Source code for langchain_core.documents.base

from __future__ import annotations

import contextlib
import mimetypes
from collections.abc import Generator
from io import BufferedReader, BytesIO
from pathlib import PurePath
from typing import Any, Literal, Optional, Union, cast

from pydantic import ConfigDict, Field, field_validator, model_validator

from langchain_core.load.serializable import Serializable

PathLike = Union[str, PurePath]


[docs] class BaseMedia(Serializable): """Use to represent media content. Media objects can be used to represent raw data, such as text or binary data. LangChain Media objects allow associating metadata and an optional identifier with the content. The presence of an ID and metadata make it easier to store, index, and search over the content in a structured way. """ # The ID field is optional at the moment. # It will likely become required in a future major release after # it has been adopted by enough vectorstore implementations. id: Optional[str] = None """An optional identifier for the document. Ideally this should be unique across the document collection and formatted as a UUID, but this will not be enforced. .. versionadded:: 0.2.11 """ metadata: dict = Field(default_factory=dict) """Arbitrary metadata associated with the content.""" @field_validator("id", mode="before") def cast_id_to_str(cls, id_value: Any) -> Optional[str]: if id_value is not None: return str(id_value) else: return id_value
[docs] class Blob(BaseMedia): """Blob represents raw data by either reference or value. Provides an interface to materialize the blob in different representations, and help to decouple the development of data loaders from the downstream parsing of the raw data. Inspired by: https://developer.mozilla.org/en-US/docs/Web/API/Blob Example: Initialize a blob from in-memory data .. code-block:: python from langchain_core.documents import Blob blob = Blob.from_data("Hello, world!") # Read the blob as a string print(blob.as_string()) # Read the blob as bytes print(blob.as_bytes()) # Read the blob as a byte stream with blob.as_bytes_io() as f: print(f.read()) Example: Load from memory and specify mime-type and metadata .. code-block:: python from langchain_core.documents import Blob blob = Blob.from_data( data="Hello, world!", mime_type="text/plain", metadata={"source": "https://example.com"} ) Example: Load the blob from a file .. code-block:: python from langchain_core.documents import Blob blob = Blob.from_path("path/to/file.txt") # Read the blob as a string print(blob.as_string()) # Read the blob as bytes print(blob.as_bytes()) # Read the blob as a byte stream with blob.as_bytes_io() as f: print(f.read()) """ data: Union[bytes, str, None] = None """Raw data associated with the blob.""" mimetype: Optional[str] = None """MimeType not to be confused with a file extension.""" encoding: str = "utf-8" """Encoding to use if decoding the bytes into a string. Use utf-8 as default encoding, if decoding to string. """ path: Optional[PathLike] = None """Location where the original content was found.""" model_config = ConfigDict( arbitrary_types_allowed=True, frozen=True, ) @property def source(self) -> Optional[str]: """The source location of the blob as string if known otherwise none. If a path is associated with the blob, it will default to the path location. Unless explicitly set via a metadata field called "source", in which case that value will be used instead. """ if self.metadata and "source" in self.metadata: return cast(Optional[str], self.metadata["source"]) return str(self.path) if self.path else None @model_validator(mode="before") @classmethod def check_blob_is_valid(cls, values: dict[str, Any]) -> Any: """Verify that either data or path is provided.""" if "data" not in values and "path" not in values: msg = "Either data or path must be provided" raise ValueError(msg) return values
[docs] def as_string(self) -> str: """Read data as a string.""" if self.data is None and self.path: with open(str(self.path), encoding=self.encoding) as f: return f.read() elif isinstance(self.data, bytes): return self.data.decode(self.encoding) elif isinstance(self.data, str): return self.data else: msg = f"Unable to get string for blob {self}" raise ValueError(msg)
[docs] def as_bytes(self) -> bytes: """Read data as bytes.""" if isinstance(self.data, bytes): return self.data elif isinstance(self.data, str): return self.data.encode(self.encoding) elif self.data is None and self.path: with open(str(self.path), "rb") as f: return f.read() else: msg = f"Unable to get bytes for blob {self}" raise ValueError(msg)
[docs] @contextlib.contextmanager def as_bytes_io(self) -> Generator[Union[BytesIO, BufferedReader], None, None]: """Read data as a byte stream.""" if isinstance(self.data, bytes): yield BytesIO(self.data) elif self.data is None and self.path: with open(str(self.path), "rb") as f: yield f else: msg = f"Unable to convert blob {self}" raise NotImplementedError(msg)
[docs] @classmethod def from_path( cls, path: PathLike, *, encoding: str = "utf-8", mime_type: Optional[str] = None, guess_type: bool = True, metadata: Optional[dict] = None, ) -> Blob: """Load the blob from a path like object. Args: path: path like object to file to be read encoding: Encoding to use if decoding the bytes into a string mime_type: if provided, will be set as the mime-type of the data guess_type: If True, the mimetype will be guessed from the file extension, if a mime-type was not provided metadata: Metadata to associate with the blob Returns: Blob instance """ if mime_type is None and guess_type: _mimetype = mimetypes.guess_type(path)[0] if guess_type else None else: _mimetype = mime_type # We do not load the data immediately, instead we treat the blob as a # reference to the underlying data. return cls( data=None, mimetype=_mimetype, encoding=encoding, path=path, metadata=metadata if metadata is not None else {}, )
[docs] @classmethod def from_data( cls, data: Union[str, bytes], *, encoding: str = "utf-8", mime_type: Optional[str] = None, path: Optional[str] = None, metadata: Optional[dict] = None, ) -> Blob: """Initialize the blob from in-memory data. Args: data: the in-memory data associated with the blob encoding: Encoding to use if decoding the bytes into a string mime_type: if provided, will be set as the mime-type of the data path: if provided, will be set as the source from which the data came metadata: Metadata to associate with the blob Returns: Blob instance """ return cls( data=data, mimetype=mime_type, encoding=encoding, path=path, metadata=metadata if metadata is not None else {}, )
def __repr__(self) -> str: """Define the blob representation.""" str_repr = f"Blob {id(self)}" if self.source: str_repr += f" {self.source}" return str_repr
[docs] class Document(BaseMedia): """Class for storing a piece of text and associated metadata. Example: .. code-block:: python from langchain_core.documents import Document document = Document( page_content="Hello, world!", metadata={"source": "https://example.com"} ) """ page_content: str """String text.""" type: Literal["Document"] = "Document" def __init__(self, page_content: str, **kwargs: Any) -> None: """Pass page_content in as positional or named arg.""" # my-py is complaining that page_content is not defined on the base class. # Here, we're relying on pydantic base class to handle the validation. super().__init__(page_content=page_content, **kwargs) # type: ignore[call-arg] @classmethod def is_lc_serializable(cls) -> bool: """Return whether this class is serializable.""" return True @classmethod def get_lc_namespace(cls) -> list[str]: """Get the namespace of the langchain object.""" return ["langchain", "schema", "document"] def __str__(self) -> str: """Override __str__ to restrict it to page_content and metadata.""" # The format matches pydantic format for __str__. # # The purpose of this change is to make sure that user code that # feeds Document objects directly into prompts remains unchanged # due to the addition of the id field (or any other fields in the future). # # This override will likely be removed in the future in favor of # a more general solution of formatting content directly inside the prompts. if self.metadata: return f"page_content='{self.page_content}' metadata={self.metadata}" else: return f"page_content='{self.page_content}'"