API
    - Navigate to the APIs & Services→Dashboard panel in Cloud Console.
    - Click Enable APIs and Services.
    - Search for Custom Search API and click on it.
    - Click Enable.
    URL for it: https://console.cloud.google.com/apis/library/customsearch.googleapis
    .com
    """
    search_engine: Any  #: :meta private:
    google_api_key: Optional[str] = None
    google_cse_id: Optional[str] = None
    k: int = 10
    siterestrict: bool = False
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    def _google_search_results(self, search_term: str, **kwargs: Any) -> List[dict]:
        cse = self.search_engine.cse()
        if self.siterestrict:
            cse = cse.siterestrict()
        res = cse.list(q=search_term, cx=self.google_cse_id, **kwargs).execute()
        return res.get("items", [])
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        google_api_key = get_from_dict_or_env(
            values, "google_api_key", "GOOGLE_API_KEY"
        )
        values["google_api_key"] = google_api_key
        google_cse_id = get_from_dict_or_env(values, "google_cse_id", "GOOGLE_CSE_ID")
        values["google_cse_id"] = google_cse_id
        try:
            from googleapiclient.discovery import build
        except ImportError:
            raise ImportError(


from googleapiclient.discovery import build
        except ImportError:
            raise ImportError(
                "google-api-python-client is not installed. "
                "Please install it with `pip install google-api-python-client`"
            )
        service = build("customsearch", "v1", developerKey=google_api_key)
        values["search_engine"] = service
        return values
[docs]    def run(self, query: str) -> str:
        """Run query through GoogleSearch and parse result."""
        snippets = []
        results = self._google_search_results(query, num=self.k)
        if len(results) == 0:
            return "No good Google Search Result was found"
        for result in results:
            if "snippet" in result:
                snippets.append(result["snippet"])
        return " ".join(snippets)
[docs]    def results(self, query: str, num_results: int) -> List[Dict]:
        """Run query through GoogleSearch and return metadata.
        Args:
            query: The query to search for.
            num_results: The number of results to return.
        Returns:
            A list of dictionaries with the following keys:
                snippet - The description of the result.
                title - The title of the result.
                link - The link to the result.
        """
        metadata_results = []
        results = self._google_search_results(query, num=num_results)
        if len(results) == 0:
            return [{"Result": "No good Google Search Result was found"}]
        for result in results:
            metadata_result = {
                "title": result["title"],
                "link": result["link"],
            }
            if "snippet" in result:
                metadata_result["snippet"] = result["snippet"]


if "snippet" in result:
                metadata_result["snippet"] = result["snippet"]
            metadata_results.append(metadata_result)
        return metadata_results
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.wikipedia
"""Util that calls Wikipedia."""
import logging
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.schema import Document
logger = logging.getLogger(__name__)
WIKIPEDIA_MAX_QUERY_LENGTH = 300
[docs]class WikipediaAPIWrapper(BaseModel):
    """Wrapper around WikipediaAPI.
    To use, you should have the ``wikipedia`` python package installed.
    This wrapper will use the Wikipedia API to conduct searches and
    fetch page summaries. By default, it will return the page summaries
    of the top-k results.
    It limits the Document content by doc_content_chars_max.
    """
    wiki_client: Any  #: :meta private:
    top_k_results: int = 3
    lang: str = "en"
    load_all_available_meta: bool = False
    doc_content_chars_max: int = 4000
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that the python package exists in environment."""
        try:
            import wikipedia
            wikipedia.set_lang(values["lang"])
            values["wiki_client"] = wikipedia
        except ImportError:
            raise ImportError(
                "Could not import wikipedia python package. "
                "Please install it with `pip install wikipedia`."
            )
        return values
[docs]    def run(self, query: str) -> str:
        """Run Wikipedia search and get page summaries."""
        page_titles = self.wiki_client.search(query[:WIKIPEDIA_MAX_QUERY_LENGTH])
        summaries = []
        for page_title in page_titles[: self.top_k_results]:


