import datetime
import json
import uuid
from typing import Any, Callable, Iterator, Optional, Sequence, Union
from langsmith import Client as LangSmithClient
from langchain_core.document_loaders.base import BaseLoader
from langchain_core.documents import Document
[docs]class LangSmithLoader(BaseLoader):
"""Load LangSmith Dataset examples as Documents.
Loads the example inputs as the Document page content and places the entire example
into the Document metadata. This allows you to easily create few-shot example
retrievers from the loaded documents.
.. dropdown:: Lazy load
.. code-block:: python
from langchain_core.document_loaders import LangSmithLoader
loader = LangSmithLoader(dataset_id="...", limit=100)
docs = []
for doc in loader.lazy_load():
docs.append(doc)
.. code-block:: pycon
# -> [Document("...", metadata={"inputs": {...}, "outputs": {...}, ...}), ...]
.. versionadded:: 0.2.34
""" # noqa: E501
[docs] def __init__(
self,
*,
dataset_id: Optional[Union[uuid.UUID, str]] = None,
dataset_name: Optional[str] = None,
example_ids: Optional[Sequence[Union[uuid.UUID, str]]] = None,
as_of: Optional[Union[datetime.datetime, str]] = None,
splits: Optional[Sequence[str]] = None,
inline_s3_urls: bool = True,
offset: int = 0,
limit: Optional[int] = None,
metadata: Optional[dict] = None,
filter: Optional[str] = None,
content_key: str = "",
format_content: Optional[Callable[..., str]] = None,
client: Optional[LangSmithClient] = None,
**client_kwargs: Any,
) -> None:
"""
Args:
dataset_id: The ID of the dataset to filter by. Defaults to None.
dataset_name: The name of the dataset to filter by. Defaults to None.
content_key: The inputs key to set as Document page content. ``"."`` characters
are interpreted as nested keys. E.g. ``content_key="first.second"`` will
result in
``Document(page_content=format_content(example.inputs["first"]["second"]))``
format_content: Function for converting the content extracted from the example
inputs into a string. Defaults to JSON-encoding the contents.
example_ids: The IDs of the examples to filter by. Defaults to None.
as_of: The dataset version tag OR
timestamp to retrieve the examples as of.
Response examples will only be those that were present at the time
of the tagged (or timestamped) version.
splits: A list of dataset splits, which are
divisions of your dataset such as 'train', 'test', or 'validation'.
Returns examples only from the specified splits.
inline_s3_urls: Whether to inline S3 URLs. Defaults to True.
offset: The offset to start from. Defaults to 0.
limit: The maximum number of examples to return.
filter: A structured filter string to apply to the examples.
client: LangSmith Client. If not provided will be initialized from below args.
client_kwargs: Keyword args to pass to LangSmith client init. Should only be
specified if ``client`` isn't.
""" # noqa: E501
if client and client_kwargs:
raise ValueError
self._client = client or LangSmithClient(**client_kwargs)
self.content_key = list(content_key.split(".")) if content_key else []
self.format_content = format_content or _stringify
self.dataset_id = dataset_id
self.dataset_name = dataset_name
self.example_ids = example_ids
self.as_of = as_of
self.splits = splits
self.inline_s3_urls = inline_s3_urls
self.offset = offset
self.limit = limit
self.metadata = metadata
self.filter = filter
[docs] def lazy_load(self) -> Iterator[Document]:
for example in self._client.list_examples(
dataset_id=self.dataset_id,
dataset_name=self.dataset_name,
example_ids=self.example_ids,
as_of=self.as_of,
splits=self.splits,
inline_s3_urls=self.inline_s3_urls,
offset=self.offset,
limit=self.limit,
metadata=self.metadata,
filter=self.filter,
):
content: Any = example.inputs
for key in self.content_key:
content = content[key]
content_str = self.format_content(content)
metadata = example.dict()
# Stringify datetime and UUID types.
for k in ("dataset_id", "created_at", "modified_at", "source_run_id", "id"):
metadata[k] = str(metadata[k]) if metadata[k] else metadata[k]
yield Document(content_str, metadata=metadata)
def _stringify(x: Union[str, dict]) -> str:
if isinstance(x, str):
return x
else:
try:
return json.dumps(x, indent=2)
except Exception:
return str(x)
