viruPVS/langchain-docs huggingface dataset:
def args(self) -> dict:
        """The tool's input arguments."""
        return self.args_schema.schema()["properties"]
    def _run(
        self,
        *args: Any,
        run_manager: Optional[CallbackManagerForToolRun] = None,
        **kwargs: Any,
    ) -> Any:
        """Use the tool."""
        if self.func:
            new_argument_supported = signature(self.func).parameters.get("callbacks")
            return (
                self.func(
                    *args,
                    callbacks=run_manager.get_child() if run_manager else None,
                    **kwargs,
                )
                if new_argument_supported
                else self.func(*args, **kwargs)
            )
        raise NotImplementedError("Tool does not support sync")
    async def _arun(
        self,
        *args: Any,
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
        **kwargs: Any,
    ) -> str:
        """Use the tool asynchronously."""
        if self.coroutine:
            new_argument_supported = signature(self.coroutine).parameters.get(
                "callbacks"
            )
            return (
                await self.coroutine(
                    *args,
                    callbacks=run_manager.get_child() if run_manager else None,
                    **kwargs,
                )
                if new_argument_supported
                else await self.coroutine(*args, **kwargs)
            )
        return await asyncio.get_running_loop().run_in_executor(
            None,
            partial(self._run, run_manager=run_manager, **kwargs),
            *args,
        )
[docs]    @classmethod
    def from_function(
        cls,
        func: Optional[Callable] = None,
cls,
        func: Optional[Callable] = None,
        coroutine: Optional[Callable[..., Awaitable[Any]]] = None,
        name: Optional[str] = None,
        description: Optional[str] = None,
        return_direct: bool = False,
        args_schema: Optional[Type[BaseModel]] = None,
        infer_schema: bool = True,
        **kwargs: Any,
    ) -> StructuredTool:
        """Create tool from a given function.
        A classmethod that helps to create a tool from a function.
        Args:
            func: The function from which to create a tool
            coroutine: The async function from which to create a tool
            name: The name of the tool. Defaults to the function name
            description: The description of the tool. Defaults to the function docstring
            return_direct: Whether to return the result directly or as a callback
            args_schema: The schema of the tool's input arguments
            infer_schema: Whether to infer the schema from the function's signature
            **kwargs: Additional arguments to pass to the tool
        Returns:
            The tool
        Examples:
            .. code-block:: python
                def add(a: int, b: int) -> int:
                    \"\"\"Add two numbers\"\"\"
                    return a + b
                tool = StructuredTool.from_function(add)
                tool.run(1, 2) # 3
        """
        if func is not None:
            source_function = func
        elif coroutine is not None:
            source_function = coroutine
        else:
            raise ValueError("Function and/or coroutine must be provided")
        name = name or source_function.__name__
        description = description or source_function.__doc__
        if description is None:
            raise ValueError(
if description is None:
            raise ValueError(
                "Function must have a docstring if description not provided."
            )
        # Description example:
        # search_api(query: str) - Searches the API for the query.
        sig = signature(source_function)
        description = f"{name}{sig} - {description.strip()}"
        _args_schema = args_schema
        if _args_schema is None and infer_schema:
            _args_schema = create_schema_from_function(f"{name}Schema", source_function)
        return cls(
            name=name,
            func=func,
            coroutine=coroutine,
            args_schema=_args_schema,
            description=description,
            return_direct=return_direct,
            **kwargs,
        )
[docs]def tool(
    *args: Union[str, Callable, Runnable],
    return_direct: bool = False,
    args_schema: Optional[Type[BaseModel]] = None,
    infer_schema: bool = True,
) -> Callable:
    """Make tools out of functions, can be used with or without arguments.
    Args:
        *args: The arguments to the tool.
        return_direct: Whether to return directly from the tool rather
            than continuing the agent loop.
        args_schema: optional argument schema for user to specify
        infer_schema: Whether to infer the schema of the arguments from
            the function's signature. This also makes the resultant tool
            accept a dictionary input to its `run()` function.