summaries = []
        for page_title in page_titles[: self.top_k_results]:
            if wiki_page := self._fetch_page(page_title):
                if summary := self._formatted_page_summary(page_title, wiki_page):
                    summaries.append(summary)
        if not summaries:
            return "No good Wikipedia Search Result was found"
        return "\n\n".join(summaries)[: self.doc_content_chars_max]
    @staticmethod
    def _formatted_page_summary(page_title: str, wiki_page: Any) -> Optional[str]:
        return f"Page: {page_title}\nSummary: {wiki_page.summary}"
    def _page_to_document(self, page_title: str, wiki_page: Any) -> Document:
        main_meta = {
            "title": page_title,
            "summary": wiki_page.summary,
            "source": wiki_page.url,
        }
        add_meta = (
            {
                "categories": wiki_page.categories,
                "page_url": wiki_page.url,
                "image_urls": wiki_page.images,
                "related_titles": wiki_page.links,
                "parent_id": wiki_page.parent_id,
                "references": wiki_page.references,
                "revision_id": wiki_page.revision_id,
                "sections": wiki_page.sections,
            }
            if self.load_all_available_meta
            else {}
        )
        doc = Document(
            page_content=wiki_page.content[: self.doc_content_chars_max],
            metadata={
                **main_meta,
                **add_meta,
            },
        )
        return doc
    def _fetch_page(self, page: str) -> Optional[str]:
        try:
            return self.wiki_client.page(title=page, auto_suggest=False)
        except (
            self.wiki_client.exceptions.PageError,


except (
            self.wiki_client.exceptions.PageError,
            self.wiki_client.exceptions.DisambiguationError,
        ):
            return None
[docs]    def load(self, query: str) -> List[Document]:
        """
        Run Wikipedia search and get the article text plus the meta information.
        See
        Returns: a list of documents.
        """
        page_titles = self.wiki_client.search(query[:WIKIPEDIA_MAX_QUERY_LENGTH])
        docs = []
        for page_title in page_titles[: self.top_k_results]:
            if wiki_page := self._fetch_page(page_title):
                if doc := self._page_to_document(page_title, wiki_page):
                    docs.append(doc)
        return docs
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.awslambda
"""Util that calls Lambda."""
import json
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
[docs]class LambdaWrapper(BaseModel):
    """Wrapper for AWS Lambda SDK.
    Docs for using:
    1. pip install boto3
    2. Create a lambda function using the AWS Console or CLI
    3. Run `aws configure` and enter your AWS credentials
    """
    lambda_client: Any  #: :meta private:
    function_name: Optional[str] = None
    awslambda_tool_name: Optional[str] = None
    awslambda_tool_description: Optional[str] = None
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that python package exists in environment."""
        try:
            import boto3
        except ImportError:
            raise ImportError(
                "boto3 is not installed. Please install it with `pip install boto3`"
            )
        values["lambda_client"] = boto3.client("lambda")
        values["function_name"] = values["function_name"]
        return values
[docs]    def run(self, query: str) -> str:
        """Invoke Lambda function and parse result."""
        res = self.lambda_client.invoke(
            FunctionName=self.function_name,
            InvocationType="RequestResponse",
            Payload=json.dumps({"body": query}),
        )
        try:
            payload_stream = res["Payload"]
            payload_string = payload_stream.read().decode("utf-8")
            answer = json.loads(payload_string)["body"]
        except StopIteration:


