Create sample dataset using llm

[AumPatel1]

Completed a TODO where llm is called for dataset generation keeping the orginal manual logic there as fallback

[greptile-apps]

Greptile Overview

Greptile Summary

This PR enhances sample data generation to use LLM-based realistic values instead of simple synthetic patterns. The main functional change is in create_input_sample() which now uses Provider to generate context-aware sample data (e.g., realistic ages, emails) and validates with Pydantic, with fallback to the original pattern-based generation.

Key changes:

• replaced hardcoded pattern generation (i * 10, etc.) with LLM calls that generate realistic values based on field names
• added Pydantic validation to ensure generated samples match schema
• preserved fallback behavior when LLM generation fails
• added comprehensive documentation (CODEBASE_STRUCTURE.md, COMPLETE_MODEL_BUILDING_WALKTHROUGH.md)
• fixed missing newline at EOF in metric.py

The implementation is solid with proper error handling, though the hardcoded provider string could be made configurable to align with patterns used elsewhere in the codebase.

Confidence Score: 4/5

• safe to merge with minor style consideration
• score reflects well-implemented feature with proper error handling and fallback logic, but includes a style suggestion about hardcoded provider configuration that would improve consistency with existing patterns
• no files require special attention - all changes are well-structured

Important Files Changed

File Analysis

Filename	Score	Overview
CODEBASE_STRUCTURE.md	5/5	New documentation file - comprehensive architectural overview with no issues
COMPLETE_MODEL_BUILDING_WALKTHROUGH.md	5/5	New documentation file - detailed model building walkthrough with no issues
plexe/internal/models/entities/metric.py	5/5	Formatting fix only - missing newline at EOF added, no functional changes
plexe/tools/datasets.py	4/5	Enhanced sample generation with LLM - uses Provider with fallback, validates with Pydantic. Minor hardcoded provider concern

[greptile-apps]

_{4 files reviewed, 1 comment}

_{Edit Code Review Agent Settings | Greptile}

[greptile-apps]

style: hardcoded openai/gpt-4o-mini provider prevents users from configuring model choice

other tools in this module receive llm_to_use parameter (see get_training_code_generation_tool, get_select_target_metric). consider adding provider parameter or getting from config

Suggested change

	provider = Provider("openai/gpt-4o-mini")
	# Use LLM to generate sensible sample values based on field names and types
	# TODO: make provider configurable via parameter
	provider = Provider("openai/gpt-4o-mini")

Prompt To Fix With AI

This is a comment left during a code review.
Path: plexe/tools/datasets.py
Line: 135:135

Comment:
**style:** hardcoded `openai/gpt-4o-mini` provider prevents users from configuring model choice

other tools in this module receive `llm_to_use` parameter (see `get_training_code_generation_tool`, `get_select_target_metric`). consider adding provider parameter or getting from config

```suggestion
        # Use LLM to generate sensible sample values based on field names and types
        # TODO: make provider configurable via parameter
        provider = Provider("openai/gpt-4o-mini")
```

How can I resolve this? If you propose a fix, please make it concise.