Update rag_faiss.py

rag_faiss.py

@@ -27,22 +27,83 @@ print("🔹 Loading embedding model...")
 embedder = SentenceTransformer(EMBED_MODEL)
 
 # -------------------------------
-# SMALL TALK HANDLER
+# SMALL TALK / IDENTITY INTENTS
 # -------------------------------
-SMALL_TALK_RESPONSES = {
-    "hello": "Hello! I’m the official virtual assistant for GUESSS India. How can I help you today?",
-    "hi": "Hi there! I’m here to help you with information about GUESSS India.",
-    "hey": "Hey! Feel free to ask me anything about GUESSS India.",
-    "how are you": "I’m doing well, thank you! How can I assist you today?",
-    "good morning": "Good morning! How can I help you with GUESSS India?",
-    "good evening": "Good evening! How can I assist you today?",
-    "thank you": "You’re welcome! If you have more questions about GUESSS India, I’m here to help.",
-    "thanks": "You’re welcome! Happy to help."
+SMALL_TALK_INTENTS = {
+    "greeting": {
+        "keywords": [
+            "hello", "hi", "hey", "hii", "hai",
+            "good morning", "good evening", "good afternoon"
+        ],
+        "answer": "Hello! I’m the official virtual assistant for GUESSS India. How can I help you today?"
+    },
+    "wellbeing": {
+        "keywords": [
+            "how are you", "how r you", "how are u",
+            "how do you do", "are you okay"
+        ],
+        "answer": "I’m doing well, thank you! How can I assist you with GUESSS India today?"
+    },
+    "identity": {
+        "keywords": [
+            "who are you", "what are you",
+            "tell me about yourself", "introduce yourself"
+        ],
+        "answer": "I’m a virtual assistant created to provide accurate and reliable information about GUESSS India."
+    },
+    "name": {
+        "keywords": [
+            "what is your name", "your name",
+            "do you have a name", "who should i call you"
+        ],
+        "answer": "I don’t have a personal name, but you can think of me as the GUESSS India Assistant."
+    },
+    "capabilities": {
+        "keywords": [
+            "what do you do", "what can you do",
+            "how can you help", "what help can you provide"
+        ],
+        "answer": (
+            "I answer questions related to GUESSS India, including surveys, programs, "
+            "campus ambassadors, podcasts, and general information."
+        )
+    },
+    "trust": {
+        "keywords": [
+            "can i trust you", "is your information reliable",
+            "are you reliable", "is this official"
+        ],
+        "answer": "Yes. My responses are based on approved and verified information provided by the GUESSS India team."
+    },
+    "human": {
+        "keywords": [
+            "are you human", "are you a real person",
+            "are you ai", "are you a bot"
+        ],
+        "answer": "No. I’m an AI-based assistant designed to share verified information from GUESSS India."
+    },
+    "availability": {
+        "keywords": [
+            "are you available", "are you always available",
+            "when are you available"
+        ],
+        "answer": "Yes. I’m available 24/7 on the GUESSS India website."
+    },
+    "thanks": {
+        "keywords": [
+            "thank you", "thanks", "thank u", "thx"
+        ],
+        "answer": "You’re welcome! If you have more questions about GUESSS India, I’m here to help."
+    }
 }
 
-def is_small_talk(query: str) -> bool:
+def handle_small_talk(query: str):
     q = query.lower().strip()
-    return any(key in q for key in SMALL_TALK_RESPONSES)
+    for intent in SMALL_TALK_INTENTS.values():
+        for kw in intent["keywords"]:
+            if kw in q:
+                return intent["answer"]
+    return None
 
 # -------------------------------
 # VECTOR STORE
@@ -52,12 +113,12 @@ class VectorStore:
         print(f"🔹 Loading FAISS index from {INDEX_FILE}")
 
         if not os.path.exists(INDEX_FILE):
-            raise FileNotFoundError("❌ FAISS index not found.")
-
-        self.index = faiss.read_index(INDEX_FILE)
+            raise FileNotFoundError("❌ index.faiss not found")
 
         if not os.path.exists(META_FILE):
-            raise FileNotFoundError("❌ meta.pkl not found.")
+            raise FileNotFoundError("❌ meta.pkl not found")
+
+        self.index = faiss.read_index(INDEX_FILE)
 
         with open(META_FILE, "rb") as f:
             self.texts = pickle.load(f)
@@ -103,32 +164,25 @@ def call_openrouter(prompt, max_tokens=300):
         "max_tokens": max_tokens
     }
 
-    try:
-        r = requests.post(
-            OPENROUTER_URL,
-            headers=headers,
-            json=payload,
-            timeout=60
-        )
-        r.raise_for_status()
-        return r.json()["choices"][0]["message"]["content"]
-
-    except Exception as e:
-        return f"❌ Error contacting OpenRouter: {e}"
+    r = requests.post(
+        OPENROUTER_URL,
+        headers=headers,
+        json=payload,
+        timeout=60
+    )
+    r.raise_for_status()
+    return r.json()["choices"][0]["message"]["content"]
 
 # -------------------------------
-# MAIN RAG LOGIC
+# MAIN ANSWER FUNCTION
 # -------------------------------
-def answer_question(vs, question):
-    q = question.lower().strip()
-
-    # 1️⃣ Handle greetings / small talk
-    if is_small_talk(q):
-        for key in SMALL_TALK_RESPONSES:
-            if key in q:
-                return SMALL_TALK_RESPONSES[key]
+def answer_question(vs, question: str):
+    # 1️⃣ Small talk / identity
+    small_talk = handle_small_talk(question)
+    if small_talk:
+        return small_talk
 
-    # 2️⃣ Knowledge-based → FAISS
+    # 2️⃣ Knowledge-based (RAG)
     contexts = vs.search(question, k=4)
 
     if not contexts:
@@ -154,15 +208,3 @@ QUESTION: {question}
 ANSWER:
 """
     return call_openrouter(prompt)
-
-# -------------------------------
-# EXAMPLE USAGE (for local test)
-# -------------------------------
-if __name__ == "__main__":
-    vs = VectorStore()
-
-    while True:
-        user_q = input("\nYou: ")
-        if user_q.lower() in ["exit", "quit"]:
-            break
-        print("Bot:", answer_question(vs, user_q))