mirror of
https://github.com/hwchase17/langchain
synced 2024-11-18 09:25:54 +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
155 lines
5.1 KiB
Python
155 lines
5.1 KiB
Python
"""Document Loader for ArcGIS FeatureLayers."""
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from __future__ import annotations
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import json
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import re
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import warnings
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from datetime import datetime, timezone
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from typing import TYPE_CHECKING, Any, Iterator, List, Optional, Union
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from langchain_core.documents import Document
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from langchain_community.document_loaders.base import BaseLoader
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if TYPE_CHECKING:
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import arcgis
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_NOT_PROVIDED = "(Not Provided)"
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class ArcGISLoader(BaseLoader):
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"""Load records from an ArcGIS FeatureLayer."""
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def __init__(
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self,
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layer: Union[str, arcgis.features.FeatureLayer],
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gis: Optional[arcgis.gis.GIS] = None,
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where: str = "1=1",
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out_fields: Optional[Union[List[str], str]] = None,
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return_geometry: bool = False,
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result_record_count: Optional[int] = None,
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lyr_desc: Optional[str] = None,
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**kwargs: Any,
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):
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try:
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import arcgis
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except ImportError as e:
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raise ImportError(
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"arcgis is required to use the ArcGIS Loader. "
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"Install it with pip or conda."
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) from e
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try:
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from bs4 import BeautifulSoup # type: ignore
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self.BEAUTIFULSOUP = BeautifulSoup
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except ImportError:
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warnings.warn("BeautifulSoup not found. HTML will not be parsed.")
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self.BEAUTIFULSOUP = None
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self.gis = gis or arcgis.gis.GIS()
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if isinstance(layer, str):
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self.url = layer
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self.layer = arcgis.features.FeatureLayer(layer, gis=gis)
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else:
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self.url = layer.url
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self.layer = layer
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self.layer_properties = self._get_layer_properties(lyr_desc)
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self.where = where
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if isinstance(out_fields, str):
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self.out_fields = out_fields
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elif out_fields is None:
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self.out_fields = "*"
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else:
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self.out_fields = ",".join(out_fields)
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self.return_geometry = return_geometry
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self.result_record_count = result_record_count
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self.return_all_records = not isinstance(result_record_count, int)
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query_params = dict(
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where=self.where,
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out_fields=self.out_fields,
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return_geometry=self.return_geometry,
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return_all_records=self.return_all_records,
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result_record_count=self.result_record_count,
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)
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query_params.update(kwargs)
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self.query_params = query_params
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def _get_layer_properties(self, lyr_desc: Optional[str] = None) -> dict:
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"""Get the layer properties from the FeatureLayer."""
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import arcgis
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layer_number_pattern = re.compile(r"/\d+$")
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props = self.layer.properties
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if lyr_desc is None:
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# retrieve description from the FeatureLayer if not provided
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try:
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if self.BEAUTIFULSOUP:
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lyr_desc = self.BEAUTIFULSOUP(props["description"]).text
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else:
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lyr_desc = props["description"]
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lyr_desc = lyr_desc or _NOT_PROVIDED
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except KeyError:
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lyr_desc = _NOT_PROVIDED
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try:
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item_id = props["serviceItemId"]
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item = self.gis.content.get(item_id) or arcgis.features.FeatureLayer(
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re.sub(layer_number_pattern, "", self.url),
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)
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try:
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raw_desc = item.description
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except AttributeError:
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raw_desc = item.properties.description
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if self.BEAUTIFULSOUP:
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item_desc = self.BEAUTIFULSOUP(raw_desc).text
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else:
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item_desc = raw_desc
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item_desc = item_desc or _NOT_PROVIDED
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except KeyError:
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item_desc = _NOT_PROVIDED
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return {
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"layer_description": lyr_desc,
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"item_description": item_desc,
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"layer_properties": props,
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}
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def lazy_load(self) -> Iterator[Document]:
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"""Lazy load records from FeatureLayer."""
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query_response = self.layer.query(**self.query_params)
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features = (feature.as_dict for feature in query_response)
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for feature in features:
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attributes = feature["attributes"]
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page_content = json.dumps(attributes)
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metadata = {
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"accessed": f"{datetime.now(timezone.utc).isoformat()}Z",
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"name": self.layer_properties["layer_properties"]["name"],
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"url": self.url,
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"layer_description": self.layer_properties["layer_description"],
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"item_description": self.layer_properties["item_description"],
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"layer_properties": self.layer_properties["layer_properties"],
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}
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if self.return_geometry:
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try:
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metadata["geometry"] = feature["geometry"]
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except KeyError:
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warnings.warn(
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"Geometry could not be retrieved from the feature layer."
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)
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yield Document(page_content=page_content, metadata=metadata)
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def load(self) -> List[Document]:
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"""Load all records from FeatureLayer."""
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return list(self.lazy_load())
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