Add new Mini-RAG logo and clean repository structure
- Added beautiful new Mini-RAG logo with teal and purple colors - Updated README to use new logo - Cleaned up repository by removing development scripts and artifacts - Repository structure now clean and professional - Ready for GitHub release preparation
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# FSS-Mini-RAG Configuration
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# Edit this file to customize indexing and search behavior
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# See docs/GETTING_STARTED.md for detailed explanations
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# Text chunking settings
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chunking:
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max_size: 2000 # Maximum characters per chunk
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min_size: 150 # Minimum characters per chunk
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strategy: semantic # 'semantic' (language-aware) or 'fixed'
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# Large file streaming settings
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streaming:
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enabled: true
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threshold_bytes: 1048576 # Files larger than this use streaming (1MB)
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# File processing settings
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files:
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min_file_size: 50 # Skip files smaller than this
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exclude_patterns:
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- "node_modules/**"
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- ".git/**"
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- "__pycache__/**"
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- "*.pyc"
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- ".venv/**"
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- "venv/**"
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- "build/**"
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- "dist/**"
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include_patterns:
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- "**/*" # Include all files by default
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# Embedding generation settings
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embedding:
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preferred_method: ollama # 'ollama', 'ml', 'hash', or 'auto'
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ollama_model: nomic-embed-text
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ollama_host: localhost:11434
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ml_model: sentence-transformers/all-MiniLM-L6-v2
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batch_size: 32 # Embeddings processed per batch
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# Search behavior settings
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search:
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default_limit: 10 # Default number of results
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enable_bm25: true # Enable keyword matching boost
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similarity_threshold: 0.1 # Minimum similarity score
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expand_queries: false # Enable automatic query expansion
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# LLM synthesis and query expansion settings
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llm:
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ollama_host: localhost:11434
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synthesis_model: auto # 'auto', 'qwen3:1.7b', etc.
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expansion_model: auto # Usually same as synthesis_model
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max_expansion_terms: 8 # Maximum terms to add to queries
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enable_synthesis: false # Enable synthesis by default
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synthesis_temperature: 0.3 # LLM temperature for analysis
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@ -1 +0,0 @@
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chunking
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12
README.md
12
README.md
@ -1,14 +1,10 @@
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# FSS-Mini-RAG
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> **A lightweight, educational RAG system that actually works**
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> *Built for beginners who want results, and developers who want to understand how RAG really works*
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## Demo
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*See it in action: index a project and search semantically in seconds*
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## How It Works
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```mermaid
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@ -112,9 +108,7 @@ That's it. No external dependencies, no configuration required, no PhD in comput
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./rag-mini status ~/new-project # Check index health
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```
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*Advanced usage: semantic search with synthesis and exploration modes*
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*Ready to try semantic search with synthesis and exploration modes? Follow the quick start below!*
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## Installation Options
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@ -1,290 +0,0 @@
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#!/usr/bin/env python3
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"""
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Asciinema to GIF Converter
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Converts .cast files to optimized GIF animations without external services.
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"""
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import os
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import sys
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import json
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import argparse
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import subprocess
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from pathlib import Path
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from typing import List, Dict, Any
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import tempfile
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import shutil
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class AsciinemaToGIF:
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def __init__(self):
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self.temp_dir = None
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def check_dependencies(self) -> Dict[str, bool]:
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"""Check if required tools are available."""
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tools = {
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'ffmpeg': self._check_command('ffmpeg'),
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'convert': self._check_command('convert'), # ImageMagick
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'gifsicle': self._check_command('gifsicle') # Optional optimizer
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}
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return tools
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def _check_command(self, command: str) -> bool:
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"""Check if a command is available."""
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return shutil.which(command) is not None
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def install_instructions(self):
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"""Show installation instructions for missing dependencies."""
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print("📦 Required Dependencies:")
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print()
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print("Ubuntu/Debian:")
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print(" sudo apt install ffmpeg imagemagick gifsicle")
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print()
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print("macOS:")
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print(" brew install ffmpeg imagemagick gifsicle")
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print()
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print("Arch Linux:")
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print(" sudo pacman -S ffmpeg imagemagick gifsicle")
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def parse_cast_file(self, cast_path: Path) -> Dict[str, Any]:
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"""Parse asciinema .cast file."""
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with open(cast_path, 'r') as f:
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lines = f.readlines()
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# First line is header
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header = json.loads(lines[0])
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# Remaining lines are events
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events = []
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for line in lines[1:]:
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if line.strip():
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events.append(json.loads(line))
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return {
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'header': header,
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'events': events,
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'width': header.get('width', 80),
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'height': header.get('height', 24)
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}
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def create_frames(self, cast_data: Dict[str, Any], output_dir: Path) -> List[Path]:
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"""Create individual frame images from cast data."""
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print("🎬 Creating frames...")
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width = cast_data['width']
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height = cast_data['height']
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events = cast_data['events']
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# Terminal state
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screen = [[' ' for _ in range(width)] for _ in range(height)]
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cursor_x, cursor_y = 0, 0
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frames = []
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frame_count = 0
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last_time = 0
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for event in events:
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timestamp, event_type, data = event
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# Calculate delay
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delay = timestamp - last_time
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last_time = timestamp
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if event_type == 'o': # Output event
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# Process terminal output
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for char in data:
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if char == '\n':
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cursor_y += 1
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cursor_x = 0
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if cursor_y >= height:
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# Scroll up
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screen = screen[1:] + [[' ' for _ in range(width)]]
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cursor_y = height - 1
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elif char == '\r':
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cursor_x = 0
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elif char == '\033':
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# Skip ANSI escape sequences (simplified)
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continue
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elif char.isprintable():
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if cursor_x < width and cursor_y < height:
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screen[cursor_y][cursor_x] = char
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cursor_x += 1
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# Create frame if significant delay or content change
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if delay > 0.1 or frame_count == 0:
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frame_path = self._create_frame_image(screen, output_dir, frame_count, delay)
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frames.append((frame_path, delay))
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frame_count += 1
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return frames
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def _create_frame_image(self, screen: List[List[str]], output_dir: Path,
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frame_num: int, delay: float) -> Path:
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"""Create a single frame image using ImageMagick."""
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# Convert screen to text
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text_content = []
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for row in screen:
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line = ''.join(row).rstrip()
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text_content.append(line)
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# Create text file
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text_file = output_dir / f"frame_{frame_num:04d}.txt"
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with open(text_file, 'w') as f:
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f.write('\n'.join(text_content))
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# Convert to image using ImageMagick
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image_file = output_dir / f"frame_{frame_num:04d}.png"
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cmd = [
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'convert',
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'-font', 'Liberation-Mono', # Monospace font
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'-pointsize', '12',
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'-background', '#1e1e1e', # Dark background
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'-fill', '#d4d4d4', # Light text
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'-gravity', 'NorthWest',
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f'label:@{text_file}',
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str(image_file)
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]
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try:
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subprocess.run(cmd, check=True, capture_output=True)
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return image_file
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except subprocess.CalledProcessError as e:
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print(f"❌ Failed to create frame {frame_num}: {e}")
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return None
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def create_gif(self, frames: List[tuple], output_path: Path, fps: int = 10) -> bool:
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"""Create GIF from frame images using ffmpeg."""
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print("🎞️ Creating GIF...")
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if not frames:
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print("❌ No frames to process")
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return False
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# Create ffmpeg input file list
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input_list = self.temp_dir / "input_list.txt"
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with open(input_list, 'w') as f:
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for frame_path, delay in frames:
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if frame_path and frame_path.exists():
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duration = max(delay, 0.1) # Minimum 0.1s per frame
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f.write(f"file '{frame_path}'\n")
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f.write(f"duration {duration}\n")
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# Create GIF with ffmpeg
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cmd = [
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'ffmpeg',
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'-f', 'concat',
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'-safe', '0',
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'-i', str(input_list),
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'-vf', 'fps=10,scale=800:-1:flags=lanczos,palettegen=reserve_transparent=0',
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'-y',
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str(output_path)
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]
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try:
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subprocess.run(cmd, check=True, capture_output=True)
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return True
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except subprocess.CalledProcessError as e:
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print(f"❌ FFmpeg failed: {e}")
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return False
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def optimize_gif(self, gif_path: Path) -> bool:
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"""Optimize GIF using gifsicle."""
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if not self._check_command('gifsicle'):
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return True # Skip if not available
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print("🗜️ Optimizing GIF...")
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optimized_path = gif_path.with_suffix('.optimized.gif')
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cmd = [
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'gifsicle',
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'-O3',
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'--lossy=80',
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'--colors', '256',
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str(gif_path),
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'-o', str(optimized_path)
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]
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try:
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subprocess.run(cmd, check=True, capture_output=True)
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# Replace original with optimized
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shutil.move(optimized_path, gif_path)
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return True
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except subprocess.CalledProcessError as e:
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print(f"⚠️ Optimization failed: {e}")
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return False
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def convert(self, cast_path: Path, output_path: Path, fps: int = 10) -> bool:
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"""Convert asciinema cast file to GIF."""
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print(f"🎯 Converting {cast_path.name} to GIF...")
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# Check dependencies
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deps = self.check_dependencies()
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missing = [tool for tool, available in deps.items() if not available and tool != 'gifsicle']
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if missing:
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print(f"❌ Missing required tools: {', '.join(missing)}")
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print()
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self.install_instructions()
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return False
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# Create temporary directory
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self.temp_dir = Path(tempfile.mkdtemp(prefix='asciinema_gif_'))
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try:
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# Parse cast file
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print("📖 Parsing cast file...")
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cast_data = self.parse_cast_file(cast_path)
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# Create frames
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frames = self.create_frames(cast_data, self.temp_dir)
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if not frames:
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print("❌ No frames created")
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return False
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# Create GIF
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success = self.create_gif(frames, output_path, fps)
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if success:
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# Optimize
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self.optimize_gif(output_path)
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# Show results
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size_mb = output_path.stat().st_size / (1024 * 1024)
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print(f"✅ GIF created: {output_path}")
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print(f"📏 Size: {size_mb:.2f} MB")
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return True
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else:
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return False
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finally:
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# Cleanup
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if self.temp_dir and self.temp_dir.exists():
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shutil.rmtree(self.temp_dir)
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def main():
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parser = argparse.ArgumentParser(description='Convert asciinema recordings to GIF')
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parser.add_argument('input', type=Path, help='Input .cast file')
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parser.add_argument('-o', '--output', type=Path, help='Output .gif file (default: same name as input)')
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parser.add_argument('--fps', type=int, default=10, help='Frames per second (default: 10)')
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args = parser.parse_args()
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if not args.input.exists():
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print(f"❌ Input file not found: {args.input}")
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sys.exit(1)
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if not args.output:
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args.output = args.input.with_suffix('.gif')
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converter = AsciinemaToGIF()
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success = converter.convert(args.input, args.output, args.fps)
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if success:
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print("🎉 Conversion complete!")
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else:
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print("💥 Conversion failed!")
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sys.exit(1)
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if __name__ == '__main__':
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main()
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BIN
assets/Fss_Mini_Rag.png
Normal file
BIN
assets/Fss_Mini_Rag.png
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After Width: | Height: | Size: 198 KiB |
BIN
assets/Fss_Rag.png
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BIN
assets/Fss_Rag.png
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After Width: | Height: | Size: 221 KiB |
@ -1,25 +0,0 @@
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# Icon Placeholder
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The current `icon.svg` is a simple placeholder. Here's the design concept:
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🔍 **Search magnifying glass** - Core search functionality
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📄 **Code brackets** - Code-focused system
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🧠 **Neural network dots** - AI/embedding intelligence
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📝 **Text lines** - Document processing
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## Design Ideas for Final Icon
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- **Colors**: Blue (#1976d2) for trust/tech, Green (#4caf50) for code, Orange (#ff9800) for AI
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- **Elements**: Search + Code + AI/Brain + Simplicity
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- **Style**: Clean, modern, friendly (not intimidating)
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- **Size**: Works well at 32x32 and 128x128
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## Suggested Improvements
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1. More polished magnifying glass with reflection
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2. Cleaner code bracket styling
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3. More sophisticated neural network representation
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4. Perhaps a small "mini" indicator to emphasize lightweight nature
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5. Consider a folder or document icon to represent project indexing
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The current SVG provides the basic structure and can be refined into a professional icon.
