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- #
- # Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- #
- import logging
- import os
- import time
- from abc import ABC
- from Bio import Entrez
- import re
- import xml.etree.ElementTree as ET
- from agent.tools.base import ToolParamBase, ToolMeta, ToolBase
- from api.utils.api_utils import timeout
-
-
- class PubMedParam(ToolParamBase):
- """
- Define the PubMed component parameters.
- """
-
- def __init__(self):
- self.meta:ToolMeta = {
- "name": "pubmed_search",
- "description": """
- PubMed is an openly accessible, free database which includes primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics.
- In addition to MEDLINE, PubMed provides access to:
- - older references from the print version of Index Medicus, back to 1951 and earlier
- - references to some journals before they were indexed in Index Medicus and MEDLINE, for instance Science, BMJ, and Annals of Surgery
- - very recent entries to records for an article before it is indexed with Medical Subject Headings (MeSH) and added to MEDLINE
- - a collection of books available full-text and other subsets of NLM records[4]
- - PMC citations
- - NCBI Bookshelf
- """,
- "parameters": {
- "query": {
- "type": "string",
- "description": "The search keywords to execute with PubMed. The keywords should be the most important words/terms(includes synonyms) from the original request.",
- "default": "{sys.query}",
- "required": True
- }
- }
- }
- super().__init__()
- self.top_n = 12
- self.email = "A.N.Other@example.com"
-
- def check(self):
- self.check_positive_integer(self.top_n, "Top N")
-
- def get_input_form(self) -> dict[str, dict]:
- return {
- "query": {
- "name": "Query",
- "type": "line"
- }
- }
-
- class PubMed(ToolBase, ABC):
- component_name = "PubMed"
-
- @timeout(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12))
- def _invoke(self, **kwargs):
- if not kwargs.get("query"):
- self.set_output("formalized_content", "")
- return ""
-
- last_e = ""
- for _ in range(self._param.max_retries+1):
- try:
- Entrez.email = self._param.email
- pubmedids = Entrez.read(Entrez.esearch(db='pubmed', retmax=self._param.top_n, term=kwargs["query"]))['IdList']
- pubmedcnt = ET.fromstring(re.sub(r'<(/?)b>|<(/?)i>', '', Entrez.efetch(db='pubmed', id=",".join(pubmedids),
- retmode="xml").read().decode("utf-8")))
- self._retrieve_chunks(pubmedcnt.findall("PubmedArticle"),
- get_title=lambda child: child.find("MedlineCitation").find("Article").find("ArticleTitle").text,
- get_url=lambda child: "https://pubmed.ncbi.nlm.nih.gov/" + child.find("MedlineCitation").find("PMID").text,
- get_content=lambda child: child.find("MedlineCitation") \
- .find("Article") \
- .find("Abstract") \
- .find("AbstractText").text \
- if child.find("MedlineCitation")\
- .find("Article").find("Abstract") \
- else "No abstract available")
- return self.output("formalized_content")
- except Exception as e:
- last_e = e
- logging.exception(f"PubMed error: {e}")
- time.sleep(self._param.delay_after_error)
-
- if last_e:
- self.set_output("_ERROR", str(last_e))
- return f"PubMed error: {last_e}"
-
- assert False, self.output()
-
- def thoughts(self) -> str:
- return "Looking for scholarly papers on `{}`,” prioritising reputable sources.".format(self.get_input().get("query", "-_-!"))
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