simple solution

app.py

@@ -8,6 +8,7 @@ import json
 
 from langchain_groq import ChatGroq
 from langchain_core.messages import HumanMessage
+from langchain_community.document_loaders import WikipediaLoader
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -25,11 +26,36 @@ llm = ChatGroq(api_key=groq_api_key, model="llama-3.3-70b-versatile")
 class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")
+
+
+    def wiki_ret(self, question: str) -> str:
+        """ Retrieve docs from wikipedia """
+
+        # Search query
+        # structured_llm = llm.with_structured_output(SearchQuery)
+        # search_query = structured_llm.invoke([search_instructions]+state['messages'])
+        
+        # Search
+        search_docs = WikipediaLoader(query=question, 
+                                    load_max_docs=2).load()
+
+        # Format
+        formatted_search_docs = "\n\n---\n\n".join(
+            [
+                f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
+                for doc in search_docs
+            ]
+        )
+
+        return formatted_search_docs
+
     def __call__(self, question: str) -> str:
 
         # print(f"Agent received question (first 50 chars): {question[:50]}...")
         # fixed_answer = "This is a default answer. --- 1"
         # print(f"Agent returning fixed answer: {fixed_answer}")
+        context = self.wiki_ret(question)
+
         prompt = f"""
         You are a general AI assistant. I will ask you a question. 
         YOUR FINAL ANSWER should be a number OR 
@@ -39,7 +65,8 @@ class BasicAgent:
         a string, don't use articles, neither abbreviations (e.g. for cities), and write 
         the digits in plain text unless specified otherwise. If you are asked for a comma 
         separated list, apply the above rules depending of whether the element to be put 
-        in the list is a number or a string. Question: {question} """
+        in the list is a number or a string. Question: {question} 
+        For answering questions you can use context from wikiperdia: {context}"""
         # Your answer must be in the following format:
 
         # {{"task_id": "task_id_1", "model_answer": "Answer 1 from your model", "reasoning_trace": "The different steps by which your model reached answer 1"}}

requirements.txt

@@ -1,3 +1,4 @@
 gradio
 requests
-langchain_groq
+langchain-groq
+langchain-community