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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)
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