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app.py

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+import langgraph
+from langgraph.graph import StateGraph, START, END
+from langgraph.prebuilt import ToolNode, tools_condition
+from langchain_groq import ChatGroq
+from typing_extensions import TypedDict, Annotated
+from pydantic import BaseModel, Field
+from langchain_community.utilities import GoogleSerperAPIWrapper, WikipediaAPIWrapper
+from langchain.tools import GoogleSerperRun, WikipediaQueryRun, DuckDuckGoSearchRun
+from langchain_core.messages import SystemMessage, HumanMessage
+from langchain_community.tools import TavilySearchResults
+from langgraph.graph.message import add_messages
+from dotenv import load_dotenv
+from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
+from langchain_core.output_parsers import PydanticOutputParser
+from langgraph.graph.message import AnyMessage
+from langgraph.checkpoint.memory import MemorySaver
+from pydantic import BaseModel,Field
+from fastapi import FastAPI, Response
+import uvicorn
+from fastapi.middleware.cors import CORSMiddleware
+import os
+import warnings
+import json
+import re
+
+warnings.filterwarnings("ignore")
+
+load_dotenv()
+
+app = FastAPI()
+origins = ["*"]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"]    
+)
+
+os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API_KEY')
+os.environ["TAVILY_API_KEY"] = os.getenv("TAVILY_API_KEY")
+os.environ["SERPER_API_KEY"] = os.getenv("SERPER_API_KEY")
+
+llm = ChatGroq(model = "qwen-qwq-32b",temperature=0.1)
+
+memorysaver = MemorySaver()
+
+class State(TypedDict):
+     messages: Annotated[list[AnyMessage], add_messages]
+    
+class MovieDetails(BaseModel):
+    title: str = Field(..., title="Movie Title", description="The title of the programme for which you want to fetch details.")
+    genres:str = Field(..., title="Genres", description="The genres of the programmee.")
+    duration:str = Field(..., title="Duration", description="The duration of the programme.")
+    synopsis:str = Field(..., title="Synopsis", description="The synopsis of the programme.")
+    numberofSeasons: str = Field(..., title="NumberOfSeasons", description="The number of seasons in the programme.")
+    numberOfEpisodes: str = Field(..., title="NumberOfEpisodes", description="The number of episodes in the programme.")
+    summary:str = Field(..., title="Summary", description="The summary of the programme which contains number of episodes and seasons.")  
+    source: str = Field(..., title="Source", description="The source(url) from where the information is fetched.")  
+
+parser = PydanticOutputParser(pydantic_object=MovieDetails)
+     
+def build_tools():    
+    serper_wrapper = GoogleSerperAPIWrapper(k = 1)
+    serper_run = GoogleSerperRun(api_wrapper = serper_wrapper)
+
+    tools = [TavilySearchResults(max_results=2)]
+    return tools
+
+def get_llm():
+    tools = build_tools()
+    llm_output = llm.with_structured_output(MovieDetails)
+    llm_with_tools = llm.bind_tools(tools)
+    return llm_with_tools
+
+def llm_callingTools(state:State): 
+    
+    format_instructions = parser.get_format_instructions()
+       
+    system_msg = SystemMessage(content=f"""You are a smart movie researcher.
+                    1. Your job is to retrieve **only real, verifiable details** from trusted sources.
+                    2. **Never assume or generate** fake names, genres, or synopses, details about episodes or seasons.
+                    3. Always provide a **brief summary** in **plain text** under the title.
+
+                    ### Format Instructions:
+                    If the user **is asking about a specific show or programme** — for example, referencing the title, episodes, seasons, cast, language, or summary — format your response like this:
+                    {format_instructions}
+
+                    Otherwise, if the latest message does **not** refer to any specific programme or show (e.g. general queries), respond in **plain text** only without JSON formatting.
+
+                    Think carefully before responding: **Is the latest message is referring to a specific show or programme, even indirectly?** Only then use the formatted output.""")
+    
+    human_message = HumanMessage( content=f"{state['messages']}.")
+    llm_with_tools = get_llm()
+    return {"messages": [llm_with_tools.invoke([system_msg]+ [human_message])]}
+
+def build_graph(clearMemory: bool = False):
+    global memorysaver
+    if clearMemory:
+        memorysaver = MemorySaver()
+    graph_builder = StateGraph(State)
+    graph_builder.add_node("llm_with_tool", llm_callingTools)
+    graph_builder.add_node("tools", ToolNode(build_tools()))
+    graph_builder.add_edge(START, "llm_with_tool")
+    graph_builder.add_conditional_edges("llm_with_tool", tools_condition)
+    graph_builder.add_edge("tools", "llm_with_tool")
+    
+    graph = graph_builder.compile(checkpointer=memorysaver)
+    return graph 
+
+def is_pattern_in_string(string: str) -> bool:
+    pattern = r'\bepisode?s?\b|\bseason?s?\b'
+    return re.search(pattern, string) is not None  
+        
+@app.post("/api/v1/get_programme_info")
+def get_data_by_prompt(prompt: str, thread_id: str): 
+    clearMemory = False
+    try:
+        print(f"Prompt: {prompt}")
+        if not is_pattern_in_string(prompt):
+            print("No previous conversation found. Starting fresh.")
+            clearMemory = True                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 
+        graph = build_graph(clearMemory)   
+        config = {"configurable": {"thread_id": thread_id}}
+        
+        message_prompt = {"messages": [{"role":"human", "content":prompt}]}
+        data = graph.invoke(message_prompt, config=config)
+        final_output = data["messages"][-1].content        
+        if is_pattern_in_string(prompt):
+            try:
+                final_output_new = json.loads(final_output)
+                if isinstance(final_output_new, dict):
+                    return  Response(content=final_output_new["summary"], media_type="text/markdown")
+                else:
+                    return Response(content=final_output, media_type="text/markdown")
+            except json.JSONDecodeError as e:
+                return Response(content=final_output, media_type="text/markdown")
+        return Response(content = data["messages"][-1].content, media_type="text/markdown")              
+        
+    except Exception as e:
+        return Response(content=str(e), media_type="text/markdown")
+
+if __name__ == "__main__":
+    #get_data_by_prompt("CSI","1")
+    uvicorn.run(app, host= "127.0.0.1", port= 8000)
+
+

requirements.txt

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+langgraph
+python-dotenv
+langchain
+langchain-community
+langchain-groq
+wikipedia 
+duckduckgo-search
+fastapi
+uvicorn