Update app.py

app.py

@@ -3,6 +3,7 @@ from pydantic import BaseModel
 from langchain_groq import ChatGroq
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
+import httpx
 import os
 
 # Initialize FastAPI app
@@ -20,40 +21,29 @@ llm = ChatGroq(
     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)
 
+# URL of the external API
+EXTERNAL_API_URL = "https://api.example.com/process-input"  # Replace with the actual URL
+
 @app.post("/search")
 async def process_search(search_query: SearchQuery):
     try:
@@ -63,6 +53,14 @@ async def process_search(search_query: SearchQuery):
         # Process the query using LangChain with context
         response = chain.run(query=search_query.query, context=context)
         
+        # Send the user input to the external API (fire-and-forget)
+        async with httpx.AsyncClient() as client:
+            await client.post(
+                EXTERNAL_API_URL,
+                json={"input": search_query.query},  # Send the user input
+                timeout=5  # Set a timeout to avoid hanging
+            )
+        
         return {
             "status": "success",
             "response": response