Update chat model initialization to "deepseek-r1-distill-llama-70b" and refactor prompt creation using ChatPromptTemplate for improved FAQ chatbot responses in main.py.

main.py

@@ -14,6 +14,7 @@ from pydantic.main import BaseModel
 from typing_extensions import List, TypedDict
 
 from langchain_cohere import CohereEmbeddings
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 
 import re
 # from dotenv import load_dotenv
@@ -31,7 +32,7 @@ if not os.environ.get("GROQ_API_KEY"):
 # print(f"GROQ_API_KEY: {os.getenv('GROQ_API_KEY')}")
 # print(f"HUGGING_FACE_API_KEY: {os.getenv('HUGGING_FACE_API_KEY')}")
 
-llm = init_chat_model("qwen-qwq-32b", model_provider="groq", api_key=os.environ["GROQ_API_KEY"])
+llm = init_chat_model("deepseek-r1-distill-llama-70b", model_provider="groq", api_key=os.environ["GROQ_API_KEY"])
 '''
 embeddings = HuggingFaceInferenceAPIEmbeddings(
     api_key = os.getenv('HUGGING_FACE_API_KEY'),
@@ -70,7 +71,14 @@ docs = [Document(page_content=text.page_content, metadata=text.metadata) for tex
 _ = vector_store.add_documents(documents=docs)
 
 
-prompt = hub.pull("rlm/rag-prompt")
+prompt = ChatPromptTemplate.from_messages([
+    SystemMessage(content="""You are a helpful FAQ chatbot assistant for the Coherence 2025 Hackathon. 
+    Use the provided context to answer questions accurately and concisely. 
+    If the answer cannot be found in the context, say so clearly.
+    Keep your responses friendly and professional."""),
+    MessagesPlaceholder(variable_name="context"),
+    HumanMessage(content="{question}")
+])
 
 class State(TypedDict):
     question: str
@@ -83,8 +91,10 @@ def retrieve(state: State):
 
 def generate(state: State):
     docs_content = "\n\n".join(doc.page_content for doc in state["context"])
-    messages = prompt.invoke({"question": state["question"], "context": docs_content})
-    print(messages)
+    messages = prompt.format_messages(
+        context=docs_content,
+        question=state["question"]
+    )
     response = llm.invoke(messages)
     return {"answer": response.content}