Upload llm_handler.py

llm_handler.py

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+import os
+from openai import OpenAI
+
+# --- Global Variables from main app ---
+encoder = None
+chroma_collection = None
+openrouter_client = None
+
+
+def initialize_llm():
+    """Initializes the OpenRouter client."""
+    global openrouter_client
+
+    # Get the API key from Hugging Face secrets
+    api_key = os.getenv("OPENROUTER_API_KEY")
+
+    if not api_key:
+        print("❌ OPENROUTER_API_KEY secret not found.")
+        return
+
+    openrouter_client = OpenAI(
+        base_url="https://openrouter.ai/api/v1",
+        api_key=api_key,
+    )
+    print("✅ OpenRouter client initialized successfully.")
+
+
+def get_rag_response(query: str) -> str:
+    """Generates a response using Retrieval-Augmented Generation with OpenRouter."""
+    if not all([encoder, chroma_collection, openrouter_client]):
+        return "Chatbot is not ready. Models or clients are not loaded."
+
+    # 1. Retrieve relevant documents from ChromaDB
+    query_embedding = encoder.encode([query])[0].tolist()
+    results = chroma_collection.query(
+        query_embeddings=[query_embedding],
+        n_results=3,
+    )
+
+    # Get the 'metadatas' which contain the full internship details
+    retrieved_docs = results.get('metadatas', [[]])[0]
+    context = "\n".join([str(doc) for doc in retrieved_docs])
+
+    # 2. Generate a response using the LLM with the retrieved context
+    system_prompt = """
+    You are a helpful and friendly assistant for the PM Internship Scheme.
+    First, try to answer the user's question based on the provided context about specific internships.
+    If the context doesn't have the answer, use your own general knowledge to provide a helpful response.
+    """
+
+    user_prompt = f"Context:\n{context}\n\nQuestion: {query}"
+
+    try:
+        completion = openrouter_client.chat.completions.create(
+            model="x-ai/grok-4-fast",  # The specific model ID for Grok-4 Fast
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt},
+            ],
+        )
+        answer = completion.choices[0].message.content
+        return answer
+    except Exception as e:
+        print(f"❌ Error calling OpenRouter API: {e}")
+        return "Sorry, I encountered an error while processing your request."