answer = json.loads(payload_string)["body"]
        except StopIteration:
            return "Failed to parse response from Lambda"
        if answer is None or answer == "":
            # We don't want to return the assumption alone if answer is empty
            return "Request failed."
        else:
            return f"Result: {answer}"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.google_serper
"""Util that calls Google Search using the Serper.dev API."""
from typing import Any, Dict, List, Optional
import aiohttp
import requests
from pydantic.class_validators import root_validator
from pydantic.main import BaseModel
from typing_extensions import Literal
from langchain.utils import get_from_dict_or_env
[docs]class GoogleSerperAPIWrapper(BaseModel):
    """Wrapper around the Serper.dev Google Search API.
    You can create a free API key at https://serper.dev.
    To use, you should have the environment variable ``SERPER_API_KEY``
    set with your API key, or pass `serper_api_key` as a named parameter
    to the constructor.
    Example:
        .. code-block:: python
            from langchain import GoogleSerperAPIWrapper
            google_serper = GoogleSerperAPIWrapper()
    """
    k: int = 10
    gl: str = "us"
    hl: str = "en"
    # "places" and "images" is available from Serper but not implemented in the
    # parser of run(). They can be used in results()
    type: Literal["news", "search", "places", "images"] = "search"
    result_key_for_type = {
        "news": "news",
        "places": "places",
        "images": "images",
        "search": "organic",
    }
    tbs: Optional[str] = None
    serper_api_key: Optional[str] = None
    aiosession: Optional[aiohttp.ClientSession] = None
    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
    @root_validator()


arbitrary_types_allowed = True
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key exists in environment."""
        serper_api_key = get_from_dict_or_env(
            values, "serper_api_key", "SERPER_API_KEY"
        )
        values["serper_api_key"] = serper_api_key
        return values
[docs]    def results(self, query: str, **kwargs: Any) -> Dict:
        """Run query through GoogleSearch."""
        return self._google_serper_api_results(
            query,
            gl=self.gl,
            hl=self.hl,
            num=self.k,
            tbs=self.tbs,
            search_type=self.type,
            **kwargs,
        )
[docs]    def run(self, query: str, **kwargs: Any) -> str:
        """Run query through GoogleSearch and parse result."""
        results = self._google_serper_api_results(
            query,
            gl=self.gl,
            hl=self.hl,
            num=self.k,
            tbs=self.tbs,
            search_type=self.type,
            **kwargs,
        )
        return self._parse_results(results)
[docs]    async def aresults(self, query: str, **kwargs: Any) -> Dict:
        """Run query through GoogleSearch."""
        results = await self._async_google_serper_search_results(
            query,
            gl=self.gl,
            hl=self.hl,
            num=self.k,
            search_type=self.type,
            tbs=self.tbs,
            **kwargs,
        )
        return results
[docs]    async def arun(self, query: str, **kwargs: Any) -> str:


"""Run query through GoogleSearch and parse result async."""
        results = await self._async_google_serper_search_results(
            query,
            gl=self.gl,
            hl=self.hl,
            num=self.k,
            search_type=self.type,
            tbs=self.tbs,
            **kwargs,
        )
        return self._parse_results(results)
    def _parse_snippets(self, results: dict) -> List[str]:
        snippets = []
        if results.get("answerBox"):
            answer_box = results.get("answerBox", {})
            if answer_box.get("answer"):
                return [answer_box.get("answer")]
            elif answer_box.get("snippet"):
                return [answer_box.get("snippet").replace("\n", " ")]
            elif answer_box.get("snippetHighlighted"):
                return answer_box.get("snippetHighlighted")
        if results.get("knowledgeGraph"):
            kg = results.get("knowledgeGraph", {})
            title = kg.get("title")
            entity_type = kg.get("type")
            if entity_type:
                snippets.append(f"{title}: {entity_type}.")
            description = kg.get("description")
            if description:
                snippets.append(description)
            for attribute, value in kg.get("attributes", {}).items():
                snippets.append(f"{title} {attribute}: {value}.")
        for result in results[self.result_key_for_type[self.type]][: self.k]:
            if "snippet" in result:
                snippets.append(result["snippet"])
            for attribute, value in result.get("attributes", {}).items():
                snippets.append(f"{attribute}: {value}.")
        if len(snippets) == 0:
            return ["No good Google Search Result was found"]
        return snippets
    def _parse_results(self, results: dict) -> str:


