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src//groq_integration.py

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+"""
+Groq LLM Integration for Context Thread Agent
+Provides fast, free reasoning without OpenAI API costs
+"""
+
+import os
+from typing import Optional
+import json
+from groq import Groq
+
+
+class GroqReasoningEngine:
+    """
+    Alternative reasoning engine using Groq API
+    Faster and free compared to OpenAI
+    """
+    
+    def __init__(self, api_key: Optional[str] = None):
+        """Initialize Groq client"""
+        self.api_key = api_key or os.getenv("GROQ_API_KEY")
+        if not self.api_key or self.api_key == "YOUR_GROQ_API_KEY_HERE":
+            raise ValueError("GROQ_API_KEY not found in environment")
+        
+        self.client = Groq(api_key=self.api_key)
+        self.model = "llama-3.3-70b-versatile"  # Latest Groq model
+        
+    def reason_with_context(
+        self,
+        question: str,
+        context: str,
+        system_prompt: Optional[str] = None,
+        conversation_history: Optional[list] = None
+    ) -> dict:
+        """
+        Generate answer using Groq with context and conversation history
+        Returns: {answer, confidence, citations}
+        """
+        if system_prompt is None:
+            system_prompt = """You are an expert data science assistant analyzing Jupyter notebooks and Excel documents.
+Answer ONLY based on the provided context. Do not make up or infer information beyond what's shown.
+If asked something not in the context, say "I cannot find that information in the provided document".
+
+When referencing specific parts of code or analysis, cite them clearly using [Cell X] format.
+Be precise, detailed, and technical in your answers.
+If you see patterns, trends, or issues in the data/code, highlight them."""
+        
+        messages = [
+            {
+                "role": "system",
+                "content": system_prompt
+            }
+        ]
+        
+        # Add conversation history for context continuity
+        if conversation_history:
+            for msg in conversation_history[-6:]:  # Last 3 exchanges
+                messages.append({
+                    "role": msg["role"],
+                    "content": msg["content"]
+                })
+        
+        # Add current query with context
+        messages.append({
+            "role": "user",
+            "content": f"Context from the document:\n{context}\n\nQuestion: {question}\n\nProvide a detailed answer based on the context:"
+        })
+        
+        try:
+            response = self.client.chat.completions.create(
+                model=self.model,
+                messages=messages,
+                max_tokens=2000,
+                temperature=0.2,
+                top_p=0.9
+            )
+            
+            answer = response.choices[0].message.content
+            
+            return {
+                "answer": answer,
+                "confidence": 0.85,
+                "citations": []
+            }
+        except Exception as e:
+            return {
+                "answer": f"Error using Groq: {str(e)}",
+                "confidence": 0.0,
+                "citations": []
+            }
+    
+    def generate_keypoints(
+        self,
+        context: str,
+        max_points: int = 10
+    ) -> dict:
+        """
+        Generate key insights and summary points from document
+        """
+        system_prompt = """You are an expert at analyzing data science notebooks and Excel documents.
+Generate a comprehensive summary with key insights, findings, and important points.
+Focus on: methodology, data transformations, key findings, issues/concerns, and conclusions.
+Format your response as a clear, bulleted list."""
+        
+        messages = [
+            {
+                "role": "system",
+                "content": system_prompt
+            },
+            {
+                "role": "user",
+                "content": f"""Analyze this document and provide {max_points} key points covering:
+1. Purpose and methodology
+2. Data characteristics and transformations
+3. Key findings or patterns
+4. Any issues, concerns, or anomalies
+5. Overall conclusions
+
+Document context:
+{context}
+
+Provide your analysis:"""
+            }
+        ]
+        
+        try:
+            response = self.client.chat.completions.create(
+                model=self.model,
+                messages=messages,
+                max_tokens=2500,
+                temperature=0.3
+            )
+            
+            keypoints = response.choices[0].message.content
+            
+            return {
+                "keypoints": keypoints,
+                "success": True
+            }
+        except Exception as e:
+            return {
+                "keypoints": f"Error generating keypoints: {str(e)}",
+                "success": False
+            }