mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
ed58eeb9c5
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
51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
from typing import Any, List
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from langchain_community.document_loaders.unstructured import (
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UnstructuredFileLoader,
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validate_unstructured_version,
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)
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class UnstructuredODTLoader(UnstructuredFileLoader):
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"""Load `OpenOffice ODT` files using `Unstructured`.
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You can run the loader in one of two modes: "single" and "elements".
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If you use "single" mode, the document will be returned as a single
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langchain Document object. If you use "elements" mode, the unstructured
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library will split the document into elements such as Title and NarrativeText.
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You can pass in additional unstructured kwargs after mode to apply
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different unstructured settings.
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Examples
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--------
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from langchain_community.document_loaders import UnstructuredODTLoader
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loader = UnstructuredODTLoader(
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"example.odt", mode="elements", strategy="fast",
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)
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docs = loader.load()
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References
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----------
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https://unstructured-io.github.io/unstructured/bricks.html#partition-odt
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"""
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def __init__(
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self, file_path: str, mode: str = "single", **unstructured_kwargs: Any
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):
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"""
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Args:
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file_path: The path to the file to load.
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mode: The mode to use when loading the file. Can be one of "single",
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"multi", or "all". Default is "single".
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**unstructured_kwargs: Any kwargs to pass to the unstructured.
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"""
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validate_unstructured_version(min_unstructured_version="0.6.3")
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super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
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def _get_elements(self) -> List:
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from unstructured.partition.odt import partition_odt
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return partition_odt(filename=self.file_path, **self.unstructured_kwargs)
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