Convert any Pydantic model to dict, compatible with both v1 and v2.
Interface for document loader.
Implementations should implement the lazy-loading method using generators to avoid loading all documents into memory at once.
load is provided just for user convenience and should not be overridden.
Class for storing a piece of text and associated metadata.
Document is for retrieval workflows, not chat I/O. For sending text
to an LLM in a conversation, use message types from langchain.messages.
Load LangSmith Dataset examples as Document objects.
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.
from langchain_core.document_loaders import LangSmithLoader
loader = LangSmithLoader(dataset_id="...", limit=100)
docs = []
for doc in loader.lazy_load():
docs.append(doc)# -> [Document("...", metadata={"inputs": {...}, "outputs": {...}, ...}), ...]