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BIN
assets/demo.gif
BIN
assets/demo.gif
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Before Width: | Height: | Size: 179 KiB |
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<svg width="128" height="128" viewBox="0 0 128 128" xmlns="http://www.w3.org/2000/svg">
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<!-- Background circle -->
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<circle cx="64" cy="64" r="60" fill="#e3f2fd" stroke="#1976d2" stroke-width="4"/>
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<!-- Search magnifying glass -->
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<circle cx="48" cy="48" r="18" fill="none" stroke="#1976d2" stroke-width="4"/>
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<line x1="62" y1="62" x2="76" y2="76" stroke="#1976d2" stroke-width="4" stroke-linecap="round"/>
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<!-- Code brackets -->
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<path d="M20 35 L10 45 L20 55" fill="none" stroke="#4caf50" stroke-width="3" stroke-linecap="round"/>
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<path d="M108 35 L118 45 L108 55" fill="none" stroke="#4caf50" stroke-width="3" stroke-linecap="round"/>
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<!-- Neural network dots -->
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<circle cx="85" cy="25" r="3" fill="#ff9800"/>
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<circle cx="100" cy="35" r="3" fill="#ff9800"/>
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<circle cx="90" cy="45" r="3" fill="#ff9800"/>
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<circle cx="105" cy="55" r="3" fill="#ff9800"/>
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<!-- Connection lines -->
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<line x1="85" y1="25" x2="100" y2="35" stroke="#ff9800" stroke-width="2" opacity="0.7"/>
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<line x1="100" y1="35" x2="90" y2="45" stroke="#ff9800" stroke-width="2" opacity="0.7"/>
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<line x1="90" y1="45" x2="105" y2="55" stroke="#ff9800" stroke-width="2" opacity="0.7"/>
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<!-- Text elements -->
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<rect x="15" y="75" width="25" height="3" fill="#666" rx="1"/>
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<rect x="15" y="82" width="35" height="3" fill="#666" rx="1"/>
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<rect x="15" y="89" width="20" height="3" fill="#666" rx="1"/>
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<rect x="60" y="85" width="30" height="3" fill="#2196f3" rx="1"/>
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<rect x="60" y="92" width="25" height="3" fill="#2196f3" rx="1"/>
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<rect x="60" y="99" width="35" height="3" fill="#2196f3" rx="1"/>
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<!-- "RAG" text -->
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<text x="64" y="118" text-anchor="middle" font-family="Arial, sans-serif" font-size="14" font-weight="bold" fill="#1976d2">RAG</text>
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</svg>
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Before Width: | Height: | Size: 1.7 KiB |
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#!/usr/bin/env python3
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"""
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Script to completely remove all Mini-RAG references from the FSS-Mini-RAG codebase.
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This ensures the repository is completely independent and avoids any licensing issues.
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"""
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import os
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import shutil
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import re
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from pathlib import Path
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from typing import Dict, List, Tuple
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class Mini-RAGCleanup:
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def __init__(self, project_root: Path):
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self.project_root = Path(project_root).resolve()
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self.replacements = {
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# Directory/module names
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'mini_rag': 'mini_rag',
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'mini-rag': 'mini-rag',
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# Class names and references
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'MiniRAG': 'MiniRAG',
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'Mini RAG': 'Mini RAG',
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'Mini RAG': 'mini rag',
|
||||
'mini_rag': 'MINI_RAG',
|
||||
|
||||
# File paths and imports
|
||||
'from mini_rag': 'from mini_rag',
|
||||
'import mini_rag': 'import mini_rag',
|
||||
'.mini-rag': '.mini-rag',
|
||||
|
||||
# Comments and documentation
|
||||
'Mini-RAG': 'Mini-RAG',
|
||||
'Mini-RAG': 'mini-rag',
|
||||
|
||||
# Specific technical references
|
||||
'the development environment': 'the development environment',
|
||||
'AI assistant': 'AI assistant',
|
||||
'Mini-RAG\'s': 'the system\'s',
|
||||
|
||||
# Config and metadata
|
||||
'mini_': 'mini_',
|
||||
'mini_': 'Mini_',
|
||||
}
|
||||
|
||||
self.files_to_rename = []
|
||||
self.dirs_to_rename = []
|
||||
self.files_modified = []
|
||||
|
||||
def scan_for_references(self) -> Dict[str, int]:
|
||||
"""Scan for all Mini-RAG references and return counts."""
|
||||
references = {}
|
||||
|
||||
for root, dirs, files in os.walk(self.project_root):
|
||||
# Skip git directory
|
||||
if '.git' in root:
|
||||
continue
|
||||
|
||||
for file in files:
|
||||
if file.endswith(('.py', '.md', '.sh', '.yaml', '.json', '.txt')):
|
||||
file_path = Path(root) / file
|
||||
try:
|
||||
with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
|
||||
content = f.read()
|
||||
|
||||
for old_ref in self.replacements.keys():
|
||||
count = content.lower().count(old_ref.lower())
|
||||
if count > 0:
|
||||
if old_ref not in references:
|
||||
references[old_ref] = 0
|
||||
references[old_ref] += count
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not scan {file_path}: {e}")
|
||||
|
||||
return references
|
||||
|
||||
def rename_directories(self):
|
||||
"""Rename directories with Mini-RAG references."""
|
||||
print("🔄 Renaming directories...")
|
||||
|
||||
# Find directories to rename
|
||||
for root, dirs, files in os.walk(self.project_root):
|
||||
if '.git' in root:
|
||||
continue
|
||||
|
||||
for dir_name in dirs:
|
||||
if 'Mini-RAG' in dir_name.lower():
|
||||
old_path = Path(root) / dir_name
|
||||
new_name = dir_name.replace('mini_rag', 'mini_rag').replace('mini-rag', 'mini-rag')
|
||||
new_path = Path(root) / new_name
|
||||
self.dirs_to_rename.append((old_path, new_path))
|
||||
|
||||
# Actually rename directories (do this carefully with git)
|
||||
for old_path, new_path in self.dirs_to_rename:
|
||||
if old_path.exists():
|
||||
print(f" 📁 {old_path.name} → {new_path.name}")
|
||||
# Use git mv to preserve history
|
||||
try:
|
||||
os.system(f'git mv "{old_path}" "{new_path}"')
|
||||
except Exception as e:
|
||||
print(f" Warning: git mv failed, using regular rename: {e}")
|
||||
shutil.move(str(old_path), str(new_path))
|
||||
|
||||
def update_file_contents(self):
|
||||
"""Update file contents to replace Mini-RAG references."""
|
||||
print("📝 Updating file contents...")
|
||||
|
||||
for root, dirs, files in os.walk(self.project_root):
|
||||
if '.git' in root:
|
||||
continue
|
||||
|
||||
for file in files:
|
||||
if file.endswith(('.py', '.md', '.sh', '.yaml', '.json', '.txt')):
|
||||
file_path = Path(root) / file
|
||||
|
||||
try:
|
||||
with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
|
||||
original_content = f.read()
|
||||
|
||||
modified_content = original_content
|
||||
changes_made = False
|
||||
|
||||
# Apply replacements in order (most specific first)
|
||||
sorted_replacements = sorted(self.replacements.items(),
|
||||
key=lambda x: len(x[0]), reverse=True)
|
||||
|
||||
for old_ref, new_ref in sorted_replacements:
|
||||
if old_ref in modified_content:
|
||||
modified_content = modified_content.replace(old_ref, new_ref)
|
||||
changes_made = True
|
||||
|
||||
# Also handle case variations
|
||||
if old_ref.lower() in modified_content.lower():
|
||||
# Use regex for case-insensitive replacement
|
||||
pattern = re.escape(old_ref)
|
||||
modified_content = re.sub(pattern, new_ref, modified_content, flags=re.IGNORECASE)
|
||||
changes_made = True
|
||||
|
||||
# Write back if changes were made
|
||||
if changes_made and modified_content != original_content:
|
||||
with open(file_path, 'w', encoding='utf-8') as f:
|
||||
f.write(modified_content)
|
||||
self.files_modified.append(file_path)
|
||||
print(f" 📄 Updated: {file_path.relative_to(self.project_root)}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not process {file_path}: {e}")
|
||||
|
||||
def update_imports_and_paths(self):
|
||||
"""Update Python imports and file paths."""
|
||||
print("🔗 Updating imports and paths...")
|
||||
|
||||
# Special handling for Python imports
|
||||
for root, dirs, files in os.walk(self.project_root):
|
||||
if '.git' in root:
|
||||
continue
|
||||
|
||||
for file in files:
|
||||
if file.endswith('.py'):
|
||||
file_path = Path(root) / file
|
||||
|
||||
try:
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
|
||||
# Fix relative imports
|
||||
content = re.sub(r'from \.mini_rag', 'from .mini_rag', content)
|
||||
content = re.sub(r'from mini_rag', 'from mini_rag', content)
|
||||
content = re.sub(r'import mini_rag', 'import mini_rag', content)
|
||||
|
||||
# Fix file paths in strings
|
||||
content = content.replace("'mini_rag'", "'mini_rag'")
|
||||
content = content.replace('"mini_rag"', '"mini_rag"')
|
||||
content = content.replace("'mini-rag'", "'mini-rag'")
|
||||
content = content.replace('"mini-rag"', '"mini-rag"')
|
||||
content = content.replace("'.mini-rag'", "'.mini-rag'")
|
||||
content = content.replace('".mini-rag"', '".mini-rag"')
|
||||
|
||||
with open(file_path, 'w', encoding='utf-8') as f:
|
||||
f.write(content)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not update imports in {file_path}: {e}")
|
||||
|
||||
def verify_cleanup(self) -> Tuple[int, List[str]]:
|
||||
"""Verify that cleanup was successful."""
|
||||
print("🔍 Verifying cleanup...")
|
||||
|
||||
remaining_refs = []
|
||||
total_count = 0
|
||||
|
||||
for root, dirs, files in os.walk(self.project_root):
|
||||
if '.git' in root:
|
||||
continue
|
||||
|
||||
for file in files:
|
||||
if file.endswith(('.py', '.md', '.sh', '.yaml', '.json', '.txt')):
|
||||
file_path = Path(root) / file
|
||||
try:
|
||||
with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
|
||||
content = f.read()
|
||||
|
||||
# Look for any remaining "Mini-RAG" references (case insensitive)
|
||||
lines = content.split('\n')
|
||||
for i, line in enumerate(lines, 1):
|
||||
if 'Mini-RAG' in line.lower():
|
||||
remaining_refs.append(f"{file_path}:{i}: {line.strip()}")
|
||||
total_count += 1
|
||||
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return total_count, remaining_refs
|
||||
|
||||
def run_cleanup(self):
|
||||
"""Run the complete cleanup process."""
|
||||
print("🧹 Starting Mini-RAG Reference Cleanup")
|
||||
print("=" * 50)
|
||||
|
||||
# Initial scan
|
||||
print("📊 Scanning for Mini-RAG references...")
|
||||
initial_refs = self.scan_for_references()
|
||||
print(f"Found {sum(initial_refs.values())} total references")
|
||||
for ref, count in sorted(initial_refs.items(), key=lambda x: x[1], reverse=True):
|
||||
if count > 0:
|
||||
print(f" • {ref}: {count} occurrences")
|
||||
print()
|
||||
|
||||
# Rename directories first
|
||||
self.rename_directories()
|
||||
|
||||
# Update file contents
|
||||
self.update_file_contents()
|
||||
|
||||
# Fix imports and paths
|
||||
self.update_imports_and_paths()
|
||||
|
||||
# Verify cleanup
|
||||
remaining_count, remaining_refs = self.verify_cleanup()
|
||||
|
||||
print("\n" + "=" * 50)
|
||||
print("🎯 Cleanup Summary:")
|
||||
print(f"📁 Directories renamed: {len(self.dirs_to_rename)}")
|
||||
print(f"📄 Files modified: {len(self.files_modified)}")
|
||||
print(f"⚠️ Remaining references: {remaining_count}")
|
||||
|
||||
if remaining_refs:
|
||||
print("\nRemaining Mini-RAG references to review:")
|
||||
for ref in remaining_refs[:10]: # Show first 10
|
||||
print(f" • {ref}")
|
||||
if len(remaining_refs) > 10:
|
||||
print(f" ... and {len(remaining_refs) - 10} more")
|
||||
|
||||
if remaining_count == 0:
|
||||
print("✅ Cleanup successful! No Mini-RAG references remain.")
