--- a
+++ b/aiagents4pharma/talk2biomodels/tools/query_article.py
@@ -0,0 +1,63 @@
+#!/usr/bin/env python3
+
+"""
+Tool for asking questions to the article.
+"""
+
+import logging
+from typing import Type, Annotated
+from pydantic import BaseModel, Field
+from langchain_core.tools import BaseTool
+from langchain_core.vectorstores import InMemoryVectorStore
+from langchain_community.document_loaders import PyPDFLoader
+from langgraph.prebuilt import InjectedState
+
+# Initialize logger
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class QueryArticleInput(BaseModel):
+    """
+    Input schema for the query_articles tool.
+    """
+    question: Annotated[str, Field(description="User question to search articles.")]
+    state: Annotated[dict, InjectedState]
+
+# Note: It's important that every field has type hints. BaseTool is a
+# Pydantic class and not having type hints can lead to unexpected behavior.
+class QueryArticle(BaseTool):
+    """
+    Tool to ask questions to the article.
+    """
+    name: str = "query_article"
+    description: str = "Ask questions to the article."
+    args_schema: Type[BaseModel] = QueryArticleInput
+
+    def _run(self,
+             question: str,
+             state: Annotated[dict, InjectedState]):
+        """
+        Run the tool.
+
+        Args:
+            query (str): The search query.
+        """
+        logger.log(logging.INFO, "loading the article from %s", state['pdf_file_name'])
+        logger.log(logging.INFO, "searching the article with the question: %s", question)
+        # Load the article
+        loader = PyPDFLoader(state['pdf_file_name'])
+        # Load the pages of the article
+        pages = []
+        for page in loader.lazy_load():
+            pages.append(page)
+        # Set up text embedding model
+        text_embedding_model = state['text_embedding_model']
+        logging.info("Loaded text embedding model %s", text_embedding_model)
+        # Create a vector store from the pages
+        vector_store = InMemoryVectorStore.from_documents(
+                                            pages,
+                                            text_embedding_model)
+        # Search the article with the question
+        docs = vector_store.similarity_search(question)
+        # Return the content of the pages
+        return "\n".join([doc.page_content for doc in docs])