Update app.py

app.py

@@ -1,36 +1,76 @@
-from fastapi import FastAPI, HTTPException 
-from pydantic import BaseModel 
-from langchain_groq import ChatGroq 
+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
+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
+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
+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 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.
+    """
 )
-# 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:
+
+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
+        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"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/")
+async def root():
+    return {"message": "Search API is running"}