Merge branch 'main' into v2.4

cookbook/tools/pubmed.py

@@ -0,0 +1,8 @@
+from phi.assistant import Assistant
+from phi.tools.pubmed import Pubmed
+
+assistant = Assistant(tools=[Pubmed()], debug_mode=True, show_tool_calls=True)
+assistant.print_response(
+ "ulcerative colitis.",
+ markdown=True,
+)

phi/tools/pubmed.py

@@ -0,0 +1,74 @@
+from typing import Optional, List, Dict, Any
+import json
+import requests
+from xml.etree import ElementTree
+from phi.tools import Toolkit
+
+
+class Pubmed(Toolkit):
+ def __init__(
+ self,
+ email: str = "[email protected]",
+ max_results: Optional[int] = None,
+ ):
+ super().__init__(name="pubmed")
+ self.max_results: Optional[int] = max_results
+ self.email: str = email
+
+ self.register(self.search_pubmed)
+
+ def fetch_pubmed_ids(self, query: str, max_results: int, email: str) -> List[str]:
+ url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
+ params = {
+ "db": "pubmed",
+ "term": query,
+ "retmax": max_results,
+ "email": email,
+ "usehistory": "y",
+ }
+ response = requests.get(url, params=params)
+ root = ElementTree.fromstring(response.content)
+ return [id_elem.text for id_elem in root.findall(".//Id") if id_elem.text is not None]
+
+ def fetch_details(self, pubmed_ids: List[str]) -> ElementTree.Element:
+ url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi"
+ params = {"db": "pubmed", "id": ",".join(pubmed_ids), "retmode": "xml"}
+ response = requests.get(url, params=params)
+ return ElementTree.fromstring(response.content)
+
+ def parse_details(self, xml_root: ElementTree.Element) -> List[Dict[str, Any]]:
+ articles = []
+ for article in xml_root.findall(".//PubmedArticle"):
+ pub_date = article.find(".//PubDate/Year")
+ title = article.find(".//ArticleTitle")
+ abstract = article.find(".//AbstractText")
+ articles.append(
+ {
+ "Published": (pub_date.text if pub_date is not None else "No date available"),
+ "Title": title.text if title is not None else "No title available",
+ "Summary": (abstract.text if abstract is not None else "No abstract available"),
+ }
+ )
+ return articles
+
+ def search_pubmed(self, query: str, max_results: int = 10) -> str:
+ """Use this function to search PubMed for articles.
+
+ Args:
+ query (str): The search query.
+ max_results (int): The maximum number of results to return.
+
+ Returns:
+ str: A JSON string containing the search results.
+ """
+ try:
+ ids = self.fetch_pubmed_ids(query, self.max_results or max_results, self.email)
+ details_root = self.fetch_details(ids)
+ articles = self.parse_details(details_root)
+ results = [
+ f"Published: {article.get('Published')}\nTitle: {article.get('Title')}\nSummary:\n{article.get('Summary')}"
+ for article in articles
+ ]
+ return json.dumps(results)
+ except Exception as e:
+ return f"Cound not fetch articles. Error: {e}"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "phidata"
-version = "2.3.89"
+version = "2.3.90"
 description = "Add memory, knowledge and tools to LLMs."
 requires-python = ">=3.8"
 readme = "README.md"