# Interactive Research Agent Session

## Overview

The Interactive Research Agent allows you to interact with the AI-powered Research Agent through a conversational CLI interface. The agent can learn from examples you provide and automatically generate code for new optimization features.

## Quick Start

### Run the Interactive Session

```bash
python examples/interactive_research_session.py
```

### Try the Demo

When the session starts, type `demo` to see an automated demonstration:

```
💬 Your request: demo
```

The demo will show:
1. **Learning from Example**: Agent learns XML material structure from a steel example
2. **Code Generation**: Automatically generates Python code (81 lines)
3. **Knowledge Reuse**: Second request reuses learned knowledge (no example needed!)

## How to Use

### Making Requests

Simply type your request in natural language:

```
💬 Your request: Create an NX material XML generator for aluminum
```

The agent will:
1. **Analyze** what it knows and what's missing
2. **Ask for examples** if it needs to learn something new
3. **Search** its knowledge base for existing patterns
4. **Generate code** from learned templates
5. **Save** the generated feature to a file

### Providing Examples

When the agent asks for an example, you have 3 options:

1. **Provide a file path:**
   ```
   Your choice: examples/my_example.xml
   ```

2. **Paste content directly:**
   ```
   Your choice: <?xml version="1.0"?>
   <MyExample>...</MyExample>
   ```

3. **Skip (if you don't have an example):**
   ```
   Your choice: skip
   ```

### Understanding the Output

The agent provides visual feedback at each step:

- 🔍 **Knowledge Gap Analysis**: Shows what's missing and confidence level
- 📋 **Research Plan**: Steps the agent will take to gather knowledge
- 🧠 **Knowledge Synthesized**: What the agent learned (schemas, patterns)
- 💻 **Code Generation**: Preview of generated Python code
- 💾 **Files Created**: Where the generated code was saved

### Confidence Levels

- **< 50%**: New domain - Learning required (will ask for examples)
- **50-80%**: Partial knowledge - Some research needed
- **> 80%**: Known domain - Can reuse existing knowledge

## Example Session

```
================================================================================
🤖 Interactive Research Agent Session
================================================================================

  Welcome! I'm your Research Agent. I can learn from examples and
  generate code for optimization features.

  Commands:
    • Type your request in natural language
    • Type 'demo' for a demonstration
    • Type 'quit' to exit

💬 Your request: Create NX material XML for titanium Ti-6Al-4V

--------------------------------------------------------------------------------
[Step 1] Analyzing Knowledge Gap
--------------------------------------------------------------------------------

  🔍 Knowledge Gap Analysis:

  Missing Features (1):
    • new_feature_required

  Missing Knowledge (1):
    • material

  Confidence Level: 80%
  📊 Status: Known domain - Can reuse existing knowledge

--------------------------------------------------------------------------------
[Step 2] Executing Research Plan
--------------------------------------------------------------------------------

  📋 Research Plan Created:

  I'll gather knowledge in 2 steps:

  1. 📚 Search Knowledge Base
     Expected confidence: 80%
     Search query: "material XML NX"

  2. 👤 Ask User For Example
     Expected confidence: 95%
     What I'll ask: "Could you provide an example of an NX material XML file?"

  ⚡ Executing Step 1/2: Search Knowledge Base
  ----------------------------------------------------------------------------
  🔍 Searching knowledge base for: "material XML NX"
  ✓ Found existing knowledge! Session: 2025-11-16_nx_materials_demo
    Confidence: 95%, Relevance: 85%

  ⚡ Executing Step 2/2: Ask User For Example
  ----------------------------------------------------------------------------
  ⊘ Skipping - Already have high confidence from knowledge base

--------------------------------------------------------------------------------
[Step 3] Synthesizing Knowledge
--------------------------------------------------------------------------------

  🧠 Knowledge Synthesized:

  Overall Confidence: 95%

  📄 Learned XML Structure:
    Root element: <PhysicalMaterial>
    Attributes: {'name': 'Steel_AISI_1020', 'version': '1.0'}
    Required fields (5):
      • Density
      • YoungModulus
      • PoissonRatio
      • ThermalExpansion
      • YieldStrength

--------------------------------------------------------------------------------
[Step 4] Generating Feature Code
--------------------------------------------------------------------------------

  🔨 Designing feature: create_nx_material_xml_for_t
    Category: engineering
    Lifecycle stage: all
    Input parameters: 5

  💻 Generating Python code...
    Generated 2327 characters (81 lines)
    ✓ Code is syntactically valid Python

  💾 Saved to: optimization_engine/custom_functions/create_nx_material_xml_for_t.py

================================================================================
✓ Request Completed Successfully!
================================================================================

  Generated file: optimization_engine/custom_functions/create_nx_material_xml_for_t.py
  Knowledge confidence: 95%
  Session saved: 2025-11-16_create_nx_material_xml_for_t

💬 Your request: quit

  👋 Goodbye! Session ended.
```