|
||||
else:
|
||||
print("⚠️ Some references remain - please review manually.")
|
||||
|
||||
return remaining_count == 0
|
||||
|
||||
def main():
|
||||
project_root = Path(__file__).parent
|
||||
cleaner = Mini-RAGCleanup(project_root)
|
||||
|
||||
success = cleaner.run_cleanup()
|
||||
|
||||
if success:
|
||||
print("\n🎉 Ready to commit changes!")
|
||||
print("Next steps:")
|
||||
print("1. Review changes: git status")
|
||||
print("2. Test the application: ./rag-mini --help")
|
||||
print("3. Commit changes: git add . && git commit -m 'Remove all Mini-RAG references'")
|
||||
else:
|
||||
print("\n⚠️ Manual review required before committing.")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,123 +0,0 @@
|
||||
#!/bin/bash
|
||||
# Create both demo GIFs for FSS-Mini-RAG
|
||||
|
||||
echo "🎬 FSS-Mini-RAG Demo GIF Creation Script"
|
||||
echo "========================================"
|
||||
echo
|
||||
|
||||
# Check dependencies
|
||||
if ! command -v asciinema &> /dev/null; then
|
||||
echo "❌ asciinema not found. Install with:"
|
||||
echo " curl -sL https://asciinema.org/install | sh"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if ! command -v agg &> /dev/null; then
|
||||
echo "❌ agg not found. Install with:"
|
||||
echo " cargo install --git https://github.com/asciinema/agg"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo "✅ Dependencies found: asciinema, agg"
|
||||
echo
|
||||
|
||||
# Create recordings directory
|
||||
mkdir -p recordings
|
||||
|
||||
# Demo 1: Synthesis Mode
|
||||
echo "🚀 Creating Synthesis Mode Demo..."
|
||||
echo "==================================="
|
||||
echo "This demo shows fast RAG search with AI synthesis"
|
||||
echo
|
||||
read -p "Press Enter to record Synthesis Mode demo..."
|
||||
|
||||
echo "Recording in 3 seconds..."
|
||||
sleep 1
|
||||
echo "2..."
|
||||
sleep 1
|
||||
echo "1..."
|
||||
sleep 1
|
||||
|
||||
asciinema rec recordings/synthesis_demo.cast -c "python3 create_synthesis_demo.py" --overwrite
|
||||
|
||||
echo
|
||||
echo "🎨 Converting to GIF..."
|
||||
agg recordings/synthesis_demo.cast recordings/synthesis_demo.gif
|
||||
|
||||
echo "✅ Synthesis demo saved: recordings/synthesis_demo.gif"
|
||||
echo
|
||||
|
||||
# Demo 2: Exploration Mode
|
||||
echo "🧠 Creating Exploration Mode Demo..."
|
||||
echo "===================================="
|
||||
echo "This demo shows interactive thinking mode with conversation memory"
|
||||
echo
|
||||
read -p "Press Enter to record Exploration Mode demo..."
|
||||
|
||||
echo "Recording in 3 seconds..."
|
||||
sleep 1
|
||||
echo "2..."
|
||||
sleep 1
|
||||
echo "1..."
|
||||
sleep 1
|
||||
|
||||
asciinema rec recordings/exploration_demo.cast -c "python3 create_exploration_demo.py" --overwrite
|
||||
|
||||
echo
|
||||
echo "🎨 Converting to GIF..."
|
||||
agg recordings/exploration_demo.gif recordings/exploration_demo.gif
|
||||
|
||||
echo "✅ Exploration demo saved: recordings/exploration_demo.gif"
|
||||
echo
|
||||
|
||||
# Summary
|
||||
echo "🎉 DEMO CREATION COMPLETE!"
|
||||
echo "=========================="
|
||||
echo
|
||||
echo "📁 Created files:"
|
||||
echo " • recordings/synthesis_demo.cast"
|
||||
echo " • recordings/synthesis_demo.gif"
|
||||
echo " • recordings/exploration_demo.cast"
|
||||
echo " • recordings/exploration_demo.gif"
|
||||
echo
|
||||
echo "💡 Usage suggestions:"
|
||||
echo " • Use synthesis_demo.gif to show fast RAG search capabilities"
|
||||
echo " • Use exploration_demo.gif to show interactive learning features"
|
||||
echo " • Both demonstrate the clean two-mode architecture"
|
||||
echo
|
||||
echo "🚀 Upload to GitHub:"
|
||||
echo " • Replace existing demo.gif with synthesis_demo.gif for main README"
|
||||
echo " • Add exploration_demo.gif for exploration mode documentation"
|
||||
echo " • Consider side-by-side comparison showing both modes"
|
||||
echo
|
||||
|
||||
# Optional: create side-by-side comparison
|
||||
read -p "Create side-by-side comparison image? (y/N): " -n 1 -r
|
||||
echo
|
||||
if [[ $REPLY =~ ^[Yy]$ ]]; then
|
||||
if command -v ffmpeg &> /dev/null; then
|
||||
echo "🎬 Creating side-by-side comparison..."
|
||||
|
||||
# Convert GIFs to MP4 first (better quality)
|
||||
ffmpeg -i recordings/synthesis_demo.gif -c:v libx264 -pix_fmt yuv420p recordings/synthesis_demo.mp4 -y
|
||||
ffmpeg -i recordings/exploration_demo.gif -c:v libx264 -pix_fmt yuv420p recordings/exploration_demo.mp4 -y
|
||||
|
||||
# Create side-by-side
|
||||
ffmpeg -i recordings/synthesis_demo.mp4 -i recordings/exploration_demo.mp4 -filter_complex \
|
||||
"[0:v][1:v]hstack=inputs=2[v]" -map "[v]" recordings/side_by_side_demo.mp4 -y
|
||||
|
||||
# Convert back to GIF
|
||||
ffmpeg -i recordings/side_by_side_demo.mp4 recordings/side_by_side_demo.gif -y
|
||||
|
||||
echo "✅ Side-by-side demo created: recordings/side_by_side_demo.gif"
|
||||
else
|
||||
echo "⚠️ ffmpeg not found - skipping side-by-side creation"
|
||||
fi
|
||||
fi
|
||||
|
||||
echo
|
||||
echo "🎯 Next Steps:"
|
||||
echo " 1. Review the generated GIFs"
|
||||
echo " 2. Update README.md with new demos"
|
||||
echo " 3. Upload to GitHub for better project presentation"
|
||||
echo " 4. Consider adding both GIFs to documentation"
|
||||
@ -1,252 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Create an animated demo script that simulates the FSS-Mini-RAG TUI experience.
|
||||
This script generates a realistic but controlled demonstration for GIF recording.
|
||||
"""
|
||||
|
||||
import time
|
||||
import sys
|
||||
import os
|
||||
from typing import List
|
||||
|
||||
class DemoSimulator:
|
||||
def __init__(self):
|
||||
self.width = 80
|
||||
self.height = 24
|
||||
|
||||
def clear_screen(self):
|
||||
"""Clear the terminal screen."""
|
||||
print("\033[H\033[2J", end="")
|
||||
|
||||
def type_text(self, text: str, delay: float = 0.03):
|
||||
"""Simulate typing text character by character."""
|
||||
for char in text:
|
||||
print(char, end="", flush=True)
|
||||
time.sleep(delay)
|
||||
print()
|
||||
|
||||
def pause(self, duration: float):
|
||||
"""Pause for the specified duration."""
|
||||
time.sleep(duration)
|
||||
|
||||
def show_header(self):
|
||||
"""Display the TUI header."""
|
||||
print("╔════════════════════════════════════════════════════╗")
|
||||
print("║ FSS-Mini-RAG TUI ║")
|
||||
print("║ Semantic Code Search Interface ║")
|
||||
print("╚════════════════════════════════════════════════════╝")
|
||||
print()
|
||||
|
||||
def show_menu(self):
|
||||
"""Display the main menu."""
|
||||
print("🎯 Main Menu")
|
||||
print("============")
|
||||
print()
|
||||
print("1. Select project directory")
|
||||
print("2. Index project for search")
|
||||
print("3. Search project")
|
||||
print("4. View status")
|
||||
print("5. Configuration")
|
||||
print("6. CLI command reference")
|
||||
print("7. Exit")
|
||||
print()
|
||||
print("💡 All these actions can be done via CLI commands")
|
||||
print(" You'll see the commands as you use this interface!")
|
||||
print()
|
||||
|
||||
def simulate_project_selection(self):
|
||||
"""Simulate selecting a project directory."""
|
||||
print("Select option (number): ", end="", flush=True)
|
||||
self.type_text("1", delay=0.15)
|
||||
self.pause(0.5)
|
||||
print()
|
||||
print("📁 Select Project Directory")
|
||||
print("===========================")
|
||||
print()
|
||||
print("Project path: ", end="", flush=True)
|
||||
self.type_text("./demo-project", delay=0.08)
|
||||
self.pause(0.8)
|
||||
print()
|
||||
print("✅ Selected: ./demo-project")
|
||||
print()
|
||||
print("💡 CLI equivalent: rag-mini index ./demo-project")
|
||||
self.pause(1.5)
|
||||
|
||||
def simulate_indexing(self):
|
||||
"""Simulate the indexing process."""
|
||||
self.clear_screen()
|
||||
self.show_header()
|
||||
print("🚀 Indexing demo-project")
|
||||
print("========================")
|
||||
print()
|
||||
print("Found 12 files to index")
|
||||
print()
|
||||
|
||||
# Simulate progress bar
|
||||
print(" Indexing files... ", end="")
|
||||
progress_chars = "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
|
||||
for i, char in enumerate(progress_chars):
|
||||
print(char, end="", flush=True)
|
||||
time.sleep(0.03) # Slightly faster
|
||||
if i % 8 == 0:
|
||||
percentage = int((i / len(progress_chars)) * 100)
|
||||
print(f" {percentage}%", end="\r")
|
||||
print(" Indexing files... " + progress_chars[:i+1], end="")
|
||||
|
||||
print(" 100%")
|
||||
print()
|
||||
print(" Added 58 chunks to database")
|
||||
print()
|
||||
print("Indexing Complete!")
|
||||
print("Files indexed: 12")
|
||||
print("Chunks created: 58")
|
||||
print("Time taken: 2.8 seconds")
|
||||
print("Speed: 4.3 files/second")
|
||||
print("✅ Indexed 12 files in 2.8s")
|
||||
print(" Created 58 chunks")
|
||||
print(" Speed: 4.3 files/sec")
|
||||
print()
|
||||
print("💡 CLI equivalent: rag-mini index ./demo-project")
|
||||
self.pause(2.0)
|
||||
|
||||
def simulate_search(self):
|
||||
"""Simulate searching the indexed project."""