    Requires:
        - Function must be of type (str) -> str
        - Function must have a docstring
    Examples:
        .. code-block:: python
            @tool
            def search_api(query: str) -> str:
                # Searches the API for the query.
                return
# Searches the API for the query.
                return
            @tool("search", return_direct=True)
            def search_api(query: str) -> str:
                # Searches the API for the query.
                return
    """
    def _make_with_name(tool_name: str) -> Callable:
        def _make_tool(dec_func: Union[Callable, Runnable]) -> BaseTool:
            if isinstance(dec_func, Runnable):
                runnable = dec_func
                if runnable.input_schema.schema().get("type") != "object":
                    raise ValueError("Runnable must have an object schema.")
                async def ainvoke_wrapper(
                    callbacks: Optional[Callbacks] = None, **kwargs: Any
                ) -> Any:
                    return await runnable.ainvoke(kwargs, {"callbacks": callbacks})
                def invoke_wrapper(
                    callbacks: Optional[Callbacks] = None, **kwargs: Any
                ) -> Any:
                    return runnable.invoke(kwargs, {"callbacks": callbacks})
                coroutine = ainvoke_wrapper
                func = invoke_wrapper
                schema: Optional[Type[BaseModel]] = runnable.input_schema
                description = repr(runnable)
            elif inspect.iscoroutinefunction(dec_func):
                coroutine = dec_func
                func = None
                schema = args_schema
                description = None
            else:
                coroutine = None
                func = dec_func
                schema = args_schema
                description = None
            if infer_schema or args_schema is not None:
                return StructuredTool.from_function(
                    func,
                    coroutine,
                    name=tool_name,
                    description=description,
                    return_direct=return_direct,
                    args_schema=schema,
                    infer_schema=infer_schema,
                )
            # If someone doesn't want a schema applied, we must treat it as
)
            # If someone doesn't want a schema applied, we must treat it as
            # a simple string->string function
            if func.__doc__ is None:
                raise ValueError(
                    "Function must have a docstring if "
                    "description not provided and infer_schema is False."
                )
            return Tool(
                name=tool_name,
                func=func,
                description=f"{tool_name} tool",
                return_direct=return_direct,
                coroutine=coroutine,
            )
        return _make_tool
    if len(args) == 2 and isinstance(args[0], str) and isinstance(args[1], Runnable):
        return _make_with_name(args[0])(args[1])
    elif len(args) == 1 and isinstance(args[0], str):
        # if the argument is a string, then we use the string as the tool name
        # Example usage: @tool("search", return_direct=True)
        return _make_with_name(args[0])
    elif len(args) == 1 and callable(args[0]):
        # if the argument is a function, then we use the function name as the tool name
        # Example usage: @tool
        return _make_with_name(args[0].__name__)(args[0])
    elif len(args) == 0:
        # if there are no arguments, then we use the function name as the tool name
        # Example usage: @tool(return_direct=True)
        def _partial(func: Callable[[str], str]) -> BaseTool:
            return _make_with_name(func.__name__)(func)
        return _partial
    else:
        raise ValueError("Too many arguments for tool decorator")
Source code for langchain_core.example_selectors.base
"""Interface for selecting examples to include in prompts."""
from abc import ABC, abstractmethod
from typing import Any, Dict, List
[docs]class BaseExampleSelector(ABC):
    """Interface for selecting examples to include in prompts."""
[docs]    @abstractmethod
    def add_example(self, example: Dict[str, str]) -> Any:
        """Add new example to store for a key."""
[docs]    @abstractmethod
    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
        """Select which examples to use based on the inputs."""
