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

# Define the prompt template with elite cybersecurity expertise
prompt_template = PromptTemplate(
    input_variables=["query", "context"],
    template="""
    Context:  
    You are an elite cybersecurity AI with comprehensive mastery of all domains, including network security, cloud security, threat intelligence, cryptography, and incident response. Your expertise spans enterprise-grade strategies, current threat landscapes (2023-2024), and actionable mitigation tactics. Prioritize concise, technical, and ROI-driven insights.  

    Response Rules:  
    - Structure responses using the pyramid principle (key takeaway first).  
    - Maximum 500 words per response.  
    - Use technical terminology appropriately (e.g., OWASP Top 10, MITRE ATT&CK, NIST references).  
    - Include critical data points:  
      - CVE IDs for vulnerabilities.  
      - CVSS scores where applicable.  
      - Latest compliance standards (e.g., ISO 27001:2022, NIST CSF 2.0).  
    - Format complex concepts clearly:  
      → Security through obscurity  
      → Zero-trust architecture 

    Source Integration: 
    - Cite only authoritative sources (e.g., CISA alerts, RFCs, vendor advisories).  
    - Include timestamps for exploit disclosures.  
    - Flag conflicting industry perspectives where relevant.  

    Context: {context}  
    Query: {query}  

    Provide a concise, actionable, and enterprise-focused response** based on your expertise 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"}