|
||||
self.clear_screen()
|
||||
self.show_header()
|
||||
print("🔍 Search Project")
|
||||
print("=================")
|
||||
print()
|
||||
print("Search query: ", end="", flush=True)
|
||||
self.type_text('"user authentication"', delay=0.08)
|
||||
self.pause(0.8)
|
||||
print()
|
||||
print("🔍 Searching \"user authentication\" in demo-project")
|
||||
self.pause(0.5)
|
||||
print("✅ Found 8 results:")
|
||||
print()
|
||||
|
||||
# Show search results with multi-line previews
|
||||
results = [
|
||||
{
|
||||
"file": "auth/manager.py",
|
||||
"function": "AuthManager.login()",
|
||||
"preview": "Authenticate user and create session.\nValidates credentials against database and\nreturns session token on success.",
|
||||
"score": "0.94"
|
||||
},
|
||||
{
|
||||
"file": "auth/validators.py",
|
||||
"function": "validate_password()",
|
||||
"preview": "Validate user password against stored hash.\nSupports bcrypt, scrypt, and argon2 hashing.\nIncludes timing attack protection.",
|
||||
"score": "0.91"
|
||||
},
|
||||
{
|
||||
"file": "middleware/auth.py",
|
||||
"function": "require_authentication()",
|
||||
"preview": "Authentication middleware decorator.\nChecks session tokens and JWT validity.\nRedirects to login on authentication failure.",
|
||||
"score": "0.88"
|
||||
},
|
||||
{
|
||||
"file": "api/endpoints.py",
|
||||
"function": "login_endpoint()",
|
||||
"preview": "Handle user login API requests.\nAccepts JSON credentials, validates input,\nand returns authentication tokens.",
|
||||
"score": "0.85"
|
||||
},
|
||||
{
|
||||
"file": "models/user.py",
|
||||
"function": "User.authenticate()",
|
||||
"preview": "User model authentication method.\nQueries database for user credentials\nand handles account status checks.",
|
||||
"score": "0.82"
|
||||
},
|
||||
{
|
||||
"file": "auth/tokens.py",
|
||||
"function": "generate_jwt_token()",
|
||||
"preview": "Generate JWT authentication tokens.\nIncludes expiration, claims, and signature.\nSupports refresh and access token types.",
|
||||
"score": "0.79"
|
||||
},
|
||||
{
|
||||
"file": "utils/security.py",
|
||||
"function": "hash_password()",
|
||||
"preview": "Secure password hashing utility.\nUses bcrypt with configurable rounds.\nProvides salt generation and validation.",
|
||||
"score": "0.76"
|
||||
},
|
||||
{
|
||||
"file": "config/auth_settings.py",
|
||||
"function": "load_auth_config()",
|
||||
"preview": "Load authentication configuration.\nHandles JWT secrets, token expiration,\nand authentication provider settings.",
|
||||
"score": "0.73"
|
||||
}
|
||||
]
|
||||
|
||||
for i, result in enumerate(results, 1):
|
||||
print(f"📄 Result {i} (Score: {result['score']})")
|
||||
print(f" File: {result['file']}")
|
||||
print(f" Function: {result['function']}")
|
||||
preview_lines = result['preview'].split('\n')
|
||||
for j, line in enumerate(preview_lines):
|
||||
if j == 0:
|
||||
print(f" Preview: {line}")
|
||||
else:
|
||||
print(f" {line}")
|
||||
print()
|
||||
self.pause(0.6)
|
||||
|
||||
print("💡 CLI equivalent: rag-mini search ./demo-project \"user authentication\"")
|
||||
self.pause(2.5)
|
||||
|
||||
def simulate_cli_reference(self):
|
||||
"""Show CLI command reference."""
|
||||
self.clear_screen()
|
||||
self.show_header()
|
||||
print("🖥️ CLI Command Reference")
|
||||
print("=========================")
|
||||
print()
|
||||
print("What you just did in the TUI:")
|
||||
print()
|
||||
print("1️⃣ Select & Index Project:")
|
||||
print(" rag-mini index ./demo-project")
|
||||
print(" # Indexed 12 files → 58 semantic chunks")
|
||||
print()
|
||||
print("2️⃣ Search Project:")
|
||||
print(' rag-mini search ./demo-project "user authentication"')
|
||||
print(" # Found 8 relevant matches with context")
|
||||
print()
|
||||
print("3️⃣ Check Status:")
|
||||
print(" rag-mini status ./demo-project")
|
||||
print()
|
||||
print("🚀 You can now use these commands directly!")
|
||||
print(" No TUI required for power users.")
|
||||
print()
|
||||
print("💡 Try semantic queries like:")
|
||||
print(' • "error handling" • "database queries"')
|
||||
print(' • "API validation" • "configuration management"')
|
||||
self.pause(3.0)
|
||||
|
||||
def run_demo(self):
|
||||
"""Run the complete demo simulation."""
|
||||
print("🎬 Starting FSS-Mini-RAG Demo...")
|
||||
self.pause(1.0)
|
||||
|
||||
# Clear and show TUI startup
|
||||
self.clear_screen()
|
||||
self.show_header()
|
||||
self.show_menu()
|
||||
self.pause(1.5)
|
||||
|
||||
# Simulate workflow
|
||||
self.simulate_project_selection()
|
||||
self.simulate_indexing()
|
||||
self.simulate_search()
|
||||
self.simulate_cli_reference()
|
||||
|
||||
# Final message
|
||||
self.clear_screen()
|
||||
print("🎉 Demo Complete!")
|
||||
print()
|
||||
print("FSS-Mini-RAG: Semantic code search that actually works")
|
||||
print("Copy the folder, run ./rag-mini, and start searching!")
|
||||
print()
|
||||
print("Ready to try it yourself? 🚀")
|
||||
|
||||
if __name__ == "__main__":
|
||||
demo = DemoSimulator()
|
||||
demo.run_demo()
|
||||
@ -1,270 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Create demo GIF for Exploration Mode - Deep Thinking & Interactive Learning
|
||||
Shows the conversational workflow for understanding and debugging codebases.
|
||||
"""
|
||||
|
||||
import time
|
||||
import sys
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
class ExplorationDemoSimulator:
|
||||
def __init__(self):
|
||||
self.width = 100
|
||||
self.height = 35
|
||||
|
||||
def clear_screen(self):
|
||||
print("\033[H\033[2J", end="")
|
||||
|
||||
def type_command(self, command: str, delay: float = 0.05):
|
||||
"""Simulate typing a command."""
|
||||
print("$ ", end="", flush=True)
|
||||
for char in command:
|
||||
print(char, end="", flush=True)
|
||||
time.sleep(delay)
|
||||
print()
|
||||
time.sleep(0.5)
|
||||
|
||||
def type_question(self, question: str, delay: float = 0.04):
|
||||
"""Simulate typing a question in exploration mode."""
|
||||
print("> ", end="", flush=True)
|
||||
for char in question:
|
||||
print(char, end="", flush=True)
|
||||
time.sleep(delay)
|
||||
print()
|
||||
time.sleep(0.5)
|
||||
|
||||
def show_thinking(self, duration: float = 2.0):
|
||||
"""Show thinking animation."""
|
||||
print("🔍 Analyzing...", end="", flush=True)
|
||||
for _ in range(3):
|
||||
time.sleep(duration / 3)
|
||||
print(".", end="", flush=True)
|
||||
print()
|
||||
time.sleep(0.5)
|
||||
|
||||
def show_response(self, lines: list, delay: float = 0.4):
|
||||
"""Show AI response with realistic timing."""
|
||||
for line in lines:
|
||||
print(line)
|
||||
time.sleep(delay)
|
||||
time.sleep(1.5)
|
||||
|
||||
def run_exploration_demo(self):
|
||||
"""Run the exploration mode demonstration."""
|
||||
self.clear_screen()
|
||||
|
||||
# Title
|
||||
print("🧠 FSS-Mini-RAG: Exploration Mode Demo")
|
||||
print("=" * 55)
|
||||
print("Deep thinking & interactive learning for complex codebases")
|
||||
print()
|
||||
time.sleep(2)
|
||||
|
||||
# Step 1: Start exploration
|
||||
print("Step 1: Start interactive exploration session")
|
||||
print("-" * 45)
|
||||
self.type_command("rag-mini explore ./webapp-project")
|
||||
|
||||
self.show_response([
|
||||
"🧠 EXPLORATION MODE STARTED",
|
||||
"=" * 50,
|
||||
"Project: webapp-project",
|
||||
"Session: explore_1702845632",
|
||||
"",
|
||||
"🎯 This mode uses thinking and remembers context.",
|
||||
" Perfect for debugging, learning, and deep exploration.",
|
||||
"",
|
||||
"💡 Tips:",
|
||||
" • Ask follow-up questions - I'll remember our conversation",
|
||||
" • Use 'why', 'how', 'explain' for detailed reasoning",
|
||||
" • Type 'quit' or 'exit' to end session",
|
||||
"",
|
||||
"=" * 50,
|
||||
"",
|
||||
"🤔 Ask your first question about the codebase:"
|
||||
])
|
||||
|
||||
# Step 2: First question - How authentication works
|
||||
print("Step 2: Understanding authentication system")
|
||||
print("-" * 42)
|
||||
self.type_question("How does authentication work in this codebase?")
|
||||
|
||||
self.show_thinking()
|
||||
|
||||
self.show_response([
|
||||
"",
|
||||
"🧠 EXPLORATION ANALYSIS (Question #1)",
|
||||
"Session: 0.2m | Results: 8 | Time: 3.4s",
|
||||
"=" * 60,
|
||||
"",
|
||||
"📝 Analysis:",
|
||||
" This webapp implements a JWT-based authentication system with",
|
||||
" session management, role-based permissions, and OAuth2 integration.",
|
||||
"",
|
||||
"🔍 Key Insights:",
|
||||
" • JWT tokens generated in auth/jwt_handler.py with 24h expiry",
|
||||
" • User sessions stored in Redis for fast lookups and invalidation",
|
||||
" • Role-based access control through middleware/permissions.py",
|
||||
" • OAuth2 integration supports Google, GitHub, and Microsoft providers",
|
||||
" • Password hashing uses bcrypt with 12 rounds for security",
|
||||
"",
|
||||
"💡 Code Examples:",
|
||||
" JWT generation: jwt.encode(payload, SECRET_KEY, algorithm='HS256')",
|
||||
" Session storage: redis.setex(f'session:{user_id}', 86400, token)",
|
||||
" Permission check: @require_permission('admin') decorator",
|
||||
"",
|
||||
"🎯 Next Steps:",
|
||||
" • Review JWT secret rotation policy",
|
||||
" • Consider implementing refresh tokens",
|
||||
" • Audit OAuth2 scope permissions",
|
||||
"",
|
||||
"🟢 Confidence: 91%"
|
||||
])
|
||||
|
||||
# Step 3: Follow-up question - Security concerns
|
||||
print("Step 3: Follow-up question (context remembered)")
|
||||
print("-" * 48)
|
||||
self.type_question("What security concerns should I be aware of?")
|
||||
|
||||
self.show_thinking(1.8)
|
||||
|
||||
self.show_response([
|
||||
"",
|
||||
"🧠 EXPLORATION ANALYSIS (Question #2)",
|
||||
"Session: 1.4m | Results: 6 | Time: 2.1s",
|
||||
"=" * 60,
|
||||
"",
|
||||
"📝 Analysis:",
|
||||
" Building on the JWT authentication system we just discussed,",
|
||||
" I've identified several security considerations that need attention.",
|
||||
"",
|
||||
"🔍 Key Insights:",
|
||||
" • JWT secret is hardcoded in config.py (CRITICAL RISK)",
|
||||
" • No rate limiting on login endpoints (brute force vulnerable)",
|
||||
" • Session invalidation only happens on logout, not on password change",
|
||||
" • OAuth2 state parameter validation could be strengthened",
|
||||
" • CORS settings allow all origins in development config",
|
||||
"",
|
||||
"💡 Code Examples:",
|
||||
" Vulnerable: SECRET_KEY = 'hardcoded-secret-123'",
|
||||
" Missing: @rate_limit('5 per minute') on /api/login",
|
||||
" Risk: CORS_ALLOW_ALL_ORIGINS = True",
|
||||
"",
|
||||
"🎯 Next Steps:",
|
||||
" • Move JWT secret to environment variables immediately",
|
||||
" • Implement rate limiting with Redis or in-memory store",
|
||||
" • Add session invalidation on password/email changes",
|
||||
" • Review and restrict CORS origins for production",
|
||||
" • Add security headers middleware",
|
||||
"",
|
||||
"🔴 Confidence: 94% | Context: 1 previous questions"
|
||||
])
|
||||
|
||||
# Step 4: Third question - Performance issues
|
||||
print("Step 4: Exploring performance (conversation continues)")
|
||||
print("-" * 54)
|
||||
self.type_question("Why might the login process be slow?")
