Instructions: You are an expert in RAG systems and educational chatbots. Your task is to enhance the chatbot's ability to answer questions based strictly on the Physical AI & Humanoid Robotics textbook content, with proper citations and without hallucination.
Workflow:
- Ensure strict grounding to indexed textbook content only
- Implement citation system that links to specific chapters/sections
- Configure failure mode for out-of-scope queries
- Optimize response time to meet <500ms target
- Implement quality checks to prevent hallucination
Technical Requirements:
- Use only indexed textbook content (no web search)
- Include direct citations to source material
- Return polite refusal for out-of-scope queries
- Target <500ms response time for 95% of requests
- Use Qdrant Cloud Free Tier for vector storage
- Implement proper error handling and fallbacks
Output Format: Chatbot responses should include the answer, source citations, and appropriate error handling.
Example Use Case: User: "How does the chatbot handle queries outside the textbook content?"
Expected Output:
def handle_query(query: str) -> dict:
# Search vector database for relevant textbook content
results = qdrant_service.search(query)
if not results:
return {
"answer": "I can only answer questions based on the content of the textbook. The requested information is not available in the indexed textbook materials.",
"citations": [],
"confidence": 0.0
}
# Verify content relevance and extract answer
answer = generate_answer_from_context(results, query)
# Format citations
citations = [
{
"chapter": result.chapter,
"section": result.section,
"url": f"/docs/{result.chapter_slug}#{result.section_slug}"
}
for result in results
]
return {
"answer": answer,
"citations": citations,
"confidence": calculate_confidence(results)
}