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Q&A System

Submit questions to your documents and get AI-generated answers with source citations and 80% cost savings.

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How it Works

When you ask a question, Hippo:
1

Semantic Search

Finds relevant chunks from your documents (not keyword matching)
2

Precision Retrieval

Retrieves only what’s needed (70% smaller context)
3

AI Generation

Generates answer using mini model with flagship accuracy
4

Source Citations

Returns answer + source documents + confidence scores
Result: 99.5% accuracy match to flagship models at 80% lower cost!

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Submit Questions

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Basic Q&A

from cerevox import Hippo

hippo = Hippo(api_key="your-api-key")

# Ask a question
answer = hippo.submit_ask(
    chat_id="chat_123",
    question="How do I authenticate users in the API?"
)

# Access the response
print(f"Answer: {answer.response}")
print(f"Confidence: {answer.confidence_score}")
print(f"Sources: {len(answer.sources)}")

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Response Structure

The answer object contains:
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response
string
The AI-generated answer to your question
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question
string
The original question (as processed)
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confidence_score
float
Confidence score from 0.0 to 1.0
  • 0.9+: High confidence
  • 0.7-0.9: Medium confidence
  • < 0.7: Low confidence (verify sources)
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sources
array
List of source documents cited in the answerEach source contains:
  • file_name: Name of the source file
  • file_id: Unique file identifier
  • page_number: Page where info was found
  • relevance_score: How relevant (0.0-1.0)
  • excerpt: Text snippet from source
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created_at
timestamp
When the question was answered

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Access Source Citations

# Ask question
answer = hippo.submit_ask(chat_id, "What is the refund policy?")

# Display answer
print(f"Answer: {answer.response}\n")

# Show sources
print(f"Based on {len(answer.sources)} sources:")
for source in answer.sources:
    print(f"\n📄 {source.file_name}")
    print(f"   Page: {source.page_number}")
    print(f"   Relevance: {source.relevance_score:.2f}")
    print(f"   Excerpt: {source.excerpt[:100]}...")

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Question History

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Get All Q&A for a Chat

# Get complete Q&A history
asks = hippo.get_asks(chat_id="chat_123")

for ask in asks:
    print(f"Q: {ask.question}")
    print(f"A: {ask.response}")
    print(f"Confidence: {ask.confidence_score}\n")

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Get Specific Q&A by Index

# Get the 3rd question/answer from chat
ask = hippo.get_ask_by_index(
    chat_id="chat_123",
    index=2  # 0-indexed
)

print(f"Q: {ask.question}")
print(f"A: {ask.response}")

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Follow-up Questions

Chats remember context for follow-up questions: 
# First question
answer1 = hippo.submit_ask(
    chat_id,
    "What are the API authentication methods?"
)
print(f"A1: {answer1.response}")

# Follow-up (references "methods" from Q1)
answer2 = hippo.submit_ask(
    chat_id,
    "Which one is most secure?"  # Understands context
)
print(f"A2: {answer2.response}")

# Another follow-up (references "secure method" from Q2)
answer3 = hippo.submit_ask(
    chat_id,
    "How do I implement it?"  # Knows what "it" refers to
)
print(f"A3: {answer3.response}")
Context memory: Last 10 Q&A exchanges are used for context in follow-up questions.

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Question Types

Best for: Specific information retrieval
questions = [
    "What is the API rate limit?",
    "What's the refund policy?",
    "What programming languages are supported?"
]
Performance: Highest accuracy (99.5%+ with clear documentation)
Best for: Step-by-step instructions
questions = [
    "How do I reset my password?",
    "How can I integrate with Stripe?",
    "How do I export data to CSV?"
]
Performance: Excellent when docs contain clear procedures
Best for: Comparing options or features
questions = [
    "What's the difference between Basic and Pro plans?",
    "How does REST compare to GraphQL in our API?",
    "Which authentication method is most secure?"
]
Performance: Good when comparison info exists in docs
Best for: Synthesizing information
questions = [
    "What are the main features of Product X?",
    "Summarize the key risks in this contract",
    "What were the Q3 revenue highlights?"
]
Performance: Good for overview questions across multiple sources
Best for: Quick verification
questions = [
    "Does the API support webhooks?",
    "Can I export data to Excel?",
    "Is there a mobile app?"
]
Performance: Excellent with clear documentation