|
||||
|
||||
self.show_thinking(2.2)
|
||||
|
||||
self.show_response([
|
||||
"",
|
||||
"🧠 EXPLORATION ANALYSIS (Question #3)",
|
||||
"Session: 2.1m | Results: 7 | Time: 2.8s",
|
||||
"=" * 60,
|
||||
"",
|
||||
"📝 Analysis:",
|
||||
" Considering our previous discussion about JWT auth and security,",
|
||||
" the login slowness likely stems from several performance bottlenecks.",
|
||||
"",
|
||||
"🔍 Key Insights:",
|
||||
" • bcrypt rounds set to 15 (recommended: 10-12) causing CPU overhead",
|
||||
" • Database queries for user lookup aren't indexed on email field",
|
||||
" • Redis connection pool not configured, creating new connections",
|
||||
" • OAuth2 provider calls lack timeout settings (can hang)",
|
||||
" • Session cleanup runs synchronously during login flow",
|
||||
"",
|
||||
"💡 Code Examples:",
|
||||
" Slow: bcrypt.hashpw(password, bcrypt.gensalt(rounds=15))",
|
||||
" Missing: CREATE INDEX idx_users_email ON users(email)",
|
||||
" Blocking: cleanup_expired_sessions() # Should be async",
|
||||
"",
|
||||
"🎯 Next Steps:",
|
||||
" • Reduce bcrypt rounds to 12 for better balance",
|
||||
" • Add database index on users.email column",
|
||||
" • Configure Redis connection pooling",
|
||||
" • Move session cleanup to background task",
|
||||
" • Add OAuth2 request timeouts (5-10 seconds)",
|
||||
"",
|
||||
"🟡 Confidence: 89% | Context: 2 previous questions"
|
||||
])
|
||||
|
||||
# Step 5: Session summary
|
||||
print("Step 5: Session summary and conclusion")
|
||||
print("-" * 38)
|
||||
self.type_question("summary")
|
||||
|
||||
self.show_response([
|
||||
"",
|
||||
"🧠 EXPLORATION SESSION SUMMARY",
|
||||
"=" * 40,
|
||||
"Project: webapp-project",
|
||||
"Session ID: explore_1702845632",
|
||||
"Duration: 2.5 minutes",
|
||||
"Questions explored: 3",
|
||||
"",
|
||||
"📋 Topics explored:",
|
||||
" 1. How does authentication work in this codebase? (confidence: 91%)",
|
||||
" 2. What security concerns should I be aware of? (confidence: 94%)",
|
||||
" 3. Why might the login process be slow? (confidence: 89%)",
|
||||
"",
|
||||
"🎯 Key Discoveries:",
|
||||
" • JWT-based auth with session management",
|
||||
" • Critical security issues (hardcoded secrets, no rate limiting)",
|
||||
" • Performance bottlenecks (bcrypt settings, missing indexes)",
|
||||
"",
|
||||
"💡 Action Items Generated:",
|
||||
" • Immediate: Fix hardcoded JWT secret",
|
||||
" • High Priority: Add rate limiting and database indexes",
|
||||
" • Monitor: Review OAuth2 configurations"
|
||||
])
|
||||
|
||||
# Step 6: Exit
|
||||
self.type_question("quit")
|
||||
|
||||
self.show_response([
|
||||
"",
|
||||
"✅ Exploration session ended.",
|
||||
"",
|
||||
"🎬 This was Exploration Mode - perfect for learning and debugging!"
|
||||
])
|
||||
|
||||
# Final summary
|
||||
print()
|
||||
print("💡 Exploration Mode Benefits:")
|
||||
print(" 🧠 Thinking-enabled AI for detailed reasoning")
|
||||
print(" 💭 Conversation memory across questions")
|
||||
print(" 🔍 Perfect for debugging and understanding")
|
||||
print(" 📚 Educational - learn how code really works")
|
||||
print(" 🎯 Context-aware follow-up responses")
|
||||
print()
|
||||
time.sleep(3)
|
||||
|
||||
def main():
|
||||
"""Run the exploration mode demo."""
|
||||
demo = ExplorationDemoSimulator()
|
||||
|
||||
print("Starting FSS-Mini-RAG Exploration Mode Demo...")
|
||||
print("Record with: asciinema rec exploration_demo.cast")
|
||||
print("Press Enter to start...")
|
||||
input()
|
||||
|
||||
demo.run_exploration_demo()
|
||||
|
||||
print("\n🎯 To create GIF:")
|
||||
print("agg exploration_demo.cast exploration_demo.gif")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,178 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Create demo GIF for Synthesis Mode - Fast & Consistent RAG Search
|
||||
Shows the streamlined workflow for quick answers and code discovery.
|
||||
"""
|
||||
|
||||
import time
|
||||
import sys
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
class SynthesisDemoSimulator:
|
||||
def __init__(self):
|
||||
self.width = 100
|
||||
self.height = 30
|
||||
|
||||
def clear_screen(self):
|
||||
print("\033[H\033[2J", end="")
|
||||
|
||||
def type_command(self, command: str, delay: float = 0.05):
|
||||
"""Simulate typing a command."""
|
||||
print("$ ", end="", flush=True)
|
||||
for char in command:
|
||||
print(char, end="", flush=True)
|
||||
time.sleep(delay)
|
||||
print()
|
||||
time.sleep(0.5)
|
||||
|
||||
def show_output(self, lines: list, delay: float = 0.3):
|
||||
"""Show command output with realistic timing."""
|
||||
for line in lines:
|
||||
print(line)
|
||||
time.sleep(delay)
|
||||
time.sleep(1.0)
|
||||
|
||||
def run_synthesis_demo(self):
|
||||
"""Run the synthesis mode demonstration."""
|
||||
self.clear_screen()
|
||||
|
||||
# Title
|
||||
print("🚀 FSS-Mini-RAG: Synthesis Mode Demo")
|
||||
print("=" * 50)
|
||||
print("Fast & consistent RAG search for quick answers")
|
||||
print()
|
||||
time.sleep(2)
|
||||
|
||||
# Step 1: Index a project
|
||||
print("Step 1: Index a sample project")
|
||||
print("-" * 30)
|
||||
self.type_command("rag-mini index ./sample-project")
|
||||
|
||||
self.show_output([
|
||||
"📁 Indexing project: sample-project",
|
||||
"🔍 Found 12 files to process",
|
||||
"✂️ Creating semantic chunks...",
|
||||
"🧠 Generating embeddings...",
|
||||
"💾 Building vector index...",
|
||||
"✅ Indexed 89 chunks from 12 files in 3.2s",
|
||||
"",
|
||||
"💡 Try: rag-mini search ./sample-project \"your search here\""
|
||||
])
|
||||
|
||||
# Step 2: Quick search
|
||||
print("Step 2: Quick semantic search")
|
||||
print("-" * 30)
|
||||
self.type_command("rag-mini search ./sample-project \"user authentication\"")
|
||||
|
||||
self.show_output([
|
||||
"🔍 Searching \"user authentication\" in sample-project",
|
||||
"✅ Found 5 results:",
|
||||
"",
|
||||
"1. auth/models.py",
|
||||
" Score: 0.923",
|
||||
" Lines: 45-62",
|
||||
" Context: User class",
|
||||
" Content:",
|
||||
" class User:",
|
||||
" def authenticate(self, password):",
|
||||
" return bcrypt.checkpw(password, self.password_hash)",
|
||||
"",
|
||||
"2. auth/views.py",
|
||||
" Score: 0.887",
|
||||
" Lines: 23-41",
|
||||
" Context: login_view function",
|
||||
" Content:",
|
||||
" def login_view(request):",
|
||||
" user = authenticate(username, password)",
|
||||
" if user:",
|
||||
" login(request, user)",
|
||||
"",
|
||||
"3. middleware/auth.py",
|
||||
" Score: 0.845",
|
||||
" Content: Authentication middleware checking..."
|
||||
])
|
||||
|
||||
# Step 3: Search with AI synthesis
|
||||
print("Step 3: Add AI synthesis for deeper understanding")
|
||||
print("-" * 50)
|
||||
self.type_command("rag-mini search ./sample-project \"error handling\" --synthesize")
|
||||
|
||||
self.show_output([
|
||||
"🔍 Searching \"error handling\" in sample-project",
|
||||
"🧠 Generating LLM synthesis...",
|
||||
"✅ Found 4 results:",
|
||||
"",
|
||||
"1. utils/exceptions.py",
|
||||
" Score: 0.934",
|
||||
" Content: Custom exception classes for API errors...",
|
||||
"",
|
||||
"2. api/handlers.py",
|
||||
" Score: 0.889",
|
||||
" Content: Global exception handler with logging...",
|
||||
"",
|
||||
"🧠 LLM SYNTHESIS",
|
||||
"=" * 50,
|
||||
"",
|
||||
"📝 Summary:",
|
||||
" This codebase implements a robust error handling system with",
|
||||
" custom exceptions, global handlers, and structured logging.",
|
||||
"",
|
||||
"🔍 Key Findings:",
|
||||
" • Custom exception hierarchy in utils/exceptions.py",
|
||||
" • Global error handler catches all API exceptions",
|
||||
" • Logging integrated with error tracking service",
|
||||
"",
|
||||
"💡 Code Patterns:",
|
||||
" try/except blocks with specific exception types",
|
||||
" Centralized error response formatting",
|
||||
"",
|
||||
"🎯 Suggested Actions:",
|
||||
" • Review exception hierarchy for completeness",
|
||||
" • Consider adding error recovery mechanisms",
|
||||
"",
|
||||
"🟢 Confidence: 87%"
|
||||
])
|
||||
|
||||
# Step 4: Show performance
|
||||
print("Step 4: Performance characteristics")
|
||||
print("-" * 35)
|
||||
print("⚡ Synthesis Mode Benefits:")
|
||||
print(" • Lightning fast responses (no thinking overhead)")
|
||||
print(" • Consistent, reliable results")
|
||||
print(" • Perfect for code discovery and quick answers")
|
||||
print(" • Works great with ultra-efficient models (qwen3:0.6b)")
|
||||
print()
|
||||
time.sleep(3)
|
||||
|
||||
# Step 5: When to use
|
||||
print("💡 When to use Synthesis Mode:")
|
||||
print(" ✅ Quick code lookups")
|
||||
print(" ✅ Finding specific functions or classes")
|
||||
print(" ✅ Understanding code structure")
|
||||
print(" ✅ Fast documentation searches")
|
||||
print(" ✅ Batch processing multiple queries")
|
||||
print()
|
||||
|
||||
print("🧠 For deeper analysis, try: rag-mini explore ./project")
|
||||
print()
|
||||
time.sleep(3)
|
||||
|
||||
print("🎬 Demo complete! This was Synthesis Mode - optimized for speed.")
|
||||
|
||||
def main():
|
||||
"""Run the synthesis mode demo."""
|
||||
demo = SynthesisDemoSimulator()
|
||||
|
||||
print("Starting FSS-Mini-RAG Synthesis Mode Demo...")
|
||||
print("Record with: asciinema rec synthesis_demo.cast")
|
||||
print("Press Enter to start...")
|
||||
input()
|
||||
|
||||
demo.run_synthesis_demo()
|
||||
|
||||
print("\n🎯 To create GIF:")
|
||||
print("agg synthesis_demo.cast synthesis_demo.gif")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
109
record_demo.sh
109
record_demo.sh
@ -1,109 +0,0 @@
|
||||
#!/bin/bash
|
||||
# Script to record the FSS-Mini-RAG demo as an animated GIF
|
||||
|
||||
set -e
|
||||
|
||||
echo "🎬 FSS-Mini-RAG Demo Recording Script"
|
||||
echo "====================================="
|
||||
echo
|
||||
|
||||
# Check if required tools are available
|
||||
check_tool() {
|
||||
if ! command -v "$1" &> /dev/null; then
|
||||
echo "❌ $1 is required but not installed."
|
||||
echo " Install with: $2"
|
||||
exit 1
|
||||
fi
|
||||
}
|
||||
|
||||
echo "🔧 Checking required tools..."