return snippets
    def _parse_results(self, results: dict) -> str:
        return " ".join(self._parse_snippets(results))
    def _google_serper_api_results(
        self, search_term: str, search_type: str = "search", **kwargs: Any
    ) -> dict:
        headers = {
            "X-API-KEY": self.serper_api_key or "",
            "Content-Type": "application/json",
        }
        params = {
            "q": search_term,
            **{key: value for key, value in kwargs.items() if value is not None},
        }
        response = requests.post(
            f"https://google.serper.dev/{search_type}", headers=headers, params=params
        )
        response.raise_for_status()
        search_results = response.json()
        return search_results
    async def _async_google_serper_search_results(
        self, search_term: str, search_type: str = "search", **kwargs: Any
    ) -> dict:
        headers = {
            "X-API-KEY": self.serper_api_key or "",
            "Content-Type": "application/json",
        }
        url = f"https://google.serper.dev/{search_type}"
        params = {
            "q": search_term,
            **{key: value for key, value in kwargs.items() if value is not None},
        }
        if not self.aiosession:
            async with aiohttp.ClientSession() as session:
                async with session.post(
                    url, params=params, headers=headers, raise_for_status=False
                ) as response:
                    search_results = await response.json()
        else:
            async with self.aiosession.post(
                url, params=params, headers=headers, raise_for_status=True


url, params=params, headers=headers, raise_for_status=True
            ) as response:
                search_results = await response.json()
        return search_results
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.wolfram_alpha
"""Util that calls WolframAlpha."""
from typing import Any, Dict, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class WolframAlphaAPIWrapper(BaseModel):
    """Wrapper for Wolfram Alpha.
    Docs for using:
    1. Go to wolfram alpha and sign up for a developer account
    2. Create an app and get your APP ID
    3. Save your APP ID into WOLFRAM_ALPHA_APPID env variable
    4. pip install wolframalpha
    """
    wolfram_client: Any  #: :meta private:
    wolfram_alpha_appid: Optional[str] = None
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        wolfram_alpha_appid = get_from_dict_or_env(
            values, "wolfram_alpha_appid", "WOLFRAM_ALPHA_APPID"
        )
        values["wolfram_alpha_appid"] = wolfram_alpha_appid
        try:
            import wolframalpha
        except ImportError:
            raise ImportError(
                "wolframalpha is not installed. "
                "Please install it with `pip install wolframalpha`"
            )
        client = wolframalpha.Client(wolfram_alpha_appid)
        values["wolfram_client"] = client
        return values
[docs]    def run(self, query: str) -> str:
        """Run query through WolframAlpha and parse result."""
        res = self.wolfram_client.query(query)


res = self.wolfram_client.query(query)
        try:
            assumption = next(res.pods).text
            answer = next(res.results).text
        except StopIteration:
            return "Wolfram Alpha wasn't able to answer it"
        if answer is None or answer == "":
            # We don't want to return the assumption alone if answer is empty
            return "No good Wolfram Alpha Result was found"
        else:
            return f"Assumption: {assumption} \nAnswer: {answer}"
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.bing_search
"""Util that calls Bing Search.
In order to set this up, follow instructions at:
https://levelup.gitconnected.com/api-tutorial-how-to-use-bing-web-search-api-in-python-4165d5592a7e
"""
from typing import Dict, List
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class BingSearchAPIWrapper(BaseModel):
    """Wrapper for Bing Search API.
    In order to set this up, follow instructions at:
    https://levelup.gitconnected.com/api-tutorial-how-to-use-bing-web-search-api-in-python-4165d5592a7e
    """
    bing_subscription_key: str
    bing_search_url: str
    k: int = 10
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    def _bing_search_results(self, search_term: str, count: int) -> List[dict]:
        headers = {"Ocp-Apim-Subscription-Key": self.bing_subscription_key}
        params = {
            "q": search_term,
            "count": count,
            "textDecorations": True,
            "textFormat": "HTML",
        }
        response = requests.get(
            self.bing_search_url, headers=headers, params=params  # type: ignore
        )
        response.raise_for_status()
        search_results = response.json()
        return search_results["webPages"]["value"]
    @root_validator(pre=True)
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and endpoint exists in environment."""
        bing_subscription_key = get_from_dict_or_env(