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Writing Effective Questions

Be Specific

✅ “What is the API rate limit for Pro plan users?” ❌ “Tell me about limits”Specific questions get specific answers

Use Natural Language

✅ “How do I reset my password?” ❌ “password reset procedure documentation”Write questions as you’d ask a person

One Question at a Time

✅ “What is the refund policy?” ❌ “What’s the refund policy and cancellation process and payment methods?”Multiple questions → Ask separately for better answers

Provide Context if Needed

✅ “In the REST API, how do I authenticate?” ❌ “How authenticate?” (ambiguous)Context helps when docs cover multiple systems

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Interpreting Confidence Scores

answer = hippo.submit_ask(chat_id, question)

if answer.confidence_score >= 0.9:
    print("✅ High confidence - Answer is very reliable")
elif answer.confidence_score >= 0.7:
    print("⚠️ Medium confidence - Check sources to verify")
else:
    print("⚠️ Low confidence - Information may not be in documents")
    print("Consider:")
    print("- Rephrasing the question")
    print("- Adding relevant documents")
    print("- Checking if info exists in uploaded files")

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Complete Example: Support Bot

from cerevox import Hippo

class SupportBot:
    def __init__(self, api_key):
        self.hippo = Hippo(api_key=api_key)
        self.folder = None
        self.chat = None

    def setup(self, support_docs):
        """Initialize knowledge base"""
        # Create folder
        self.folder = self.hippo.create_folder(
            "Support Knowledge Base",
            "Customer support documentation"
        )

        # Upload support docs
        for doc in support_docs:
            self.hippo.upload_file(self.folder.id, doc)

        # Create chat
        self.chat = self.hippo.create_chat(
            self.folder.id,
            "Customer Support Chat"
        )

    def ask(self, question):
        """Ask a support question"""
        answer = self.hippo.submit_ask(self.chat.id, question)

        # Format response
        response = {
            'answer': answer.response,
            'confidence': answer.confidence_score,
            'sources': [
                {
                    'file': s.file_name,
                    'page': s.page_number,
                    'relevance': s.relevance_score
                }
                for s in answer.sources
            ]
        }

        return response

    def get_history(self):
        """Get all Q&A history"""
        return self.hippo.get_asks(self.chat.id)

# Usage
bot = SupportBot(api_key="your-api-key")
bot.setup(["faq.pdf", "user-guide.pdf", "troubleshooting.pdf"])

# Ask questions
questions = [
    "How do I reset my password?",
    "What payment methods are accepted?",
    "How long does shipping take?"
]

for q in questions:
    result = bot.ask(q)
    print(f"\nQ: {q}")
    print(f"A: {result['answer']}")
    print(f"Confidence: {result['confidence']:.2f}")
    print(f"Sources: {len(result['sources'])}")

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Best Practices

answer = hippo.submit_ask(chat_id, question)

if answer.confidence_score < 0.7:
    print("Low confidence - verifying sources...")

    for source in answer.sources:
        print(f"Check: {source.file_name}, page {source.page_number}")
Always check sources for low-confidence answers.
# First attempt
answer1 = hippo.submit_ask(chat_id, "What's the policy?")

if answer1.confidence_score < 0.7:
    # Rephrase with more context
    answer2 = hippo.submit_ask(
        chat_id,
        "What is the company's refund policy for products?"
    )
More specific questions → Better answers
answer = hippo.submit_ask(chat_id, "What is the SLA?")

if len(answer.sources) == 0:
    print("No sources found - may need to upload SLA document")

    # Upload missing documentation
    hippo.upload_file(folder_id, "service-level-agreement.pdf")

    # Ask again
    answer = hippo.submit_ask(chat_id, "What is the SLA?")

# Initial question
a1 = hippo.submit_ask(chat_id, "How do I authenticate?")

# If answer mentions multiple methods, follow up
a2 = hippo.submit_ask(chat_id, "Which method is recommended?")

# Further clarification
a3 = hippo.submit_ask(chat_id, "Can you show an example?")
Conversation flow leads to better understanding

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Next Steps

Best Practices

Optimize answer quality and costs

Examples

Complete RAG workflow examples

Folder Management

Organize your knowledge bases
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