# Simple Q&A Agent with Signature-Based Programming

## Overview
Demonstrates the foundational pattern for building agents with signature-based programming. This agent handles direct question-answering with structured input/output patterns and comprehensive error handling.

## Use Case
- Customer support chatbots
- FAQ automation systems
- Knowledge base query interfaces
- Educational Q&A assistants

## Agent Specification

### Core Functionality
- **Input**: Natural language questions
- **Processing**: LLM-based reasoning with structured prompts
- **Output**: Formatted answers with confidence scores
- **Memory**: None (stateless for simplicity)

### Signature Pattern
```python
class QASignature(dspy.Signature):
    """Answer questions accurately and concisely with confidence scoring."""
    question: str = dspy.InputField(desc="The question to answer")
    context: str = dspy.InputField(desc="Additional context if available")

    answer: str = dspy.OutputField(desc="Clear, accurate answer")
    confidence: float = dspy.OutputField(desc="Confidence score 0.0-1.0")
    reasoning: str = dspy.OutputField(desc="Brief explanation of reasoning")
```

## Expected Execution Flow

### Phase 1: Initialization (0-50ms)
```
[00:00:000] WorkflowBuilder initialized
[00:00:015] QASignature defined with input/output fields
[00:00:032] LLMAgentNode configured with signature
[00:00:045] Workflow built and validated
```

### Phase 2: Question Processing (50-1500ms)
```
[00:00:050] Input received: {"question": "What is machine learning?", "context": ""}
[00:00:065] Signature validation passed
[00:00:080] LLM prompt constructed with signature template
[00:00:095] OpenAI API call initiated
[00:00:850] LLM response received
[00:00:875] Output parsing and validation
[00:00:890] Confidence scoring applied
```

### Phase 3: Response Formatting (1500-1600ms)
```
[00:01:500] Structured output generated:
{
  "answer": "Machine learning is a subset of AI that enables computers to learn patterns from data without explicit programming.",
  "confidence": 0.92,
  "reasoning": "Based on standard ML definition with high certainty given clear question."
}
[00:01:580] Workflow execution completed
[00:01:595] Results returned to caller
```

## Technical Requirements

### Dependencies
```python
# Core Kailash SDK
from kailash.workflow.builder import WorkflowBuilder
from kailash.runtime.local import LocalRuntime
from kailash.nodes.llm_agent import LLMAgentNode

# Signature programming
import dspy
from typing import Optional
```

### Configuration
```yaml
llm_config:
  provider: "openai"
  model: "gpt-4"
  temperature: 0.1
  max_tokens: 300

workflow_config:
  timeout: 30
  retry_attempts: 3
  log_level: "INFO"
```

### Memory Requirements
- **Runtime Memory**: ~50MB
- **Model Context**: 4K tokens maximum
- **Response Cache**: Optional, 100MB max

## Success Criteria

### Functional Requirements
- ✅ Processes questions in under 2 seconds
- ✅ Returns structured output with all required fields
- ✅ Handles edge cases (empty questions, long queries)
- ✅ Maintains confidence scoring accuracy >90%

### Quality Requirements
- ✅ Answer relevance score >85%
- ✅ Reasoning quality validated by humans
- ✅ Consistent output format across queries
- ✅ Graceful degradation on API failures

### Performance Requirements
- ✅ Response time: <2 seconds (95th percentile)
- ✅ Throughput: >100 queries/minute
- ✅ Memory usage: <100MB resident
- ✅ Error rate: <1% for valid inputs

## Enterprise Considerations

### Security
- Input validation and sanitization
- API key management and rotation
- Rate limiting and abuse prevention
- Audit logging for all interactions

### Compliance
- Response content filtering
- PII detection and redaction
- Regulatory compliance (GDPR, HIPAA)
- Audit trail maintenance

### Monitoring
- Response time distribution
- Confidence score analytics
- Error rate and failure modes
- API usage and cost tracking

## Error Scenarios

### API Failures
```python
# Expected behavior on OpenAI API timeout
{
  "answer": "I'm temporarily unable to process your question. Please try again.",
  "confidence": 0.0,
  "reasoning": "Service temporarily unavailable",
  "error_code": "API_TIMEOUT"
}
```

### Invalid Input
```python
# Response to malformed or empty questions
{
  "answer": "Please provide a clear question for me to answer.",
  "confidence": 0.0,
  "reasoning": "Invalid or empty input received",
  "error_code": "INVALID_INPUT"
}
```

### Content Filtering
```python
# Response when content violates policies
{
  "answer": "I cannot provide information on this topic due to content policies.",
  "confidence": 0.0,
  "reasoning": "Content filtered for policy compliance",
  "error_code": "CONTENT_FILTERED"
}
```

## Testing Strategy

### Unit Tests
- Signature validation
- Input/output formatting
- Error handling scenarios
- Configuration management

### Integration Tests
- End-to-end workflow execution
- API integration validation
- Performance benchmarking
- Memory usage profiling

### Load Tests
- Concurrent query handling
- Rate limit validation
- Resource exhaustion scenarios
- Recovery and failover testing