bing_subscription_key = get_from_dict_or_env(
            values, "bing_subscription_key", "BING_SUBSCRIPTION_KEY"
        )
        values["bing_subscription_key"] = bing_subscription_key
        bing_search_url = get_from_dict_or_env(
            values,
            "bing_search_url",
            "BING_SEARCH_URL",
            # default="https://api.bing.microsoft.com/v7.0/search",
        )
        values["bing_search_url"] = bing_search_url
        return values
[docs]    def run(self, query: str) -> str:
        """Run query through BingSearch and parse result."""
        snippets = []
        results = self._bing_search_results(query, count=self.k)
        if len(results) == 0:
            return "No good Bing Search Result was found"
        for result in results:
            snippets.append(result["snippet"])
        return " ".join(snippets)
[docs]    def results(self, query: str, num_results: int) -> List[Dict]:
        """Run query through BingSearch and return metadata.
        Args:
            query: The query to search for.
            num_results: The number of results to return.
        Returns:
            A list of dictionaries with the following keys:
                snippet - The description of the result.
                title - The title of the result.
                link - The link to the result.
        """
        metadata_results = []
        results = self._bing_search_results(query, count=num_results)
        if len(results) == 0:
            return [{"Result": "No good Bing Search Result was found"}]
        for result in results:
            metadata_result = {
                "snippet": result["snippet"],
                "title": result["name"],


"snippet": result["snippet"],
                "title": result["name"],
                "link": result["url"],
            }
            metadata_results.append(metadata_result)
        return metadata_results
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.powerbi
"""Wrapper around a Power BI endpoint."""
from __future__ import annotations
import asyncio
import logging
import os
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Union
import aiohttp
import requests
from aiohttp import ServerTimeoutError
from pydantic import BaseModel, Field, root_validator, validator
from requests.exceptions import Timeout
_LOGGER = logging.getLogger(__name__)
BASE_URL = os.getenv("POWERBI_BASE_URL", "https://api.powerbi.com/v1.0/myorg")
if TYPE_CHECKING:
    from azure.core.credentials import TokenCredential
[docs]class PowerBIDataset(BaseModel):
    """Create PowerBI engine from dataset ID and credential or token.
    Use either the credential or a supplied token to authenticate.
    If both are supplied the credential is used to generate a token.
    The impersonated_user_name is the UPN of a user to be impersonated.
    If the model is not RLS enabled, this will be ignored.
    """
    dataset_id: str
    table_names: List[str]
    group_id: Optional[str] = None
    credential: Optional[TokenCredential] = None
    token: Optional[str] = None
    impersonated_user_name: Optional[str] = None
    sample_rows_in_table_info: int = Field(default=1, gt=0, le=10)
    schemas: Dict[str, str] = Field(default_factory=dict)
    aiosession: Optional[aiohttp.ClientSession] = None
    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
    @validator("table_names", allow_reuse=True)
    def fix_table_names(cls, table_names: List[str]) -> List[str]:
        """Fix the table names."""


"""Fix the table names."""
        return [fix_table_name(table) for table in table_names]
    @root_validator(pre=True, allow_reuse=True)
    def token_or_credential_present(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        """Validate that at least one of token and credentials is present."""
        if "token" in values or "credential" in values:
            return values
        raise ValueError("Please provide either a credential or a token.")
    @property
    def request_url(self) -> str:
        """Get the request url."""
        if self.group_id:
            return f"{BASE_URL}/groups/{self.group_id}/datasets/{self.dataset_id}/executeQueries"  # noqa: E501 # pylint: disable=C0301
        return f"{BASE_URL}/datasets/{self.dataset_id}/executeQueries"  # noqa: E501 # pylint: disable=C0301
    @property
    def headers(self) -> Dict[str, str]:
        """Get the token."""
        if self.token:
            return {
                "Content-Type": "application/json",
                "Authorization": "Bearer " + self.token,
            }
        from azure.core.exceptions import (
            ClientAuthenticationError,  # pylint: disable=import-outside-toplevel
        )
        if self.credential:
            try:
                token = self.credential.get_token(
                    "https://analysis.windows.net/powerbi/api/.default"
                ).token
                return {
                    "Content-Type": "application/json",
                    "Authorization": "Bearer " + token,
                }
            except Exception as exc:  # pylint: disable=broad-exception-caught
                raise ClientAuthenticationError(
                    "Could not get a token from the supplied credentials."
                ) from exc


