Update app/utils.py

app/utils.py

@@ -1,111 +1,111 @@
-import os
-from dotenv import load_dotenv
-from google import genai
-from google.genai import types
-from pinecone import Pinecone
-
-load_dotenv()
-
-# Initialize Pinecone
-pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
-index = pc.Index("portfolio-chat")
-
-# Initialize Google GenAI Client
-client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))
-
-# Constants
-EMBEDDING_MODEL = "gemini-embedding-001"
-LLM_MODEL = "gemini-2.5-flash-lite"
-EMBEDDING_DIMENSION = 768
-
-def get_embedding(text: str) -> list[float]:
-    """Generate embedding for a given text using Gemini embedding model."""
-    try:
-        response = client.models.embed_content(
-            model=EMBEDDING_MODEL,
-            contents=text,
-            config=types.EmbedContentConfig(
-                output_dimensionality=EMBEDDING_DIMENSION
-            )
-        )
-        return response.embeddings[0].values
-    except Exception as e:
-        print(f"Error generating embedding: {e}")
-        return []
-
-def get_rag_response(query: str) -> str:
-    """
-    RAG pipeline: embed query, retrieve context from Pinecone, generate response.
-    """
-    try:
-        # Step 1: Embed the query
-        query_embedding = get_embedding(query)
-        if not query_embedding:
-            return "I'm having a little trouble accessing my brain right now. Please try again!"
-
-        # Step 2: Query Pinecone for top 5 matches
-        results = index.query(
-            vector=query_embedding,
-            top_k=10,
-            include_metadata=True
-        )
-        
-        # Step 3: Extract context from matches
-        context_chunks = []
-        for match in results.matches:
-            if match.metadata and "text" in match.metadata:
-                context_chunks.append(match.metadata["text"])
-        
-        # Handle case where no context is found
-        if not context_chunks:
-            return "I couldn't find any specific details about that in Mrigank's portfolio, but feel free to ask about his patents, DASES, or other projects!"
-        
-        # Join chunks to create the context text
-        context_text = "\n\n---\n\n".join(context_chunks)
-        
-        # Step 4: Construct the system prompt
-        system_prompt = f"""You are the Advanced AI Assistant for **Mrigank Singh**, a Full Stack AI Developer and Innovator. 
-        Your goal is to impress recruiters and engineers by accurately showcasing Mrigank's technical depth, innovation, and leadership.
-
-        ### CORE INSTRUCTIONS:
-        1. **Identity:** You are NOT Mrigank. You are his digital assistant. Refer to him as "Mrigank" or "he".
-        2. **Tone:** Professional, confident, and technically precise. Sound like a Software Engineer, not a marketing brochure.
-        3. **Formatting:** Use **Markdown** to make answers readable. 
-           - Use **bold** for key technologies or metrics.
-           - Use `bullet points` for lists (skills, projects).
-           - Do not output large walls of text; break it up.
-        4. **Source of Truth:** Answer ONLY based on the "CONTEXT" provided below. Do not make up facts. 
-           - If the answer isn't in the context, say: "I don't have that specific detail, but I can tell you about his patents, his projects or more about him."
-
-        ### CRITICAL BEHAVIORS:
-        - **Recruiters:** If asked about hiring, availability, or contact info, explicitly provide his **Email** and **LinkedIn** from the context.
-        - **Patents:** If asked about innovation, ALWAYS mention his 3 filed patents (Terms & Conditions AI, LexiBot, MealMatch).
-        - **Group Projects:** Credit **Konal Puri and Aviral Khanna** for DASES/UPES Career Platform. Specify Mrigank's role (Mobile App/Frontend).
-        - **Technical Depth:** Mention specific algorithms (e.g., "Knapsack Pruning", "Isolation Forests", "Regex Chunking") to show engineering depth.
-
-        ### CONTEXT FROM KNOWLEDGE BASE:
-        {context_text}
-        """
-        
-        # Step 5: Generate response using Gemini
-        response = client.models.generate_content(
-            model=LLM_MODEL,
-            contents=[
-                types.Content(
-                    role="user", 
-                    parts=[
-                        types.Part.from_text(text=system_prompt + "\n\nUser Question: " + query)
-                    ]
-                )
-            ],
-            config=types.GenerateContentConfig(
-                temperature=0.7,
-                max_output_tokens=500
-            )
-        )
-        
-        return response.text
-
-    except Exception as e:
-        print(f"Error in RAG pipeline: {e}")
+import os
+from dotenv import load_dotenv
+from google import genai
+from google.genai import types
+from pinecone import Pinecone
+
+load_dotenv()
+
+# Initialize Pinecone
+pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
+index = pc.Index("portfolio-chat")
+
+# Initialize Google GenAI Client
+client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))
+
+# Constants
+EMBEDDING_MODEL = "gemini-embedding-001"
+LLM_MODEL = "gemini-2.5-flash-lite"
+EMBEDDING_DIMENSION = 768
+
+def get_embedding(text: str) -> list[float]:
+    """Generate embedding for a given text using Gemini embedding model."""
