langchain/libs/community/langchain_community/document_loaders/blob_loaders/schema.py
Bagatur ed58eeb9c5
community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion:

```
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
```

Moved the following to core
```
mv langchain/langchain/utils/json_schema.py core/langchain_core/utils
mv langchain/langchain/utils/html.py core/langchain_core/utils
mv langchain/langchain/utils/strings.py core/langchain_core/utils
cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py
rm langchain/langchain/utils/env.py
```

See .scripts/community_split/script_integrations.sh for all changes
2023-12-11 13:53:30 -08:00

196 lines
6.5 KiB
Python

"""Schema for Blobs and Blob Loaders.
The goal is to facilitate decoupling of content loading from content parsing code.
In addition, content loading code should provide a lazy loading interface by default.
"""
from __future__ import annotations
import contextlib
import mimetypes
from abc import ABC, abstractmethod
from io import BufferedReader, BytesIO
from pathlib import PurePath
from typing import Any, Dict, Generator, Iterable, Mapping, Optional, Union, cast
from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
PathLike = Union[str, PurePath]
class Blob(BaseModel):
"""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
"""
data: Union[bytes, str, 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."""
metadata: Dict[str, Any] = Field(default_factory=dict)
"""Metadata about the blob (e.g., source)"""
class Config:
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
@root_validator(pre=True)
def check_blob_is_valid(cls, values: Mapping[str, Any]) -> Mapping[str, Any]:
"""Verify that either data or path is provided."""
if "data" not in values and "path" not in values:
raise ValueError("Either data or path must be provided")
return values
def as_string(self) -> str:
"""Read data as a string."""
if self.data is None and self.path:
with open(str(self.path), "r", 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:
raise ValueError(f"Unable to get string for blob {self}")
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:
raise ValueError(f"Unable to get bytes for blob {self}")
@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:
raise NotImplementedError(f"Unable to convert blob {self}")
@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 {},
)
@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
class BlobLoader(ABC):
"""Abstract interface for blob loaders implementation.
Implementer should be able to load raw content from a storage system according
to some criteria and return the raw content lazily as a stream of blobs.
"""
@abstractmethod
def yield_blobs(
self,
) -> Iterable[Blob]:
"""A lazy loader for raw data represented by LangChain's Blob object.
Returns:
A generator over blobs
"""