fixing get key api

app.py

@@ -5,15 +5,15 @@ from langchain_community.document_loaders import PyMuPDFLoader
 import requests
 from groq import Groq
 import os
-from dotenv import load_dotenv
+# from dotenv import load_dotenv
 import tempfile
 from googlesearch import search
 from bs4 import BeautifulSoup
 from urllib.parse import urljoin, urlparse
 import re
 
-load_dotenv()
-
+# load_dotenv()
+print(os.getenv("GROQ_API_KEY"))
 class DocumentState(TypedDict):
     documents: list[dict]
     summaries: list[str]
@@ -63,7 +63,8 @@ def get_groq_response(prompt):
 
 def google_search_agent(state: DocumentState) -> DocumentState:
     """Performs Google search and extracts content from results."""
-    if not state.get('search_query'):
+    search_query = state.get('search_query')
+    if not search_query or not isinstance(search_query, str):
         return state
     
     try:
@@ -110,7 +111,8 @@ def google_search_agent(state: DocumentState) -> DocumentState:
 
 def search_analyzer_agent(state: DocumentState) -> DocumentState:
     """Analyzes user query to determine if web search is needed."""
-    if not state.get('search_query'):
+    search_query = state.get('search_query')
+    if not search_query or not isinstance(search_query, str):
         return state
     
     # Keywords that typically indicate need for current information
@@ -120,26 +122,30 @@ def search_analyzer_agent(state: DocumentState) -> DocumentState:
         'explain', 'information about', 'tell me about', 'research'
     ]
     
-    query_lower = state['search_query'].lower()
+    query_lower = search_query.lower()
     state['needs_search'] = any(indicator in query_lower for indicator in search_indicators)
     
     return state
 
 def search_response_agent(state: DocumentState) -> DocumentState:
     """Generates response based on search results."""
-    if not state.get('search_results'):
+    search_results = state.get('search_results')
+    search_query = state.get('search_query')
+    
+    if not search_results or not isinstance(search_results, list):
         # Fallback to regular LLM response
-        llm_response = get_llm_response(state['search_query'])
-        state['summaries'] = [llm_response['response']]
+        if search_query and isinstance(search_query, str):
+            llm_response = get_llm_response(search_query)
+            state['summaries'] = [llm_response['response']]
         return state
     
     # Prepare search results for LLM
     search_context = "\n\n".join([
         f"Source: {result['title']} ({result['url']})\nContent: {result['content']}"
-        for result in state['search_results']
+        for result in search_results
     ])
     
-    prompt = f"""Based on the following search results, provide a comprehensive and accurate answer to the user's question: "{state['search_query']}"
+    prompt = f"""Based on the following search results, provide a comprehensive and accurate answer to the user's question: "{search_query}"
 
 Search Results:
 {search_context}