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from typing import Dict, Any
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser
from scripts.rag_chat import build_general_qa_chain
def build_router_chain(model_name=None):
general_qa = build_general_qa_chain(model_name=model_name)
llm = ChatOpenAI(model_name=model_name or "gpt-4o-mini", temperature=0.0)
# This prompt asks the LLM to choose which "mode" to use
router_prompt = ChatPromptTemplate.from_template("""
You are a routing assistant for a chatbot.
Classify the following user request into one of these categories:
- "code" for programming or debugging
- "summarize" for summary requests
- "calculate" for math or numeric calculations
- "general" for general Q&A using course files
Return ONLY the category word.
User request: {input}
""")
router_chain = router_prompt | llm | StrOutputParser()
class Router:
def invoke(self, input_dict: Dict[str, Any]):
category = router_chain.invoke({"input": input_dict["input"]}).strip().lower()
print(f"[ROUTER] User query routed to category: {category}")
if category == "code":
prompt = ChatPromptTemplate.from_template(
"As a coding assistant, help with this Python question.\nQuestion: {input}\nAnswer:"
)
chain = prompt | llm | StrOutputParser()
return {"result": chain.invoke({"input": input_dict["input"]})}
# elif category == "summarize":
# prompt = ChatPromptTemplate.from_template(
# "Provide a concise summary about: {input}\nSummary:"
# )
# chain = prompt | llm | StrOutputParser()
# return {"result": chain.invoke({"input": input_dict["input"]})}
elif category == "summarize":
# 1. Use RAG to retrieve relevant docs
rag_result = general_qa({"query": input_dict["input"]})
# 2. Extract docs and prepare text
source_docs = rag_result.get("source_documents", [])
combined_text = "\n\n".join([doc.page_content for doc in source_docs])
# 3. Run the summarizer chain on the retrieved text
from scripts.summarizer import get_summarizer
summarizer_chain = get_summarizer()
summary = summarizer_chain.run(combined_text)
# 4. Add sources if any
sources = list({str(doc.metadata.get("source", "unknown")) for doc in source_docs})
if sources:
summary += f"\n\n📚 Sources: {', '.join(sources)}"
return {"result": summary}
elif category == "calculate":
prompt = ChatPromptTemplate.from_template(
"Solve the following calculation step-by-step:\n{input}"
)
chain = prompt | llm | StrOutputParser()
return {"result": chain.invoke({"input": input_dict["input"]})}
else: # "general"
return general_qa({"query": input_dict["input"]})
return Router()
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