Source code for langchain_core.example_selectors.length_based
"""Select examples based on length."""
import re
from typing import Callable, Dict, List
from langchain_core.example_selectors.base import BaseExampleSelector
from langchain_core.prompts.prompt import PromptTemplate
from langchain_core.pydantic_v1 import BaseModel, validator
def _get_length_based(text: str) -> int:
    return len(re.split("\n| ", text))
[docs]class LengthBasedExampleSelector(BaseExampleSelector, BaseModel):
    """Select examples based on length."""
    examples: List[dict]
    """A list of the examples that the prompt template expects."""
    example_prompt: PromptTemplate
    """Prompt template used to format the examples."""
    get_text_length: Callable[[str], int] = _get_length_based
    """Function to measure prompt length. Defaults to word count."""
    max_length: int = 2048
    """Max length for the prompt, beyond which examples are cut."""
    example_text_lengths: List[int] = []  #: :meta private:
[docs]    def add_example(self, example: Dict[str, str]) -> None:
        """Add new example to list."""
        self.examples.append(example)
        string_example = self.example_prompt.format(**example)
        self.example_text_lengths.append(self.get_text_length(string_example))
    @validator("example_text_lengths", always=True)
    def calculate_example_text_lengths(cls, v: List[int], values: Dict) -> List[int]:
        """Calculate text lengths if they don't exist."""
        # Check if text lengths were passed in
        if v:
            return v
        # If they were not, calculate them
        example_prompt = values["example_prompt"]
        get_text_length = values["get_text_length"]
get_text_length = values["get_text_length"]
        string_examples = [example_prompt.format(**eg) for eg in values["examples"]]
        return [get_text_length(eg) for eg in string_examples]
[docs]    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
        """Select which examples to use based on the input lengths."""
        inputs = " ".join(input_variables.values())
        remaining_length = self.max_length - self.get_text_length(inputs)
        i = 0
        examples = []
        while remaining_length > 0 and i < len(self.examples):
            new_length = remaining_length - self.example_text_lengths[i]
            if new_length < 0:
                break
            else:
                examples.append(self.examples[i])
                remaining_length = new_length
            i += 1
        return examples
Source code for langchain_core.example_selectors.semantic_similarity
"""Example selector that selects examples based on SemanticSimilarity."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Type
from langchain_core.example_selectors.base import BaseExampleSelector
from langchain_core.pydantic_v1 import BaseModel, Extra
from langchain_core.vectorstores import VectorStore
if TYPE_CHECKING:
    from langchain_core.embeddings import Embeddings
[docs]def sorted_values(values: Dict[str, str]) -> List[Any]:
    """Return a list of values in dict sorted by key."""
    return [values[val] for val in sorted(values)]
[docs]class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel):
    """Example selector that selects examples based on SemanticSimilarity."""
    vectorstore: VectorStore
    """VectorStore than contains information about examples."""
    k: int = 4
    """Number of examples to select."""
    example_keys: Optional[List[str]] = None
    """Optional keys to filter examples to."""
    input_keys: Optional[List[str]] = None
    """Optional keys to filter input to. If provided, the search is based on
    the input variables instead of all variables."""
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
[docs]    def add_example(self, example: Dict[str, str]) -> str:
        """Add new example to vectorstore."""
        if self.input_keys:
            string_example = " ".join(
                sorted_values({key: example[key] for key in self.input_keys})
            )
        else:
            string_example = " ".join(sorted_values(example))
)
        else:
            string_example = " ".join(sorted_values(example))
        ids = self.vectorstore.add_texts([string_example], metadatas=[example])
        return ids[0]
[docs]    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
        """Select which examples to use based on semantic similarity."""
        # Get the docs with the highest similarity.
        if self.input_keys:
            input_variables = {key: input_variables[key] for key in self.input_keys}
        query = " ".join(sorted_values(input_variables))
        example_docs = self.vectorstore.similarity_search(query, k=self.k)
        # Get the examples from the metadata.
        # This assumes that examples are stored in metadata.
        examples = [dict(e.metadata) for e in example_docs]
        # If example keys are provided, filter examples to those keys.
        if self.example_keys:
            examples = [{k: eg[k] for k in self.example_keys} for eg in examples]
        return examples
[docs]    @classmethod
    def from_examples(
        cls,
        examples: List[dict],
        embeddings: Embeddings,
        vectorstore_cls: Type[VectorStore],
        k: int = 4,
        input_keys: Optional[List[str]] = None,
        **vectorstore_cls_kwargs: Any,
    ) -> SemanticSimilarityExampleSelector:
        """Create k-shot example selector using example list and embeddings.