"Could not get a token from the supplied credentials."
                ) from exc
        raise ClientAuthenticationError("No credential or token supplied.")
[docs]    def get_table_names(self) -> Iterable[str]:
        """Get names of tables available."""
        return self.table_names
[docs]    def get_schemas(self) -> str:
        """Get the available schema's."""
        if self.schemas:
            return ", ".join([f"{key}: {value}" for key, value in self.schemas.items()])
        return "No known schema's yet. Use the schema_powerbi tool first."
    @property
    def table_info(self) -> str:
        """Information about all tables in the database."""
        return self.get_table_info()
    def _get_tables_to_query(
        self, table_names: Optional[Union[List[str], str]] = None
    ) -> Optional[List[str]]:
        """Get the tables names that need to be queried, after checking they exist."""
        if table_names is not None:
            if (
                isinstance(table_names, list)
                and len(table_names) > 0
                and table_names[0] != ""
            ):
                fixed_tables = [fix_table_name(table) for table in table_names]
                non_existing_tables = [
                    table for table in fixed_tables if table not in self.table_names
                ]
                if non_existing_tables:
                    _LOGGER.warning(
                        "Table(s) %s not found in dataset.",
                        ", ".join(non_existing_tables),
                    )
                tables = [
                    table for table in fixed_tables if table not in non_existing_tables
                ]
                return tables if tables else None
            if isinstance(table_names, str) and table_names != "":
                if table_names not in self.table_names:


if table_names not in self.table_names:
                    _LOGGER.warning("Table %s not found in dataset.", table_names)
                    return None
                return [fix_table_name(table_names)]
        return self.table_names
    def _get_tables_todo(self, tables_todo: List[str]) -> List[str]:
        """Get the tables that still need to be queried."""
        return [table for table in tables_todo if table not in self.schemas]
    def _get_schema_for_tables(self, table_names: List[str]) -> str:
        """Create a string of the table schemas for the supplied tables."""
        schemas = [
            schema for table, schema in self.schemas.items() if table in table_names
        ]
        return ", ".join(schemas)
[docs]    def get_table_info(
        self, table_names: Optional[Union[List[str], str]] = None
    ) -> str:
        """Get information about specified tables."""
        tables_requested = self._get_tables_to_query(table_names)
        if tables_requested is None:
            return "No (valid) tables requested."
        tables_todo = self._get_tables_todo(tables_requested)
        for table in tables_todo:
            self._get_schema(table)
        return self._get_schema_for_tables(tables_requested)
[docs]    async def aget_table_info(
        self, table_names: Optional[Union[List[str], str]] = None
    ) -> str:
        """Get information about specified tables."""
        tables_requested = self._get_tables_to_query(table_names)
        if tables_requested is None:
            return "No (valid) tables requested."
        tables_todo = self._get_tables_todo(tables_requested)
        await asyncio.gather(*[self._aget_schema(table) for table in tables_todo])


