app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from langchain_groq import ChatGroq
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
import os

# Initialize FastAPI app
app = FastAPI()

# Create a request model with context
class SearchQuery(BaseModel):
    query: str
    context: str = None  # Optional context field

# Initialize LangChain with Groq
llm = ChatGroq(
    temperature=0.7,
    model_name="mixtral-8x7b-32768",
    groq_api_key="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"  # Replace with your actual Groq API key
)

# Define the prompt template with cybersecurity expertise
prompt_template = PromptTemplate(
    input_variables=["query", "context"],
    template="""
    Context: You are a cybersecurity expert with extensive experience in all sub-streams of the industry, including but not limited to network security, application security, cloud security, threat intelligence, penetration testing, and incident response. {context}
    Query: {query}
    Please provide a detailed and professional response to the query based on your expertise in cybersecurity and the provided context.
    """
)
chain = LLMChain(llm=llm, prompt=prompt_template)

@app.post("/search")
async def process_search(search_query: SearchQuery):
    try:
        # Set default context if not provided
        context = search_query.context or "You are a cybersecurity expert."
        
        # Process the query using LangChain with context
        response = chain.run(query=search_query.query, context=context)
        
        return {
            "status": "success",
            "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"}