        Reshuffles examples dynamically based on query similarity.
        Args:
            examples: List of examples to use in the prompt.
            embeddings: An initialized embedding API interface, e.g. OpenAIEmbeddings().
            vectorstore_cls: A vector store DB interface class, e.g. FAISS.
vectorstore_cls: A vector store DB interface class, e.g. FAISS.
            k: Number of examples to select
            input_keys: If provided, the search is based on the input variables
                instead of all variables.
            vectorstore_cls_kwargs: optional kwargs containing url for vector store
        Returns:
            The ExampleSelector instantiated, backed by a vector store.
        """
        if input_keys:
            string_examples = [
                " ".join(sorted_values({k: eg[k] for k in input_keys}))
                for eg in examples
            ]
        else:
            string_examples = [" ".join(sorted_values(eg)) for eg in examples]
        vectorstore = vectorstore_cls.from_texts(
            string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
        )
        return cls(vectorstore=vectorstore, k=k, input_keys=input_keys)
[docs]class MaxMarginalRelevanceExampleSelector(SemanticSimilarityExampleSelector):
    """ExampleSelector that selects examples based on Max Marginal Relevance.
    This was shown to improve performance in this paper:
    https://arxiv.org/pdf/2211.13892.pdf
    """
    fetch_k: int = 20
    """Number of examples to fetch to rerank."""
[docs]    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
        """Select which examples to use based on semantic similarity."""
        # Get the docs with the highest similarity.
        if self.input_keys:
            input_variables = {key: input_variables[key] for key in self.input_keys}
        query = " ".join(sorted_values(input_variables))
        example_docs = self.vectorstore.max_marginal_relevance_search(
            query, k=self.k, fetch_k=self.fetch_k
query, k=self.k, fetch_k=self.fetch_k
        )
        # Get the examples from the metadata.
        # This assumes that examples are stored in metadata.
        examples = [dict(e.metadata) for e in example_docs]
        # If example keys are provided, filter examples to those keys.
        if self.example_keys:
            examples = [{k: eg[k] for k in self.example_keys} for eg in examples]
        return examples
[docs]    @classmethod
    def from_examples(
        cls,
        examples: List[dict],
        embeddings: Embeddings,
        vectorstore_cls: Type[VectorStore],
        k: int = 4,
        input_keys: Optional[List[str]] = None,
        fetch_k: int = 20,
        **vectorstore_cls_kwargs: Any,
    ) -> MaxMarginalRelevanceExampleSelector:
        """Create k-shot example selector using example list and embeddings.
        Reshuffles examples dynamically based on query similarity.
        Args:
            examples: List of examples to use in the prompt.
            embeddings: An iniialized embedding API interface, e.g. OpenAIEmbeddings().
            vectorstore_cls: A vector store DB interface class, e.g. FAISS.
            k: Number of examples to select
            input_keys: If provided, the search is based on the input variables
                instead of all variables.
            vectorstore_cls_kwargs: optional kwargs containing url for vector store
        Returns:
            The ExampleSelector instantiated, backed by a vector store.
