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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
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GitHub
238
libs/community/langchain_community/utilities/arxiv.py
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238
libs/community/langchain_community/utilities/arxiv.py
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"""Util that calls Arxiv."""
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import logging
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import os
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import re
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from typing import Any, Dict, List, Optional
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from langchain_core.documents import Document
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from langchain_core.pydantic_v1 import BaseModel, root_validator
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logger = logging.getLogger(__name__)
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class ArxivAPIWrapper(BaseModel):
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"""Wrapper around ArxivAPI.
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To use, you should have the ``arxiv`` python package installed.
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https://lukasschwab.me/arxiv.py/index.html
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This wrapper will use the Arxiv API to conduct searches and
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fetch document summaries. By default, it will return the document summaries
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of the top-k results.
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If the query is in the form of arxiv identifier
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(see https://info.arxiv.org/help/find/index.html), it will return the paper
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corresponding to the arxiv identifier.
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It limits the Document content by doc_content_chars_max.
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Set doc_content_chars_max=None if you don't want to limit the content size.
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Attributes:
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top_k_results: number of the top-scored document used for the arxiv tool
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ARXIV_MAX_QUERY_LENGTH: the cut limit on the query used for the arxiv tool.
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load_max_docs: a limit to the number of loaded documents
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load_all_available_meta:
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if True: the `metadata` of the loaded Documents contains all available
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meta info (see https://lukasschwab.me/arxiv.py/index.html#Result),
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if False: the `metadata` contains only the published date, title,
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authors and summary.
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doc_content_chars_max: an optional cut limit for the length of a document's
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content
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Example:
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.. code-block:: python
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from langchain_community.utilities.arxiv import ArxivAPIWrapper
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arxiv = ArxivAPIWrapper(
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top_k_results = 3,
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ARXIV_MAX_QUERY_LENGTH = 300,
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load_max_docs = 3,
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load_all_available_meta = False,
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doc_content_chars_max = 40000
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)
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arxiv.run("tree of thought llm)
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"""
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arxiv_search: Any #: :meta private:
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arxiv_exceptions: Any # :meta private:
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top_k_results: int = 3
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ARXIV_MAX_QUERY_LENGTH: int = 300
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load_max_docs: int = 100
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load_all_available_meta: bool = False
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doc_content_chars_max: Optional[int] = 4000
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def is_arxiv_identifier(self, query: str) -> bool:
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"""Check if a query is an arxiv identifier."""
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arxiv_identifier_pattern = r"\d{2}(0[1-9]|1[0-2])\.\d{4,5}(v\d+|)|\d{7}.*"
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for query_item in query[: self.ARXIV_MAX_QUERY_LENGTH].split():
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match_result = re.match(arxiv_identifier_pattern, query_item)
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if not match_result:
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return False
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assert match_result is not None
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if not match_result.group(0) == query_item:
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return False
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return True
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that the python package exists in environment."""
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try:
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import arxiv
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values["arxiv_search"] = arxiv.Search
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values["arxiv_exceptions"] = (
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arxiv.ArxivError,
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arxiv.UnexpectedEmptyPageError,
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arxiv.HTTPError,
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)
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values["arxiv_result"] = arxiv.Result
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except ImportError:
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raise ImportError(
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"Could not import arxiv python package. "
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"Please install it with `pip install arxiv`."
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)
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return values
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def get_summaries_as_docs(self, query: str) -> List[Document]:
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"""
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Performs an arxiv search and returns list of
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documents, with summaries as the content.
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If an error occurs or no documents found, error text
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is returned instead. Wrapper for
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https://lukasschwab.me/arxiv.py/index.html#Search
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Args:
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query: a plaintext search query
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""" # noqa: E501
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try:
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if self.is_arxiv_identifier(query):
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results = self.arxiv_search(
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id_list=query.split(),
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max_results=self.top_k_results,
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).results()
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else:
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results = self.arxiv_search( # type: ignore
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query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.top_k_results
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).results()
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except self.arxiv_exceptions as ex:
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return [Document(page_content=f"Arxiv exception: {ex}")]
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docs = [
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Document(
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page_content=result.summary,
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metadata={
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"Published": result.updated.date(),
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"Title": result.title,
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"Authors": ", ".join(a.name for a in result.authors),
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},
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)
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for result in results
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]
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return docs
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def run(self, query: str) -> str:
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"""
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Performs an arxiv search and A single string
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with the publish date, title, authors, and summary
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for each article separated by two newlines.
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If an error occurs or no documents found, error text
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is returned instead. Wrapper for
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https://lukasschwab.me/arxiv.py/index.html#Search
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Args:
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query: a plaintext search query
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""" # noqa: E501
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try:
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if self.is_arxiv_identifier(query):
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results = self.arxiv_search(
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id_list=query.split(),
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max_results=self.top_k_results,
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).results()
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else:
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results = self.arxiv_search( # type: ignore
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query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.top_k_results
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).results()
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except self.arxiv_exceptions as ex:
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return f"Arxiv exception: {ex}"
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docs = [
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f"Published: {result.updated.date()}\n"
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f"Title: {result.title}\n"
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f"Authors: {', '.join(a.name for a in result.authors)}\n"
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f"Summary: {result.summary}"
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for result in results
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]
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if docs:
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return "\n\n".join(docs)[: self.doc_content_chars_max]
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else:
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return "No good Arxiv Result was found"
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def load(self, query: str) -> List[Document]:
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"""
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Run Arxiv search and get the article texts plus the article meta information.
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See https://lukasschwab.me/arxiv.py/index.html#Search
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Returns: a list of documents with the document.page_content in text format
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Performs an arxiv search, downloads the top k results as PDFs, loads
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them as Documents, and returns them in a List.
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Args:
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query: a plaintext search query
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""" # noqa: E501
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try:
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import fitz
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except ImportError:
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raise ImportError(
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"PyMuPDF package not found, please install it with "
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"`pip install pymupdf`"
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)
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try:
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# Remove the ":" and "-" from the query, as they can cause search problems
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query = query.replace(":", "").replace("-", "")
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if self.is_arxiv_identifier(query):
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results = self.arxiv_search(
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id_list=query[: self.ARXIV_MAX_QUERY_LENGTH].split(),
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max_results=self.load_max_docs,
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).results()
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else:
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results = self.arxiv_search( # type: ignore
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query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.load_max_docs
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).results()
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except self.arxiv_exceptions as ex:
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logger.debug("Error on arxiv: %s", ex)
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return []
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docs: List[Document] = []
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for result in results:
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try:
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doc_file_name: str = result.download_pdf()
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with fitz.open(doc_file_name) as doc_file:
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text: str = "".join(page.get_text() for page in doc_file)
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except (FileNotFoundError, fitz.fitz.FileDataError) as f_ex:
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logger.debug(f_ex)
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continue
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if self.load_all_available_meta:
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extra_metadata = {
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"entry_id": result.entry_id,
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"published_first_time": str(result.published.date()),
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"comment": result.comment,
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"journal_ref": result.journal_ref,
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"doi": result.doi,
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"primary_category": result.primary_category,
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"categories": result.categories,
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"links": [link.href for link in result.links],
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}
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else:
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extra_metadata = {}
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metadata = {
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"Published": str(result.updated.date()),
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"Title": result.title,
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"Authors": ", ".join(a.name for a in result.authors),
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"Summary": result.summary,
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**extra_metadata,
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}
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doc = Document(
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page_content=text[: self.doc_content_chars_max], metadata=metadata
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)
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docs.append(doc)
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os.remove(doc_file_name)
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return docs
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