Update blog_content_generator.py

blog_content_generator.py

@@ -1,29 +1,44 @@
+import os
+from dotenv import load_dotenv
+
+# Secret Key
+load_dotenv(override=True)
+tavily_api_key = os.getenv("TAVILY_API_KEY")
+
 def blog_content_agent(llm, state):
   from langgraph.graph import MessagesState
   from langchain_community.tools import DuckDuckGoSearchRun
-  from langchain_google_genai import ChatGoogleGenerativeAI
+  from langchain_community.tools import TavilySearchResults
   from langchain_core.messages import HumanMessage, SystemMessage
   from datetime import date
   
   topic = state["final_topic"]
+  print(topic)
+  
   # tool
-  def duckduckgo_search(query: str) -> str:
+  def tavily_search(query: str) -> str:
     """
-    Performs a search on DuckDuckGo using the provided query.
+    Performs a search on Tavily using the provided query.
+
     Args:
         query (str): The search query to be executed.
+
     Returns:
         str: The result of the search query.
     """
-    current_year = date.today().strftime("%Y")
+    tavily = TavilySearchResults(
+    max_results=5,
+    search_depth="advanced",
+    include_answer=True,
+    include_raw_content=True,
+    )
     
-    search = DuckDuckGoSearchRun()
-    result = search.invoke(f"Search this in UK: {query} after:{current_year}")
+    text = tavily.invoke({"query": f"Search this in UK: {query} After:2025"})
     print("Tool Call")
-    print(result)
-    return result
+    print(text)
+    return text
 
-  tool = [duckduckgo_search]
+  tool = [tavily_search]
 #   llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash-exp", api_key =  gemini_api_key)
   llm_with_tools = llm.bind_tools(tool)
 
@@ -126,9 +141,9 @@ Replacing your boiler is a big investment, but it’s one that can bring signifi
 
 Context:
 - The blog post should be informative and engaging.
-- The Blog post is for the UK audience.
-- Always Use the attached tool duckduckgo_search to get the latest information about the topic.
-- Always use the Tool Message in writing the Blog content.
+- The Blog Post is for the UK audience. 
+- Always Use the attached tool tavily_search to get the latest information about the topic.
+- Always use the Tool Message in writing the Blog content. 
 
 Constraints:
 - The blog post should be between 700 to 800 words.
@@ -169,5 +184,4 @@ Output Format:
 
   message = [HumanMessage(content=f"Search this query: {topic}")]
   messages = react_graph.invoke({"messages": message})
-#   print(messages['messages'][-1].content)
   return {"blog_content": messages['messages'][-1].content}