|
||||
check_tool "asciinema" "pip install asciinema"
|
||||
echo "✅ asciinema found"
|
||||
|
||||
# Optional: Check for gif conversion tools
|
||||
if command -v "agg" &> /dev/null; then
|
||||
echo "✅ agg found (for gif conversion)"
|
||||
CONVERTER="agg"
|
||||
elif command -v "svg-term" &> /dev/null; then
|
||||
echo "✅ svg-term found (for gif conversion)"
|
||||
CONVERTER="svg-term"
|
||||
else
|
||||
echo "⚠️ No gif converter found. You can:"
|
||||
echo " - Install agg: cargo install --git https://github.com/asciinema/agg"
|
||||
echo " - Or use online converter at: https://dstein64.github.io/gifcast/"
|
||||
CONVERTER="none"
|
||||
fi
|
||||
|
||||
echo
|
||||
|
||||
# Set up recording environment
|
||||
export TERM=xterm-256color
|
||||
export COLUMNS=80
|
||||
export LINES=24
|
||||
|
||||
# Create recording directory
|
||||
mkdir -p recordings
|
||||
TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
|
||||
RECORDING_FILE="recordings/fss-mini-rag-demo-${TIMESTAMP}.cast"
|
||||
GIF_FILE="recordings/fss-mini-rag-demo-${TIMESTAMP}.gif"
|
||||
|
||||
echo "🎥 Starting recording..."
|
||||
echo " Output: $RECORDING_FILE"
|
||||
echo
|
||||
|
||||
# Record the demo
|
||||
asciinema rec "$RECORDING_FILE" \
|
||||
--title "FSS-Mini-RAG Demo" \
|
||||
--command "python3 create_demo_script.py" \
|
||||
--cols 80 \
|
||||
--rows 24
|
||||
|
||||
echo
|
||||
echo "✅ Recording complete: $RECORDING_FILE"
|
||||
|
||||
# Convert to GIF if converter is available
|
||||
if [ "$CONVERTER" = "agg" ]; then
|
||||
echo "🎨 Converting to GIF with agg..."
|
||||
agg "$RECORDING_FILE" "$GIF_FILE" \
|
||||
--font-size 14 \
|
||||
--line-height 1.2 \
|
||||
--cols 80 \
|
||||
--rows 24 \
|
||||
--theme monokai
|
||||
|
||||
echo "✅ GIF created: $GIF_FILE"
|
||||
|
||||
# Optimize GIF size
|
||||
if command -v "gifsicle" &> /dev/null; then
|
||||
echo "🗜️ Optimizing GIF size..."
|
||||
gifsicle -O3 --lossy=80 -o "${GIF_FILE}.optimized" "$GIF_FILE"
|
||||
mv "${GIF_FILE}.optimized" "$GIF_FILE"
|
||||
echo "✅ GIF optimized"
|
||||
fi
|
||||
|
||||
elif [ "$CONVERTER" = "svg-term" ]; then
|
||||
echo "🎨 Converting to SVG with svg-term..."
|
||||
svg-term --cast "$RECORDING_FILE" --out "${RECORDING_FILE%.cast}.svg" \
|
||||
--window --width 80 --height 24
|
||||
echo "✅ SVG created: ${RECORDING_FILE%.cast}.svg"
|
||||
echo "💡 Convert SVG to GIF online at: https://cloudconvert.com/svg-to-gif"
|
||||
fi
|
||||
|
||||
echo
|
||||
echo "🎉 Demo recording complete!"
|
||||
echo
|
||||
echo "📁 Files created:"
|
||||
echo " 📼 Recording: $RECORDING_FILE"
|
||||
if [ "$CONVERTER" != "none" ] && [ -f "$GIF_FILE" ]; then
|
||||
echo " 🎞️ GIF: $GIF_FILE"
|
||||
fi
|
||||
echo
|
||||
echo "📋 Next steps:"
|
||||
echo " 1. Review the recording: asciinema play $RECORDING_FILE"
|
||||
if [ "$CONVERTER" = "none" ]; then
|
||||
echo " 2. Convert to GIF online: https://dstein64.github.io/gifcast/"
|
||||
fi
|
||||
echo " 3. Add to README.md after the mermaid diagram"
|
||||
echo " 4. Optimize for web (target: <2MB for fast loading)"
|
||||
echo
|
||||
echo "🚀 Perfect demo for showcasing FSS-Mini-RAG!"
|
||||
@ -1,158 +0,0 @@
|
||||
{"version": 2, "width": 80, "height": 24, "timestamp": 1754977674, "env": {"SHELL": "/bin/bash", "TERM": "xterm-256color"}, "title": "FSS-Mini-RAG Demo"}
|
||||
[0.014891, "o", "🎬 Starting FSS-Mini-RAG Demo...\r\n"]
|
||||
[1.014958, "o", "\u001b[H\u001b[2J╔════════════════════════════════════════════════════╗\r\n║ FSS-Mini-RAG TUI ║\r\n║ Semantic Code Search Interface ║\r\n╚════════════════════════════════════════════════════╝\r\n\r\n🎯 Main Menu\r\n============\r\n\r\n1. Select project directory\r\n2. Index project for search\r\n3. Search project\r\n4. View status\r\n5. Configuration\r\n6. CLI command reference\r\n7. Exit\r\n\r\n💡 All these actions can be done via CLI commands\r\n You'll see the commands as you use this interface!\r\n\r\n"]
|
||||
[2.515054, "o", "S"]
|
||||
[2.615209, "o", "e"]
|
||||
[2.715242, "o", "l"]
|
||||
[2.815395, "o", "e"]
|
||||
[2.915451, "o", "c"]
|
||||
[3.015487, "o", "t"]
|
||||
[3.115572, "o", " "]
|
||||
[3.215656, "o", "o"]
|
||||
[3.315727, "o", "p"]
|
||||
[3.416006, "o", "t"]
|
||||
[3.516068, "o", "i"]
|
||||
[3.616208, "o", "o"]
|
||||
[3.716279, "o", "n"]
|
||||
[3.816447, "o", " "]
|
||||
[3.916533, "o", "("]
|
||||
[4.016581, "o", "n"]
|
||||
[4.116668, "o", "u"]
|
||||
[4.216761, "o", "m"]
|
||||
[4.316822, "o", "b"]
|
||||
[4.417016, "o", "e"]
|
||||
[4.517543, "o", "r"]
|
||||
[4.617592, "o", ")"]
|
||||
[4.718071, "o", ":"]
|
||||
[4.818318, "o", " "]
|
||||
[4.918361, "o", "1"]
|
||||
[5.018414, "o", "\r\n"]
|
||||
[5.518525, "o", "\r\n📁 Select Project Directory\r\n===========================\r\n\r\nE"]
|
||||
[5.598584, "o", "n"]
|
||||
[5.678726, "o", "t"]
|
||||
[5.758779, "o", "e"]
|
||||
[5.838886, "o", "r"]
|
||||
[5.919012, "o", " "]
|
||||
[5.999089, "o", "p"]
|
||||
[6.079147, "o", "r"]
|
||||
[6.159235, "o", "o"]
|
||||
[6.239302, "o", "j"]
|
||||
[6.319408, "o", "e"]
|
||||
[6.399486, "o", "c"]
|
||||
[6.479652, "o", "t"]
|
||||
[6.559809, "o", " "]
|
||||
[6.639896, "o", "p"]
|
||||
[6.720059, "o", "a"]
|
||||
[6.800089, "o", "t"]
|
||||
[6.880181, "o", "h"]
|
||||
[6.960251, "o", ":"]
|
||||
[7.040319, "o", " "]
|
||||
[7.120431, "o", "."]
|
||||
[7.200494, "o", "/"]
|
||||
[7.280648, "o", "d"]
|
||||
[7.360669, "o", "e"]
|
||||
[7.440783, "o", "m"]
|
||||
[7.520913, "o", "o"]
|
||||
[7.600973, "o", "-"]
|
||||
[7.681166, "o", "p"]
|
||||
[7.761303, "o", "r"]
|
||||
[7.84134, "o", "o"]
|
||||
[7.92185, "o", "j"]
|
||||
[8.001944, "o", "e"]
|
||||
[8.082028, "o", "c"]
|
||||
[8.162096, "o", "t"]
|
||||
[8.242167, "o", "\r\n"]
|
||||
[9.042306, "o", "\r\n✅ Selected: ./demo-project\r\n\r\n💡 CLI equivalent: rag-mini index ./demo-project\r\n"]
|
||||
[10.542386, "o", "\u001b[H\u001b[2J╔════════════════════════════════════════════════════╗\r\n║ FSS-Mini-RAG TUI ║\r\n║ Semantic Code Search Interface ║\r\n╚════════════════════════════════════════════════════╝\r\n\r\n🚀 Indexing demo-project\r\n========================\r\n\r\nFound 3 files to index\r\n\r\n"]
|
||||
[10.542493, "o", " Indexing files... ━"]
|
||||
[10.582592, "o", " 0%\r Indexing files... ━━"]
|
||||
[10.622796, "o", "━"]
|
||||
[10.66291, "o", "━"]
|
||||
[10.702993, "o", "━"]
|
||||
[10.743511, "o", "━"]
|
||||
[10.783632, "o", "━"]
|
||||
[10.823758, "o", "━"]
|
||||
[10.863851, "o", "━"]
|
||||
[10.903941, "o", " 20%\r Indexing files... ━━━━━━━━━━"]
|
||||
[10.944044, "o", "━"]
|
||||
[10.984163, "o", "━"]
|
||||
[11.024229, "o", "━"]
|
||||
[11.064409, "o", "━"]
|
||||
[11.104477, "o", "━"]
|
||||
[11.144566, "o", "━"]
|
||||
[11.184615, "o", "━"]
|
||||
[11.224697, "o", " 40%\r Indexing files... ━━━━━━━━━━━━━━━━━━"]
|
||||
[11.264789, "o", "━"]
|
||||
[11.304922, "o", "━"]
|
||||
[11.34499, "o", "━"]
|
||||
[11.385148, "o", "━"]
|
||||
[11.42525, "o", "━"]
|
||||
[11.465388, "o", "━"]
|
||||
[11.505515, "o", "━"]
|
||||
[11.545594, "o", " 60%\r Indexing files... ━━━━━━━━━━━━━━━━━━━━━━━━━━"]
|
||||
[11.585692, "o", "━"]
|
||||
[11.625812, "o", "━"]
|
||||
[11.665872, "o", "━"]
|
||||
[11.706052, "o", "━"]
|
||||
[11.746099, "o", "━"]
|
||||
[11.786201, "o", "━"]
|
||||
[11.826257, "o", "━"]
|
||||
[11.866434, "o", " 80%\r Indexing files... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"]
|
||||
[11.906588, "o", "━"]
|
||||
[11.946604, "o", "━"]
|
||||
[11.986758, "o", "━"]
|
||||
[12.027235, "o", "━"]
|
||||
[12.067334, "o", "━"]
|
||||
[12.107377, "o", "━"]
|
||||
[12.147441, "o", " 100%\r\n\r\n Added 15 chunks to database\r\n\r\nIndexing Complete!\r\nFiles indexed: 3\r\nChunks created: 15\r\nTime taken: 1.2 seconds\r\nSpeed: 2.5 files/second\r\n✅ Indexed 3 files in 1.2s\r\n Created 15 chunks\r\n"]
|
||||
[12.147527, "o", " Speed: 2.5 files/sec\r\n\r\n💡 CLI equivalent: rag-mini index ./demo-project\r\n"]
|
||||
[14.147607, "o", "\u001b[H\u001b[2J╔════════════════════════════════════════════════════╗\r\n║ FSS-Mini-RAG TUI ║\r\n║ Semantic Code Search Interface ║\r\n╚════════════════════════════════════════════════════╝\r\n\r\n🔍 Search Project\r\n=================\r\n\r\nE"]
|
||||
[14.227716, "o", "n"]
|
||||
[14.307806, "o", "t"]
|
||||
[14.387892, "o", "e"]
|
||||
[14.468085, "o", "r"]
|
||||
[14.548197, "o", " "]
|
||||
[14.628255, "o", "s"]
|
||||
[14.708446, "o", "e"]
|
||||
[14.788519, "o", "a"]
|
||||
[14.86859, "o", "r"]
|
||||
[14.94868, "o", "c"]
|
||||
[15.02873, "o", "h"]
|
||||
[15.108879, "o", " "]
|
||||
[15.188919, "o", "q"]
|
||||
[15.269002, "o", "u"]
|
||||
[15.349108, "o", "e"]
|
||||
[15.429214, "o", "r"]
|
||||
[15.509323, "o", "y"]
|
||||
[15.589404, "o", ":"]
|
||||
[15.66948, "o", " "]
|
||||
[15.749622, "o", "\""]
|
||||
[15.829711, "o", "u"]
|
||||
[15.909904, "o", "s"]
|
||||
[15.990037, "o", "e"]
|
||||
[16.070093, "o", "r"]
|
||||
[16.150185, "o", " "]
|
||||
[16.230262, "o", "a"]
|
||||
[16.310347, "o", "u"]
|
||||
[16.390448, "o", "t"]
|
||||
[16.470554, "o", "h"]
|
||||
[16.550682, "o", "e"]
|
||||
[16.630812, "o", "n"]
|
||||
[16.710895, "o", "t"]
|
||||
[16.791063, "o", "i"]
|
||||
[16.871124, "o", "c"]