+    try:
+        response = client.models.embed_content(
+            model=EMBEDDING_MODEL,
+            contents=text,
+            config=types.EmbedContentConfig(
+                output_dimensionality=EMBEDDING_DIMENSION
+            )
+        )
+        return response.embeddings[0].values
+    except Exception as e:
+        print(f"Error generating embedding: {e}")
+        return []
+
+def get_rag_response(query: str) -> str:
+    """
+    RAG pipeline: embed query, retrieve context from Pinecone, generate response.
+    """
+    try:
+        # Step 1: Embed the query
+        query_embedding = get_embedding(query)
+        if not query_embedding:
+            return "I'm having a little trouble accessing my brain right now. Please try again!"
+
+        # Step 2: Query Pinecone for top 5 matches
+        results = index.query(
+            vector=query_embedding,
+            top_k=10,
+            include_metadata=True
+        )
+        
+        # Step 3: Extract context from matches
+        context_chunks = []
+        for match in results.matches:
+            if match.metadata and "text" in match.metadata:
+                context_chunks.append(match.metadata["text"])
+        
+        # Handle case where no context is found
+        if not context_chunks:
+            return "I couldn't find any specific details about that in Mrigank's portfolio, but feel free to ask about his patents, DASES, or other projects!"
+        
+        # Join chunks to create the context text
+        context_text = "\n\n---\n\n".join(context_chunks)
+        
+        # Step 4: Construct the system prompt
+        system_prompt = f"""You are the Advanced AI Assistant for **Mrigank Singh**, a Full Stack AI Developer and Innovator. 
+        Your goal is to impress recruiters and engineers by accurately showcasing Mrigank's technical depth, innovation, and leadership.
+
+        ### CORE INSTRUCTIONS:
+        1. **Identity:** You are NOT Mrigank. You are his digital assistant. Refer to him as "Mrigank" or "he".
+        2. **Tone:** Professional, confident, and technically precise. Sound like a Software Engineer, not a marketing brochure.
+        3. **Formatting:** Use **Markdown** to make answers readable. 
+           - Use **bold** for key technologies or metrics.
+           - Use `bullet points` for lists (skills, projects).
+           - Do not output large walls of text; break it up.
+        4. **Source of Truth:** Answer ONLY based on the "CONTEXT" provided below. Do not make up facts. 
+           - If the answer isn't in the context, say: "I don't have that specific detail, but I can tell you about his patents, his projects or more about him."
+
+        ### CRITICAL BEHAVIORS:
+        - **Recruiters:** If asked about hiring, availability, or contact info, explicitly provide his **Email** and **LinkedIn** from the context.
+        - **Patents:** If asked about innovation, ALWAYS mention his 3 filed patents (Terms & Conditions AI, LexiBot, MealMatch).
+        - **Group Projects:** Credit **Konal Puri and Aviral Khanna** for DASES/UPES Career Platform. Specify Mrigank's role (Mobile App/Frontend).
+        - **Technical Depth:** Mention specific algorithms (e.g., "Knapsack Pruning", "Isolation Forests", "Regex Chunking") to show engineering depth.
+
+        ### CONTEXT FROM KNOWLEDGE BASE:
+        {context_text}
+        """
+        
+        # Step 5: Generate response using Gemini
+        response = client.models.generate_content(
+            model=LLM_MODEL,
+            contents=[
+                types.Content(
+                    role="user", 
+                    parts=[
+                        types.Part.from_text(text=system_prompt + "\n\nUser Question: " + query)
+                    ]
+                )
+            ],
+            config=types.GenerateContentConfig(
+                temperature=0.7,
+                max_output_tokens=1024
+            )
+        )
+        
+        return response.text
+
+    except Exception as e:
+        print(f"Error in RAG pipeline: {e}")
         return "I'm encountering a temporary issue connecting to the knowledge base. Please try again in a moment."