Update utils/llm.py

utils/llm.py

@@ -1,34 +1,34 @@
-import os
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_core.prompts import ChatPromptTemplate
-from config import GOOGLE_API_KEY
-
-
-# Load Gemini LLM
-llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro", google_api_key=GOOGLE_API_KEY)
-
-# Define a structured prompt template
-PROMPT_TEMPLATE = """
-You are an AI assistant that answers user queries based strictly on the given context.
-
-CONTEXT INFORMATION:
-{context}
-
-QUESTION:
-{question}
-
-INSTRUCTIONS:
-- Answer the question based ONLY on the context.
-- Do NOT add any information beyond what is in the context.
-- Do NOT include phrases like "according to the context" or "mentioned in the context".
-- Provide a direct and precise response.
-"""
-
-
-prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
-
-def generate_answer(query: str, context: str):
-    """Generate AI response based on retrieved context."""
-    prompt = prompt_template.format(context=context, question=query)
-    response = llm.invoke(prompt)
-    return response.content
+import os
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.prompts import ChatPromptTemplate
+from config import GOOGLE_API_KEY
+
+
+# Load Gemini LLM
+llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro", google_api_key=GOOGLE_API_KEY)
+
+PROMPT_TEMPLATE = """
+You are an AI assistant that answers user queries based on the given context.
+
+CONTEXT INFORMATION:
+{context}
+
+QUESTION:
+{question}
+
+INSTRUCTIONS:
+- Provide a detailed answer based on the context.
+- If multiple relevant points exist, include them.
+- Keep the response informative but concise.
+- Do NOT add external information beyond the given context.
+"""
+
+
+
+prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
+
+def generate_answer(query: str, context: str):
+    """Generate AI response based on retrieved context."""
+    prompt = prompt_template.format(context=context, question=query)
+    response = llm.invoke(prompt)
+    return response.content