|
||||
[16.9512, "o", "a"]
|
||||
[17.031378, "o", "t"]
|
||||
[17.111425, "o", "i"]
|
||||
[17.191534, "o", "o"]
|
||||
[17.27162, "o", "n"]
|
||||
[17.351674, "o", "\""]
|
||||
[17.431755, "o", "\r\n"]
|
||||
[18.231819, "o", "\r\n🔍 Searching \"user authentication\" in demo-project\r\n"]
|
||||
[18.731954, "o", "✅ Found 3 results:\r\n\r\n📄 Result 1 (Score: 0.94)\r\n File: auth.py\r\n Function: AuthManager.login()\r\n Preview: Authenticate user and create session...\r\n\r\n"]
|
||||
[19.132078, "o", "📄 Result 2 (Score: 0.87)\r\n File: auth.py\r\n Function: validate_password()\r\n Preview: Validate user password against stored hash...\r\n\r\n"]
|
||||
[19.532193, "o", "📄 Result 3 (Score: 0.82)\r\n File: api_endpoints.py\r\n Function: login_endpoint()\r\n Preview: Handle user login requests...\r\n\r\n"]
|
||||
[19.932399, "o", "💡 CLI equivalent: rag-mini search ./demo-project \"user authentication\"\r\n"]
|
||||
[22.432404, "o", "\u001b[H\u001b[2J╔════════════════════════════════════════════════════╗\r\n║ FSS-Mini-RAG TUI ║\r\n║ Semantic Code Search Interface ║\r\n╚════════════════════════════════════════════════════╝\r\n\r\n🖥️ CLI Command Reference\r\n=========================\r\n\r\nWhat you just did in the TUI:\r\n\r\n1️⃣ Select & Index Project:\r\n rag-mini index ./demo-project\r\n\r\n2️⃣ Search Project:\r\n rag-mini search ./demo-project \"user authentication\"\r\n\r\n3️⃣ Check Status:\r\n rag-mini status ./demo-project\r\n\r\n🚀 You can now use these commands directly!\r\n No TUI required for power users.\r\n"]
|
||||
[25.432541, "o", "\u001b[H\u001b[2J🎉 Demo Complete!\r\n\r\nFSS-Mini-RAG: Semantic code search that actually works\r\nCopy the folder, run ./rag-mini, and start searching!\r\n\r\n"]
|
||||
[25.432566, "o", "Ready to try it yourself? 🚀\r\n"]
|
||||
@ -1,159 +0,0 @@
|
||||
{"version": 2, "width": 80, "height": 24, "timestamp": 1754978845, "env": {"SHELL": "/bin/bash", "TERM": "xterm-256color"}, "title": "FSS-Mini-RAG Demo"}
|
||||
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|
||||
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|
||||
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|
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[11.745039, "o", " 100%\r\n\r\n Added 58 chunks to database\r\n\r\nIndexing Complete!\r\nFiles indexed: 12\r\nChunks created: 58\r\nTime taken: 2.8 seconds\r\nSpeed: 4.3 files/second\r\n✅ Indexed 12 files in 2.8s\r\n Created 58 chunks\r\n Speed: 4.3 files/sec\r\n\r\n💡 CLI equivalent: rag-mini index ./demo-project\r\n"]
|
||||
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|
||||
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|
||||
[18.329754, "o", "✅ Found 8 results:\r\n\r\n📄 Result 1 (Score: 0.94)\r\n File: auth/manager.py\r\n Function: AuthManager.login()\r\n Preview: Authenticate user and create session.\r\n"]
|
||||
[18.32978, "o", " Validates credentials against database and\r\n returns session token on success.\r\n\r\n"]
|
||||
[18.929899, "o", "📄 Result 2 (Score: 0.91)\r\n File: auth/validators.py\r\n Function: validate_password()\r\n Preview: Validate user password against stored hash.\r\n Supports bcrypt, scrypt, and argon2 hashing.\r\n Includes timing attack protection.\r\n\r\n"]
|
||||
[19.530068, "o", "📄 Result 3 (Score: 0.88)\r\n File: middleware/auth.py\r\n Function: require_authentication()\r\n Preview: Authentication middleware decorator.\r\n Checks session tokens and JWT validity.\r\n Redirects to login on authentication failure.\r\n\r\n"]
|
||||
[20.130149, "o", "📄 Result 4 (Score: 0.85)\r\n File: api/endpoints.py\r\n Function: login_endpoint()\r\n Preview: Handle user login API requests.\r\n Accepts JSON credentials, validates input,\r\n and returns authentication tokens.\r\n\r\n"]
|
||||
[20.730301, "o", "📄 Result 5 (Score: 0.82)\r\n File: models/user.py\r\n Function: User.authenticate()\r\n Preview: User model authentication method.\r\n Queries database for user credentials\r\n and handles account status checks.\r\n\r\n"]
|
||||
[21.330399, "o", "💡 CLI equivalent: rag-mini search ./demo-project \"user authentication\"\r\n"]
|
||||
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|
||||
[26.830711, "o", "\u001b[H\u001b[2J🎉 Demo Complete!\r\n\r\nFSS-Mini-RAG: Semantic code search that actually works\r\nCopy the folder, run ./rag-mini, and start searching!\r\n\r\nReady to try it yourself? 🚀\r\n"]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
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|
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|
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|
||||
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|
||||
[7.787329, "o", " 100%\r\n\r\n Added 58 chunks to database\r\n\r\nIndexing Complete!\r\nFiles indexed: 12\r\nChunks created: 58\r\nTime taken: 2.8 seconds\r\nSpeed: 4.3 files/second\r\n✅ Indexed 12 files in 2.8s\r\n Created 58 chunks\r\n Speed: 4.3 files/sec\r\n\r\n💡 CLI equivalent: rag-mini index ./demo-project\r\n"]
|
||||
[9.787461, "o", "\u001b[H\u001b[2J╔════════════════════════════════════════════════════╗\r\n║ FSS-Mini-RAG TUI ║\r\n║ Semantic Code Search Interface ║\r\n╚════════════════════════════════════════════════════╝\r\n\r\n🔍 Search Project\r\n=================\r\n\r\nSearch query: \""]
|
||||
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|
||||
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|
||||
[10.027763, "o", "e"]
|
||||
[10.107854, "o", "r"]
|
||||
[10.187948, "o", " "]
|
||||
[10.268032, "o", "a"]
|
||||
[10.348137, "o", "u"]
|
||||
[10.428233, "o", "t"]
|
||||
[10.508289, "o", "h"]
|
||||
[10.588404, "o", "e"]
|
||||
[10.668488, "o", "n"]
|
||||
[10.748564, "o", "t"]
|
||||
[10.828606, "o", "i"]
|
||||
[10.908678, "o", "c"]
|
||||
[10.988754, "o", "a"]
|
||||
[11.068836, "o", "t"]
|
||||
[11.148986, "o", "i"]
|
||||
[11.229091, "o", "o"]
|
||||
[11.309143, "o", "n"]
|
||||
[11.389238, "o", "\""]
|
||||
[11.469342, "o", "\r\n"]
|
||||
[12.269425, "o", "\r\n🔍 Searching \"user authentication\" in demo-project\r\n"]
|
||||
[12.76953, "o", "✅ Found 8 results:\r\n\r\n📄 Result 1 (Score: 0.94)\r\n File: auth/manager.py\r\n Function: AuthManager.login()\r\n Preview: Authenticate user and create session.\r\n"]
|
||||
[12.769549, "o", " Validates credentials against database and\r\n returns session token on success.\r\n\r\n"]
|
||||
[13.369697, "o", "📄 Result 2 (Score: 0.91)\r\n File: auth/validators.py\r\n Function: validate_password()\r\n Preview: Validate user password against stored hash.\r\n Supports bcrypt, scrypt, and argon2 hashing.\r\n Includes timing attack protection.\r\n\r\n"]
|
||||
[13.969821, "o", "📄 Result 3 (Score: 0.88)\r\n File: middleware/auth.py\r\n Function: require_authentication()\r\n Preview: Authentication middleware decorator.\r\n Checks session tokens and JWT validity.\r\n Redirects to login on authentication failure.\r\n\r\n"]
|
||||
[14.569938, "o", "📄 Result 4 (Score: 0.85)\r\n File: api/endpoints.py\r\n Function: login_endpoint()\r\n Preview: Handle user login API requests.\r\n Accepts JSON credentials, validates input,\r\n and returns authentication tokens.\r\n\r\n"]
|
||||
[15.170064, "o", "📄 Result 5 (Score: 0.82)\r\n File: models/user.py\r\n Function: User.authenticate()\r\n Preview: User model authentication method.\r\n Queries database for user credentials\r\n and handles account status checks.\r\n\r\n"]
|
||||
[15.770453, "o", "💡 CLI equivalent: rag-mini search ./demo-project \"user authentication\"\r\n"]
|
||||
[18.270591, "o", "\u001b[H\u001b[2J╔════════════════════════════════════════════════════╗\r\n║ FSS-Mini-RAG TUI ║\r\n║ Semantic Code Search Interface ║\r\n╚════════════════════════════════════════════════════╝\r\n\r\n🖥️ CLI Command Reference\r\n=========================\r\n\r\nWhat you just did in the TUI:\r\n\r\n1️⃣ Select & Index Project:\r\n rag-mini index ./demo-project\r\n # Indexed 12 files → 58 semantic chunks\r\n\r\n2️⃣ Search Project:\r\n rag-mini search ./demo-project \"user authentication\"\r\n # Found 8 relevant matches with context\r\n\r\n3️⃣ Check Status:\r\n rag-mini status ./demo-project\r\n\r\n🚀 You can now use these commands directly!\r\n No TUI required for power users.\r\n\r\n💡 Try semantic queries like:\r\n • \"error handling\" • \"database queries\"\r\n • \"API validation\" • \"configuration management\"\r\n"]
|
||||
[21.270814, "o", "\u001b[H\u001b[2J🎉 Demo Complete!\r\n\r\nFSS-Mini-RAG: Semantic code search that actually works\r\nCopy the folder, run ./rag-mini, and start searching!\r\n\r\nReady to try it yourself? 🚀\r\n"]
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 179 KiB |
@ -1,265 +0,0 @@
|
||||
# RAG System Comprehensive Analysis
|
||||
## Dual-Perspective Synthesis Report
|
||||
|
||||
### Executive Summary
|
||||
|
||||
After comprehensive analysis from both beginner (Emma) and expert (Michael) perspectives, this RAG system emerges as an **exceptional educational tool** that successfully balances accessibility with technical sophistication. The system achieves a rare feat: being genuinely useful for beginners while maintaining production-quality architecture patterns.
