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Question Answering System

Build a question answering system with DSPy Code.

Overview

This tutorial shows you how to build a question answering system that can answer questions based on context.

Step 1: Start and Initialize

dspy-code
/init
/connect ollama llama3.1:8b

Step 2: Create the QA System

Create a question answering module that takes a question and context and generates an answer

Step 3: Review Generated Code

import dspy

class QuestionAnswering(dspy.Module):
    """Question answering module."""

    def __init__(self):
        super().__init__()
        self.answer = dspy.ChainOfThought(
            "context, question -> reasoning -> answer"
        )

    def forward(self, context, question):
        return self.answer(context=context, question=question)

Step 4: Save and Test

/save qa_system.py
/run qa_system.py --input context="DSPy is a framework..." question="What is DSPy?"

Complete Example

import dspy

# Configure model
lm = dspy.LM(model="ollama/llama3.1:8b")
dspy.configure(lm=lm)

# Create QA module
class QuestionAnswering(dspy.Module):
    def __init__(self):
        super().__init__()
        self.answer = dspy.ChainOfThought(
            "context, question -> reasoning -> answer"
        )

    def forward(self, context, question):
        return self.answer(context=context, question=question)

# Use it
qa = QuestionAnswering()
result = qa(
    context="DSPy is a framework for building AI systems.",
    question="What is DSPy?"
)
print(result.answer)

Enhancements

Add Reasoning

self.answer = dspy.ChainOfThought(
    "context, question -> reasoning -> answer"
)

Add Confidence

class QuestionAnswering(dspy.Module):
    def __init__(self):
        super().__init__()
        self.answer = dspy.ChainOfThought(
            "context, question -> reasoning -> answer, confidence"
        )

Next Steps

  • Add retrieval for RAG
  • Optimize with GEPA
  • Add evaluation metrics

For more details, see RAG System