        """
        if input_keys:
            string_examples = [
                " ".join(sorted_values({k: eg[k] for k in input_keys}))
                for eg in examples
            ]
        else:
for eg in examples
            ]
        else:
            string_examples = [" ".join(sorted_values(eg)) for eg in examples]
        vectorstore = vectorstore_cls.from_texts(
            string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
        )
        return cls(vectorstore=vectorstore, k=k, fetch_k=fetch_k, input_keys=input_keys)
Source code for langchain_core.runnables.base
from __future__ import annotations
import asyncio
import inspect
import threading
from abc import ABC, abstractmethod
from concurrent.futures import FIRST_COMPLETED, wait
from copy import deepcopy
from functools import partial, wraps
from itertools import groupby, tee
from operator import itemgetter
from typing import (
    TYPE_CHECKING,
    Any,
    AsyncIterator,
    Awaitable,
    Callable,
    Dict,
    Generic,
    Iterator,
    List,
    Mapping,
    Optional,
    Sequence,
    Set,
    Tuple,
    Type,
    TypeVar,
    Union,
    cast,
    overload,
)
from typing_extensions import Literal, get_args
from langchain_core.load.dump import dumpd, dumps
from langchain_core.load.serializable import Serializable
from langchain_core.pydantic_v1 import BaseModel, Field, create_model
from langchain_core.runnables.config import (
    RunnableConfig,
    acall_func_with_variable_args,
    call_func_with_variable_args,
    ensure_config,
    get_async_callback_manager_for_config,
    get_callback_manager_for_config,
    get_config_list,
    get_executor_for_config,
    merge_configs,
    patch_config,
)
from langchain_core.runnables.utils import (
    AddableDict,
    AnyConfigurableField,
    ConfigurableField,
    ConfigurableFieldSpec,
    Input,
    Output,
    accepts_config,
    accepts_run_manager,
    gather_with_concurrency,
    get_function_first_arg_dict_keys,
    get_lambda_source,
    get_unique_config_specs,
    indent_lines_after_first,
)
from langchain_core.utils.aiter import atee, py_anext
)
from langchain_core.utils.aiter import atee, py_anext
from langchain_core.utils.iter import safetee
if TYPE_CHECKING:
    from langchain_core.callbacks.manager import (
        AsyncCallbackManagerForChainRun,
        CallbackManagerForChainRun,
    )
    from langchain_core.runnables.fallbacks import (
        RunnableWithFallbacks as RunnableWithFallbacksT,
    )
    from langchain_core.tracers.log_stream import RunLog, RunLogPatch
    from langchain_core.tracers.root_listeners import Listener
Other = TypeVar("Other")
[docs]class Runnable(Generic[Input, Output], ABC):
    """A unit of work that can be invoked, batched, streamed, transformed and composed.
     Key Methods
     ===========
    * invoke/ainvoke: Transforms a single input into an output.
    * batch/abatch: Efficiently transforms multiple inputs into outputs.
    * stream/astream: Streams output from a single input as it's produced.
    * astream_log: Streams output and selected intermediate results from an input.
    Built-in optimizations:
    * Batch: By default, batch runs invoke() in parallel using a thread pool executor.
             Override to optimize batching.
    * Async: Methods with "a" suffix are asynchronous. By default, they execute
             the sync counterpart using asyncio's thread pool.
             Override for native async.
    All methods accept an optional config argument, which can be used to configure
    execution, add tags and metadata for tracing and debugging etc.
    Runnables expose schematic information about their input, output and config via
    the input_schema property, the output_schema property and config_schema method.
    LCEL and Composition
    ====================
LCEL and Composition
    ====================
    The LangChain Expression Language (LCEL) is a declarative way to compose Runnables
    into chains. Any chain constructed this way will automatically have sync, async,
    batch, and streaming support.
    The main composition primitives are RunnableSequence and RunnableParallel.
    RunnableSequence invokes a series of runnables sequentially, with one runnable's
    output serving as the next's input. Construct using the `|` operator or by
    passing a list of runnables to RunnableSequence.
    RunnableParallel invokes runnables concurrently, providing the same input
    to each. Construct it using a dict literal within a sequence or by passing a
    dict to RunnableParallel.
    For example,
    .. code-block:: python
        from langchain_core.runnables import RunnableLambda
        # A RunnableSequence constructed using the `|` operator
        sequence = RunnableLambda(lambda x: x + 1) | RunnableLambda(lambda x: x * 2)
        sequence.invoke(1) # 4
        sequence.batch([1, 2, 3]) # [4, 6, 8]
        # A sequence that contains a RunnableParallel constructed using a dict literal
        sequence = RunnableLambda(lambda x: x + 1) | {
            'mul_2': RunnableLambda(lambda x: x * 2),
            'mul_5': RunnableLambda(lambda x: x * 5)
        }
        sequence.invoke(1) # {'mul_2': 4, 'mul_5': 10}
    Standard Methods
    ================
    All Runnables expose additional methods that can be used to modify their behavior
    (e.g., add a retry policy, add lifecycle listeners, make them configurable, etc.).