await asyncio.gather(*[self._aget_schema(table) for table in tables_todo])
        return self._get_schema_for_tables(tables_requested)
    def _get_schema(self, table: str) -> None:
        """Get the schema for a table."""
        try:
            result = self.run(
                f"EVALUATE TOPN({self.sample_rows_in_table_info}, {table})"
            )
            self.schemas[table] = json_to_md(result["results"][0]["tables"][0]["rows"])
        except Timeout:
            _LOGGER.warning("Timeout while getting table info for %s", table)
            self.schemas[table] = "unknown"
        except Exception as exc:  # pylint: disable=broad-exception-caught
            _LOGGER.warning("Error while getting table info for %s: %s", table, exc)
            self.schemas[table] = "unknown"
    async def _aget_schema(self, table: str) -> None:
        """Get the schema for a table."""
        try:
            result = await self.arun(
                f"EVALUATE TOPN({self.sample_rows_in_table_info}, {table})"
            )
            self.schemas[table] = json_to_md(result["results"][0]["tables"][0]["rows"])
        except ServerTimeoutError:
            _LOGGER.warning("Timeout while getting table info for %s", table)
            self.schemas[table] = "unknown"
        except Exception as exc:  # pylint: disable=broad-exception-caught
            _LOGGER.warning("Error while getting table info for %s: %s", table, exc)
            self.schemas[table] = "unknown"
    def _create_json_content(self, command: str) -> dict[str, Any]:


def _create_json_content(self, command: str) -> dict[str, Any]:
        """Create the json content for the request."""
        return {
            "queries": [{"query": rf"{command}"}],
            "impersonatedUserName": self.impersonated_user_name,
            "serializerSettings": {"includeNulls": True},
        }
[docs]    def run(self, command: str) -> Any:
        """Execute a DAX command and return a json representing the results."""
        _LOGGER.debug("Running command: %s", command)
        result = requests.post(
            self.request_url,
            json=self._create_json_content(command),
            headers=self.headers,
            timeout=10,
        )
        return result.json()
[docs]    async def arun(self, command: str) -> Any:
        """Execute a DAX command and return the result asynchronously."""
        _LOGGER.debug("Running command: %s", command)
        if self.aiosession:
            async with self.aiosession.post(
                self.request_url,
                headers=self.headers,
                json=self._create_json_content(command),
                timeout=10,
            ) as response:
                response_json = await response.json()
                return response_json
        async with aiohttp.ClientSession() as session:
            async with session.post(
                self.request_url,
                headers=self.headers,
                json=self._create_json_content(command),
                timeout=10,
            ) as response:
                response_json = await response.json()
                return response_json
def json_to_md(
    json_contents: List[Dict[str, Union[str, int, float]]],
    table_name: Optional[str] = None,
) -> str:
    """Converts a JSON object to a markdown table."""


) -> str:
    """Converts a JSON object to a markdown table."""
    output_md = ""
    headers = json_contents[0].keys()
    for header in headers:
        header.replace("[", ".").replace("]", "")
        if table_name:
            header.replace(f"{table_name}.", "")
        output_md += f"| {header} "
    output_md += "|\n"
    for row in json_contents:
        for value in row.values():
            output_md += f"| {value} "
        output_md += "|\n"
    return output_md
def fix_table_name(table: str) -> str:
    """Add single quotes around table names that contain spaces."""
    if " " in table and not table.startswith("'") and not table.endswith("'"):
        return f"'{table}'"
    return table
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.openweathermap
"""Util that calls OpenWeatherMap using PyOWM."""
from typing import Any, Dict, Optional
from pydantic import Extra, root_validator
from langchain.tools.base import BaseModel
from langchain.utils import get_from_dict_or_env
[docs]class OpenWeatherMapAPIWrapper(BaseModel):
    """Wrapper for OpenWeatherMap API using PyOWM.
    Docs for using:
    1. Go to OpenWeatherMap and sign up for an API key
    2. Save your API KEY into OPENWEATHERMAP_API_KEY env variable
    3. pip install pyowm
    """
    owm: Any
    openweathermap_api_key: Optional[str] = None
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator(pre=True)
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key exists in environment."""
        openweathermap_api_key = get_from_dict_or_env(
            values, "openweathermap_api_key", "OPENWEATHERMAP_API_KEY"
        )
        try:
            import pyowm
        except ImportError:
            raise ImportError(
                "pyowm is not installed. Please install it with `pip install pyowm`"
            )
        owm = pyowm.OWM(openweathermap_api_key)
        values["owm"] = owm
        return values
    def _format_weather_info(self, location: str, w: Any) -> str:
        detailed_status = w.detailed_status
        wind = w.wind()
        humidity = w.humidity
        temperature = w.temperature("celsius")
        rain = w.rain
        heat_index = w.heat_index
        clouds = w.clouds


