Add Multi-Turn vs Single-Turn Agent Comparison Example

17-multi-turn/README.md

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+# Multi-Turn vs Single-Turn Agent Testing
+
+This example demonstrates the differences between multi-turn and single-turn agents in Contextual AI through comprehensive testing scenarios.
+
+## Overview
+
+The notebook creates two agents with identical configurations except for their multi-turn settings:
+- **MultiTurnAgent**: Maintains conversation context across multiple messages
+- **SingleTurnAgent**: Treats each message independently without memory
+
+## What You'll Learn
+
+- How to enable/disable multi-turn functionality in Contextual AI agents
+- The difference in behavior between multi-turn and single-turn agents
+- How to use `conversation_id` to continue previous conversations
+- Best practices for testing agent memory capabilities
+
+## Key Concepts
+
+### Multi-Turn Agents
+- Remember conversation history using `conversation_id`
+- Can reference previous messages and maintain context
+- Ideal for conversational interfaces and complex interactions
+
+### Single-Turn Agents
+- Process each message independently
+- No memory of previous interactions
+- Suitable for stateless operations and simple Q&A
+
+### Conversation Management
+- The first message in a conversation generates a `conversation_id`
+- Subsequent messages use this ID to maintain context
+- Conversations can be resumed later using the same ID
+
+## Test Methodology
+
+The notebook follows a systematic approach:
+
+1. **Setup**: Creates two identical agents with different multi-turn settings
+2. **Initial Facts**: Shares the same information with both agents
+3. **Memory Testing**: Asks follow-up questions to test retention
+4. **Comparison**: Validates expected vs actual behavior
+5. **Recurring Conversations**: Tests conversation continuity
+
+### Test Scenarios
+
+| Scenario | MultiTurnAgent | SingleTurnAgent |
+|----------|----------------|-----------------|
+| Initial message with facts | ✓ Acknowledges | ✓ Acknowledges |
+| Follow-up question about facts | ✓ Remembers | ✗ Forgets |
+| Complex recall request | ✓ Full context | ✗ No context |
+| Conversation resumption | ✓ Continues | N/A |
+
+## Running the Notebook
+
+### Prerequisites
+- Contextual AI API key
+- Python environment with required packages
+
+### Setup Steps
+1. Update the `API_KEY` variable with your Contextual AI API key
+2. Run all cells sequentially
+3. Review the test results summary at the end
+
+### Expected Outputs
+- Multi-turn agent should pass memory tests (remember color and cats)
+- Single-turn agent should fail memory tests (forget previous information)
+- All 5 tests should pass if multi-turn functionality works correctly
+
+## Configuration Details
+
+### Agent Configuration
+```python
+agent_configs = {
+    "global_config": {
+        "enable_multi_turn": True,  # Key difference between agents
+        "enable_filter": False,
+        "enable_rerank": False,
+        "should_check_retrieval_need": True,
+    },
+    "reformulation_config": {
+        "enable_query_expansion": False,
+        "enable_query_decomposition": False,
+    },
+    "generate_response_config": {
+        "model": 'vertex_ai/gemini-2.0-flash-lite'
+    }
+}
+```
+
+### Query with Conversation ID
+```python
+response = client.agents.query.create(
+    agent_id=agent.id,
+    messages=[{"content": "Your message", "role": "user"}],
+    conversation_id=conversation_id  # Enables context retention
+)
+```
+
+## Test Data
+
+The example uses simple, verifiable facts:
+- **Favorite color**: Blue
+- **Pet count**: 3 cats
+- **Pet names**: Whiskers, Mittens, Shadow
+
+These concrete values make it easy to verify whether agents remember information correctly.
+
+## Cleanup
+
+The notebook includes an optional cleanup section to delete the test agents and datastore after experimentation. Uncomment the cleanup code if you want to remove the created resources.
+
+## Use Cases
+
+### Multi-Turn Agents Are Ideal For:
+- Customer service chatbots
+- Interactive tutorials
+- Complex problem-solving sessions
+- Personalized conversations
+
+### Single-Turn Agents Are Ideal For:
+- FAQ systems
+- Simple classification tasks
+- Stateless API endpoints
+- One-off queries
+
+## Troubleshooting
+
+### Common Issues
+- **Agent doesn't remember**: Ensure you're using the correct `conversation_id`
+- **Tests fail**: Check API key validity and agent configuration
+- **Unexpected behavior**: Verify the `enable_multi_turn` setting
+
+### Debugging Tips
+- Monitor conversation IDs in the output
+- Check agent responses for context clues
+- Validate test data matches expected values
+
+## Next Steps
+
+After understanding multi-turn behavior, consider:
+- Implementing conversation persistence
+- Adding conversation branching
+- Building stateful applications
+- Exploring advanced context management
+
+---
+
+**Note**: Remember to keep your API key secure and never commit it to version control.