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d9fe279dde
### What problem does this PR solve? #9082 #6365 <u> **WARNING: it's not compatible with the older version of `Agent` module, which means that `Agent` from older versions can not work anymore.**</u> ### Type of change - [x] New Feature (non-breaking change which adds functionality)
106 lines
4.6 KiB
Python
106 lines
4.6 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import logging
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import os
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import time
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from abc import ABC
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from Bio import Entrez
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import re
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import xml.etree.ElementTree as ET
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from agent.tools.base import ToolParamBase, ToolMeta, ToolBase
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from api.utils.api_utils import timeout
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class PubMedParam(ToolParamBase):
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"""
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Define the PubMed component parameters.
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"""
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def __init__(self):
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self.meta:ToolMeta = {
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"name": "pubmed_search",
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"description": """
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PubMed is an openly accessible, free database which includes primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics.
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In addition to MEDLINE, PubMed provides access to:
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- older references from the print version of Index Medicus, back to 1951 and earlier
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- references to some journals before they were indexed in Index Medicus and MEDLINE, for instance Science, BMJ, and Annals of Surgery
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- very recent entries to records for an article before it is indexed with Medical Subject Headings (MeSH) and added to MEDLINE
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- a collection of books available full-text and other subsets of NLM records[4]
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- PMC citations
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- NCBI Bookshelf
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""",
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"parameters": {
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"query": {
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"type": "string",
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"description": "The search keywords to execute with PubMed. The keywords should be the most important words/terms(includes synonyms) from the original request.",
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"default": "{sys.query}",
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"required": True
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}
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}
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}
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super().__init__()
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self.top_n = 12
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self.email = "A.N.Other@example.com"
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def check(self):
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self.check_positive_integer(self.top_n, "Top N")
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def get_input_form(self) -> dict[str, dict]:
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return {
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"query": {
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"name": "Query",
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"type": "line"
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}
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}
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class PubMed(ToolBase, ABC):
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component_name = "PubMed"
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@timeout(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12))
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def _invoke(self, **kwargs):
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if not kwargs.get("query"):
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self.set_output("formalized_content", "")
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return ""
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last_e = ""
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for _ in range(self._param.max_retries+1):
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try:
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Entrez.email = self._param.email
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pubmedids = Entrez.read(Entrez.esearch(db='pubmed', retmax=self._param.top_n, term=kwargs["query"]))['IdList']
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pubmedcnt = ET.fromstring(re.sub(r'<(/?)b>|<(/?)i>', '', Entrez.efetch(db='pubmed', id=",".join(pubmedids),
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retmode="xml").read().decode("utf-8")))
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self._retrieve_chunks(pubmedcnt.findall("PubmedArticle"),
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get_title=lambda child: child.find("MedlineCitation").find("Article").find("ArticleTitle").text,
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get_url=lambda child: "https://pubmed.ncbi.nlm.nih.gov/" + child.find("MedlineCitation").find("PMID").text,
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get_content=lambda child: child.find("MedlineCitation") \
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.find("Article") \
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.find("Abstract") \
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.find("AbstractText").text \
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if child.find("MedlineCitation")\
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.find("Article").find("Abstract") \
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else "No abstract available")
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return self.output("formalized_content")
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except Exception as e:
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last_e = e
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logging.exception(f"PubMed error: {e}")
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time.sleep(self._param.delay_after_error)
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if last_e:
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self.set_output("_ERROR", str(last_e))
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return f"PubMed error: {last_e}"
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assert False, self.output()
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