langchain/libs/community/langchain_community/utilities/pubmed.py

import json
import logging
import time
import urllib.error
import urllib.parse
import urllib.request
from typing import Any, Dict, Iterator, List

from langchain_core.documents import Document
from langchain_core.pydantic_v1 import BaseModel, root_validator

logger = logging.getLogger(__name__)


class PubMedAPIWrapper(BaseModel):
    """
    Wrapper around PubMed API.

    This wrapper will use the PubMed API to conduct searches and fetch
    document summaries. By default, it will return the document summaries
    of the top-k results of an input search.

    Parameters:
        top_k_results: number of the top-scored document used for the PubMed tool
        MAX_QUERY_LENGTH: maximum length of the query.
          Default is 300 characters.
        doc_content_chars_max: maximum length of the document content.
          Content will be truncated if it exceeds this length.
          Default is 2000 characters.
        max_retry: maximum number of retries for a request. Default is 5.
        sleep_time: time to wait between retries.
          Default is 0.2 seconds.
        email: email address to be used for the PubMed API.
    """

    parse: Any  #: :meta private:

    base_url_esearch: str = (
        "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?"
    )
    base_url_efetch: str = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?"
    max_retry: int = 5
    sleep_time: float = 0.2

    # Default values for the parameters
    top_k_results: int = 3
    MAX_QUERY_LENGTH: int = 300
    doc_content_chars_max: int = 2000
    email: str = "your_email@example.com"

    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that the python package exists in environment."""
        try:
            import xmltodict

            values["parse"] = xmltodict.parse
        except ImportError:
            raise ImportError(
                "Could not import xmltodict python package. "
                "Please install it with `pip install xmltodict`."
            )
        return values

    def run(self, query: str) -> str:
        """
        Run PubMed search and get the article meta information.
        See https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESearch
        It uses only the most informative fields of article meta information.
        """

        try:
            # Retrieve the top-k results for the query
            docs = [
                f"Published: {result['Published']}\n"
                f"Title: {result['Title']}\n"
                f"Copyright Information: {result['Copyright Information']}\n"
                f"Summary::\n{result['Summary']}"
                for result in self.load(query[: self.MAX_QUERY_LENGTH])
            ]

            # Join the results and limit the character count
            return (
                "\n\n".join(docs)[: self.doc_content_chars_max]
                if docs
                else "No good PubMed Result was found"
            )
        except Exception as ex:
            return f"PubMed exception: {ex}"

    def lazy_load(self, query: str) -> Iterator[dict]:
        """
        Search PubMed for documents matching the query.
        Return an iterator of dictionaries containing the document metadata.
        """

        url = (
            self.base_url_esearch
            + "db=pubmed&term="
            + str({urllib.parse.quote(query)})
            + f"&retmode=json&retmax={self.top_k_results}&usehistory=y"
        )
        result = urllib.request.urlopen(url)
        text = result.read().decode("utf-8")
        json_text = json.loads(text)

        webenv = json_text["esearchresult"]["webenv"]
        for uid in json_text["esearchresult"]["idlist"]:
            yield self.retrieve_article(uid, webenv)

    def load(self, query: str) -> List[dict]:
        """
        Search PubMed for documents matching the query.
        Return a list of dictionaries containing the document metadata.
        """
        return list(self.lazy_load(query))

    def _dict2document(self, doc: dict) -> Document:
        summary = doc.pop("Summary")
        return Document(page_content=summary, metadata=doc)

    def lazy_load_docs(self, query: str) -> Iterator[Document]:
        for d in self.lazy_load(query=query):
            yield self._dict2document(d)

    def load_docs(self, query: str) -> List[Document]:
        return list(self.lazy_load_docs(query=query))

    def retrieve_article(self, uid: str, webenv: str) -> dict:
        url = (
            self.base_url_efetch
            + "db=pubmed&retmode=xml&id="
            + uid
            + "&webenv="
            + webenv
        )

        retry = 0
        while True:
            try:
                result = urllib.request.urlopen(url)
                break
            except urllib.error.HTTPError as e:
                if e.code == 429 and retry < self.max_retry:
                    # Too Many Requests errors
                    # wait for an exponentially increasing amount of time
                    print(  # noqa: T201
                        f"Too Many Requests, "
                        f"waiting for {self.sleep_time:.2f} seconds..."
                    )
                    time.sleep(self.sleep_time)
                    self.sleep_time *= 2
                    retry += 1
                else:
                    raise e

        xml_text = result.read().decode("utf-8")
        text_dict = self.parse(xml_text)
        return self._parse_article(uid, text_dict)

    def _parse_article(self, uid: str, text_dict: dict) -> dict:
        try:
            ar = text_dict["PubmedArticleSet"]["PubmedArticle"]["MedlineCitation"][
                "Article"
            ]
        except KeyError:
            ar = text_dict["PubmedArticleSet"]["PubmedBookArticle"]["BookDocument"]
        abstract_text = ar.get("Abstract", {}).get("AbstractText", [])
        summaries = [
            f"{txt['@Label']}: {txt['#text']}"
            for txt in abstract_text
            if "#text" in txt and "@Label" in txt
        ]
        summary = (
            "\n".join(summaries)
            if summaries
            else (
                abstract_text
                if isinstance(abstract_text, str)
                else (
                    "\n".join(str(value) for value in abstract_text.values())
                    if isinstance(abstract_text, dict)
                    else "No abstract available"
                )
            )
        )
        a_d = ar.get("ArticleDate", {})
        pub_date = "-".join(
            [a_d.get("Year", ""), a_d.get("Month", ""), a_d.get("Day", "")]
        )

        return {
            "uid": uid,
            "Title": ar.get("ArticleTitle", ""),
            "Published": pub_date,
            "Copyright Information": ar.get("Abstract", {}).get(
                "CopyrightInformation", ""
            ),
            "Summary": summary,
        }