heat_index = w.heat_index
        clouds = w.clouds
        return (
            f"In {location}, the current weather is as follows:\n"
            f"Detailed status: {detailed_status}\n"
            f"Wind speed: {wind['speed']} m/s, direction: {wind['deg']}°\n"
            f"Humidity: {humidity}%\n"
            f"Temperature: \n"
            f"  - Current: {temperature['temp']}°C\n"
            f"  - High: {temperature['temp_max']}°C\n"
            f"  - Low: {temperature['temp_min']}°C\n"
            f"  - Feels like: {temperature['feels_like']}°C\n"
            f"Rain: {rain}\n"
            f"Heat index: {heat_index}\n"
            f"Cloud cover: {clouds}%"
        )
[docs]    def run(self, location: str) -> str:
        """Get the current weather information for a specified location."""
        mgr = self.owm.weather_manager()
        observation = mgr.weather_at_place(location)
        w = observation.weather
        return self._format_weather_info(location, w)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 2023.


Source code for langchain.utilities.metaphor_search
"""Util that calls Metaphor Search API.
In order to set this up, follow instructions at:
"""
import json
from typing import Dict, List
import aiohttp
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
METAPHOR_API_URL = "https://api.metaphor.systems"
[docs]class MetaphorSearchAPIWrapper(BaseModel):
    """Wrapper for Metaphor Search API."""
    metaphor_api_key: str
    k: int = 10
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    def _metaphor_search_results(self, query: str, num_results: int) -> List[dict]:
        headers = {"X-Api-Key": self.metaphor_api_key}
        params = {"numResults": num_results, "query": query}
        response = requests.post(
            # type: ignore
            f"{METAPHOR_API_URL}/search",
            headers=headers,
            json=params,
        )
        response.raise_for_status()
        search_results = response.json()
        print(search_results)
        return search_results["results"]
    @root_validator(pre=True)
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and endpoint exists in environment."""
        metaphor_api_key = get_from_dict_or_env(
            values, "metaphor_api_key", "METAPHOR_API_KEY"
        )
        values["metaphor_api_key"] = metaphor_api_key
        return values
[docs]    def results(self, query: str, num_results: int) -> List[Dict]:


"""Run query through Metaphor Search and return metadata.
        Args:
            query: The query to search for.
            num_results: The number of results to return.
        Returns:
            A list of dictionaries with the following keys:
                title - The title of the
                url - The url
                author - Author of the content, if applicable. Otherwise, None.
                date_created - Estimated date created,
                    in YYYY-MM-DD format. Otherwise, None.
        """
        raw_search_results = self._metaphor_search_results(
            query, num_results=num_results
        )
        return self._clean_results(raw_search_results)
[docs]    async def results_async(self, query: str, num_results: int) -> List[Dict]:
        """Get results from the Metaphor Search API asynchronously."""
        # Function to perform the API call
        async def fetch() -> str:
            headers = {"X-Api-Key": self.metaphor_api_key}
            params = {"numResults": num_results, "query": query}
            async with aiohttp.ClientSession() as session:
                async with session.post(
                    f"{METAPHOR_API_URL}/search", json=params, headers=headers
                ) as res:
                    if res.status == 200:
                        data = await res.text()
                        return data
                    else:
                        raise Exception(f"Error {res.status}: {res.reason}")
        results_json_str = await fetch()
        results_json = json.loads(results_json_str)
        return self._clean_results(results_json["results"])
    def _clean_results(self, raw_search_results: List[Dict]) -> List[Dict]:
        cleaned_results = []
        for result in raw_search_results:
            cleaned_results.append(
                {


for result in raw_search_results:
            cleaned_results.append(
                {
                    "title": result["title"],
                    "url": result["url"],
                    "author": result["author"],
                    "date_created": result["dateCreated"],
                }
            )
        return cleaned_results
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 25, 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: