Update app.py

app.py

@@ -1,7 +1,8 @@
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from langchain_groq import ChatGroq
-from crewai import Agent, Task, Crew
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
 import os
 
 # Initialize FastAPI app
@@ -18,63 +19,25 @@ llm = ChatGroq(
     groq_api_key="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"  # Replace with your actual Groq API key
 )
 
-# Define the classifier agent
-classifier_agent = Agent(
-    role='Classifier',
-    goal='Understand the context of the user query and generate up to 5 suggestions.',
-    backstory='You are an AI that specializes in understanding user queries and providing relevant suggestions.',
-    llm=llm,
-    verbose=True
-)
-
-# Define the task for the classifier agent
-classifier_task = Task(
-    description='Analyze the user query and generate up to 5 suggestions based on the context.',
-    agent=classifier_agent,
-    expected_output='A list of up to 5 suggestions related to the user query.'
-)
-
-# Define the main agent for processing the query
-main_agent = Agent(
-    role='Main Agent',
-    goal='Provide a detailed response to the user query.',
-    backstory='You are an AI that specializes in providing detailed and accurate responses to user queries.',
-    llm=llm,
-    verbose=True
-)
-
-# Define the task for the main agent
-main_task = Task(
-    description='Provide a detailed response to the user query.',
-    agent=main_agent,
-    expected_output='A detailed and accurate response to the user query.'
-)
-
-# Create the crew
-crew = Crew(
-    agents=[classifier_agent, main_agent],
-    tasks=[classifier_task, main_task],
-    verbose=2
+prompt_template = PromptTemplate(
+    input_variables=["query"],
+    template="Please provide a detailed response to the following query: {query}"
 )
+chain = LLMChain(llm=llm, prompt=prompt_template)
 
 @app.post("/search")
 async def process_search(search_query: SearchQuery):
     try:
-        # Process the query using CrewAI
-        result = crew.kickoff(inputs={'query': search_query.query})
-        
-        # Extract the response and suggestions from the result
-        response = result['outputs']['main_agent']
-        suggestions = result['outputs']['classifier_agent']
+        # Process the query using LangChain
+        response = chain.run(query=search_query.query)
         
         return {
             "status": "success",
-            "response": response,
-            "suggestions": suggestions
+            "response": response
         }
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
 @app.get("/")
 async def root():
-    return {"message": "Search API is running"}
+    return {"message": "Search API is running"}