abcd

main.py

@@ -14,7 +14,6 @@ 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
@@ -71,14 +70,7 @@ docs = [Document(page_content=text.page_content, metadata=text.metadata) for tex
 _ = vector_store.add_documents(documents=docs)
 
 
-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}")
-])
+prompt = hub.pull("rlm/rag-prompt")
 
 class State(TypedDict):
     question: str
@@ -91,10 +83,8 @@ def retrieve(state: State):
 
 def generate(state: State):
     docs_content = "\n\n".join(doc.page_content for doc in state["context"])
-    messages = prompt.format_messages(
-        context=docs_content,
-        question=state["question"]
-    )
+    messages = prompt.invoke({"question": state["question"], "context": docs_content})
+    print(messages)
     response = llm.invoke(messages)
     return {"answer": response.content}