## Key Features

### 1. Knowledge Accumulation
- Agent remembers what it learns across sessions
- Second similar request doesn't require re-learning
- Knowledge base grows over time

### 2. Intelligent Research Planning
- Prioritizes reliable sources (user examples > MCP > web)
- Creates step-by-step research plan
- Explains what it will do before doing it

### 3. Pattern Recognition
- Extracts XML schemas from examples
- Identifies Python code patterns (functions, classes, imports)
- Learns relationships between inputs and outputs

### 4. Code Generation
- Generates complete Python modules with:
  - Docstrings and documentation
  - Type hints for all parameters
  - Example usage code
  - Error handling
- Code is syntactically validated before saving

### 5. Session Documentation
- Every research session is automatically documented
- Includes: user question, sources, findings, decisions
- Searchable for future knowledge retrieval

## Advanced Usage

### Auto Mode (for Testing)

For automated testing, you can run the session in auto-mode:

```python
from examples.interactive_research_session import InteractiveResearchSession

session = InteractiveResearchSession(auto_mode=True)
session.run_demo()  # Runs without user input prompts
```

### Programmatic Usage

You can also use the Research Agent programmatically:

```python
from optimization_engine.research_agent import ResearchAgent

agent = ResearchAgent()

# Identify what's missing
gap = agent.identify_knowledge_gap("Create NX modal analysis")

# Search existing knowledge
existing = agent.search_knowledge_base("modal analysis")

# Create research plan
plan = agent.create_research_plan(gap)

# ... execute plan and synthesize knowledge
```

## Troubleshooting

### "No matching session found"
- This is normal for new domains the agent hasn't seen before
- The agent will ask for an example to learn from

### "Confidence too low to generate code"
- Provide more detailed examples
- Try providing multiple examples of the same pattern
- Check that your example files are well-formed

### "Generated code has syntax errors"
- This is rare and indicates a bug in code generation
- Please report this with the example that caused it

## What's Next

The interactive session currently includes:
- ✅ Knowledge gap detection
- ✅ Knowledge base search and retrieval
- ✅ Learning from user examples
- ✅ Python code generation
- ✅ Session documentation

**Coming in future phases:**
- 🔜 MCP server integration (query NX documentation)
- 🔜 Web search integration (search online resources)
- 🔜 Multi-turn conversations with context
- 🔜 Code refinement based on feedback
- 🔜 Feature validation and testing

## Testing

Run the automated test:

```bash
python tests/test_interactive_session.py
```

This will demonstrate the complete workflow including:
- Learning from an example (steel material XML)
- Generating working Python code
- Reusing knowledge for a second request
- All without user interaction

## Support

For issues or questions:
- Check the existing research sessions in `knowledge_base/research_sessions/`
- Review generated code in `optimization_engine/custom_functions/`
- See test examples in `tests/test_*.py`