|
||||
|
||||
**Overall Assessment: 8.7/10** - Outstanding educational project with production potential
|
||||
|
||||
---
|
||||
|
||||
## Convergent Findings: Where Both Perspectives Align
|
||||
|
||||
### 🌟 **Universal Strengths**
|
||||
|
||||
**Educational Excellence** ✅
|
||||
Both analysts praised the progressive complexity design:
|
||||
- **Emma**: "Brilliant educational approach! TUI shows CLI commands as you use it"
|
||||
- **Michael**: "Educational excellence - best-in-class for learning RAG concepts"
|
||||
|
||||
**Robust Architecture** ✅
|
||||
Both recognized the solid engineering foundation:
|
||||
- **Emma**: "Smart fallback system - Ollama → ML models → Hash means it always works"
|
||||
- **Michael**: "Multi-tier fallback system prevents system failure when components unavailable"
|
||||
|
||||
**Clear Code Organization** ✅
|
||||
Both appreciated the modular design:
|
||||
- **Emma**: "Single responsibility - each file does one main thing"
|
||||
- **Michael**: "Clean separation of concerns with interface-driven design"
|
||||
|
||||
**Production-Ready Error Handling** ✅
|
||||
Both noted comprehensive error management:
|
||||
- **Emma**: "Clear error messages include suggested solutions"
|
||||
- **Michael**: "Graceful fallbacks for every external dependency"
|
||||
|
||||
### ⚠️ **Shared Concerns**
|
||||
|
||||
**Configuration Complexity** ❌
|
||||
Both found configuration overwhelming:
|
||||
- **Emma**: "6 different configuration classes - overwhelming for beginners"
|
||||
- **Michael**: "Nested dataclass configuration is verbose and hard to extend"
|
||||
|
||||
**Technical Jargon Barriers** ❌
|
||||
Both noted explanation gaps:
|
||||
- **Emma**: "Embeddings used everywhere but never explained in simple terms"
|
||||
- **Michael**: "Missing beginner glossary for core concepts"
|
||||
|
||||
**Scalability Questions** ❌
|
||||
Both raised scaling concerns:
|
||||
- **Emma**: "Memory usage could spike with very large codebases"
|
||||
- **Michael**: "Single-process architecture may become bottleneck at >50k files"
|
||||
|
||||
---
|
||||
|
||||
## Divergent Insights: Where Perspectives Differ
|
||||
|
||||
### Technical Implementation Assessment
|
||||
|
||||
**Emma's Beginner View:**
|
||||
- Sees complexity as intimidating barriers to entry
|
||||
- Focuses on what makes learning difficult vs. easy
|
||||
- Values simplification over sophisticated features
|
||||
- Concerned about overwhelming new users
|
||||
|
||||
**Michael's Expert View:**
|
||||
- Appreciates architectural sophistication
|
||||
- Evaluates production readiness and scalability
|
||||
- Values technical depth and implementation quality
|
||||
- Focused on enterprise concerns and maintainability
|
||||
|
||||
### Key Perspective Splits
|
||||
|
||||
| Aspect | Emma (Beginner) | Michael (Expert) |
|
||||
|--------|----------------|------------------|
|
||||
| **Configuration** | "Too many options, overwhelming" | "Verbose but well-structured" |
|
||||
| **Fallback Logic** | "Complex but works reliably" | "Sophisticated error recovery" |
|
||||
| **Code Comments** | "Need more explanation" | "Good documentation coverage" |
|
||||
| **Architecture** | "Hard to follow threading" | "Clean modular design" |
|
||||
| **Error Handling** | "Try/catch blocks confusing" | "Comprehensive exception handling" |
|
||||
|
||||
---
|
||||
|
||||
## Synthesis Assessment by Use Case
|
||||
|
||||
### 🎓 **For Learning/Educational Use**
|
||||
**Rating: 9.5/10**
|
||||
|
||||
**Strengths:**
|
||||
- Progressive disclosure from TUI → CLI → Python API
|
||||
- Real production patterns without oversimplification
|
||||
- Working examples that actually demonstrate concepts
|
||||
- Multiple entry points for different comfort levels
|
||||
|
||||
**Recommendations:**
|
||||
1. Add beginner glossary explaining RAG, embeddings, chunking in plain English
|
||||
2. Create configuration presets: "simple", "advanced", "production"
|
||||
3. Add visual guide with TUI screenshots
|
||||
4. Include troubleshooting FAQ with common issues
|
||||
|
||||
### 🏢 **For Production Use**
|
||||
**Rating: 7.5/10**
|
||||
|
||||
**Strengths:**
|
||||
- Solid architectural foundation with proper patterns
|
||||
- Comprehensive error handling and graceful degradation
|
||||
- Performance optimizations (hybrid search, caching)
|
||||
- Clean, maintainable codebase
|
||||
|
||||
**Limitations:**
|
||||
- Single-process architecture limits scalability
|
||||
- Missing enterprise features (auth, monitoring, containers)
|
||||
- Thread safety concerns in high-concurrency scenarios
|
||||
- No database abstraction layer
|
||||
|
||||
**Recommendations:**
|
||||
1. Add containerization and deployment configs
|
||||
2. Implement structured logging and metrics
|
||||
3. Add authentication/authorization layer
|
||||
4. Create database abstraction for vector store switching
|
||||
|
||||
### 🛠 **For Development/Experimentation**
|
||||
**Rating: 9.0/10**
|
||||
|
||||
**Strengths:**
|
||||
- Easy to modify and extend
|
||||
- Clear extension points and plugin architecture
|
||||
- Good debugging capabilities
|
||||
- Multiple embedding fallbacks for reliability
|
||||
|
||||
**Perfect For:**
|
||||
- RAG concept experimentation
|
||||
- Custom chunking algorithm development
|
||||
- Embedding model comparisons
|
||||
- Local development workflows
|
||||
|
||||
---
|
||||
|
||||
## Critical Success Factors
|
||||
|
||||
### What Makes This System Exceptional
|
||||
|
||||
**1. Educational Design Philosophy**
|
||||
Unlike most RAG tutorials that are too simple or enterprise systems that are too complex, this system:
|
||||
- Uses real production patterns
|
||||
- Maintains approachability for beginners
|
||||
- Provides multiple complexity levels
|
||||
- Includes working, non-trivial examples
|
||||
|
||||
**2. Engineering Maturity**
|
||||
- Proper error handling with specific exception types
|
||||
- Graceful degradation across all components
|
||||
- Performance optimizations (hybrid search, caching)
|
||||
- Clean separation of concerns
|
||||
|
||||
**3. Practical Usability**
|
||||
- Works out of the box with sensible defaults
|
||||
- Multiple interfaces for different user types
|
||||
- Comprehensive fallback systems
|
||||
- Clear status reporting and debugging info
|
||||
|
||||
### Critical Weaknesses to Address
|
||||
|
||||
**1. Documentation Gap**
|
||||
- Missing beginner glossary for technical terms
|
||||
- No architectural overview for developers
|
||||
- Limited troubleshooting guidance
|
||||
- Few usage examples beyond basic case
|
||||
|
||||
**2. Configuration Complexity**
|
||||
- Too many options without clear guidance
|
||||
- No preset configurations for common use cases
|
||||
- Runtime configuration validation missing
|
||||
- Complex option interdependencies
|
||||
|
||||
**3. Scalability Architecture**
|
||||
- Single-process threading model
|
||||
- No distributed processing capabilities
|
||||
- Memory usage concerns for large projects
|
||||
- Limited concurrent user support
|
||||
|
||||
---
|
||||
|
||||
## Strategic Recommendations
|
||||
|
||||
### Immediate Improvements (High Impact, Low Effort)
|
||||
|
||||
**1. Documentation Enhancement**
|
||||
```markdown
|
||||
- Add beginner glossary (RAG, embeddings, chunks, vectors)
|
||||
- Create configuration presets (simple/advanced/production)
|
||||
- Add troubleshooting FAQ
|
||||
- Include TUI screenshots and visual guide
|
||||
```
|
||||
|
||||
**2. Configuration Simplification**
|
||||
```python
|
||||
# Add preset configurations
|
||||
config = RAGConfig.preset("beginner") # Minimal options
|
||||
config = RAGConfig.preset("production") # Optimized defaults
|
||||
```
|
||||
|
||||
**3. Better Error Messages**
|
||||
```python
|
||||
# More contextual error messages
|
||||
"❌ Ollama not available. Falling back to lightweight embeddings.
|
||||
To use full features: brew install ollama && ollama serve"
|
||||
```
|
||||
|
||||
### Medium-Term Enhancements
|
||||
|
||||
**1. Enterprise Features**
|
||||
- Add structured logging (JSON format)
|
||||
- Implement metrics export (Prometheus)
|
||||
- Create Docker containers
|
||||
- Add basic authentication layer
|
||||
|
||||
**2. Performance Optimization**
|
||||
- Database abstraction layer
|
||||
- Connection pooling improvements
|
||||
- Memory usage optimization
|
||||
- Batch processing enhancements
|
||||
|
||||
**3. Developer Experience**
|
||||
- Plugin architecture documentation
|
||||
- Extension examples
|
||||
- Development setup guide
|
||||
- Contribution guidelines
|
||||
|
||||
### Long-Term Evolution
|
||||
|
||||
**1. Scalability Architecture**
|
||||
- Multi-process architecture option
|
||||
- Distributed processing capabilities
|
||||
- Horizontal scaling support
|
||||
- Load balancing integration
|
||||
|
||||
**2. Advanced Features**
|
||||
- Real-time collaboration support
|
||||
- Advanced query processing
|
||||
- Custom model integration
|
||||
- Enterprise security features
|
||||
|
||||
---
|
||||
|
||||
## Final Verdict
|
||||
|
||||
This RAG system represents a **remarkable achievement** in educational software engineering. It successfully demonstrates that production-quality software can be accessible to beginners without sacrificing technical sophistication.
|
||||
|
||||
### Key Success Metrics:
|
||||
- ✅ **Beginner Accessibility**: 8/10 (needs documentation improvements)
|
||||
- ✅ **Technical Quality**: 9/10 (excellent architecture and implementation)
|
||||
- ✅ **Educational Value**: 10/10 (outstanding progressive complexity)
|
||||
- ✅ **Production Viability**: 7/10 (solid foundation, needs enterprise features)
|
||||
|
||||
### Primary Use Cases:
|
||||
1. **Educational Tool**: Perfect for learning RAG concepts
|
||||
2. **Development Platform**: Excellent for experimentation and prototyping
|
||||
3. **Production Foundation**: Strong base requiring additional hardening
|
||||
|
||||
### Bottom Line:
|
||||
**This system achieves the rare balance of being genuinely educational while maintaining production-quality patterns.** With targeted improvements in documentation and configuration simplification, it could become the gold standard for RAG educational resources.
|
||||
|
||||
The convergent praise from both beginner and expert perspectives validates the fundamental design decisions, while the divergent concerns provide a clear roadmap for enhancement priorities.
|
||||
|
||||
**Recommendation: Highly suitable for educational use, excellent foundation for production development, needs targeted improvements for enterprise deployment.**
|
||||
@ -1,184 +0,0 @@
|
||||
# RAG System Codebase Analysis - Beginner's Perspective
|
||||
|
||||
## What I Found **GOOD** 📈
|
||||
|
||||
### **Clear Entry Points and Documentation**
|
||||
- **README.md**: Excellent start! The mermaid diagram showing "Files → Index → Chunks → Embeddings → Database" makes the flow crystal clear
|
||||
- **GET_STARTED.md**: Perfect 2-minute quick start guide - exactly what beginners need
|
||||
- **Multiple entry points**: The three different ways to use it (`./rag-tui`, `./rag-mini`, `./install_mini_rag.sh`) gives options for different comfort levels
|
||||
|
||||
### **Beginner-Friendly Design Philosophy**
|
||||
- **TUI (Text User Interface)**: The `rag-tui.py` shows CLI commands as you use the interface - brilliant educational approach!
|
||||
- **Progressive complexity**: You can start simple with the TUI, then graduate to CLI commands
|
||||
- **Helpful error messages**: In `rag-mini.py`, errors like "❌ Project not indexed" include the solution: "Run: rag-mini index /path/to/project"
|
||||
|
||||
### **Excellent Code Organization**
|
||||
- **Clean module structure**: `mini_rag/` contains all the core code with logical names like `chunker.py`, `search.py`, `indexer.py`
|
||||
- **Single responsibility**: Each file does one main thing - the chunker chunks, the searcher searches, etc.
|
||||
- **Good naming**: Functions like `index_project()`, `search_project()`, `status_check()` are self-explanatory
|
||||
|
||||
### **Smart Fallback System**
|
||||
- **Multiple embedding options**: Ollama → ML models → Hash-based fallbacks means it always works
|
||||
- **Clear status reporting**: Shows which system is active: "✅ Ollama embeddings active" or "⚠️ Using hash-based embeddings"
|
||||
|
||||
### **Educational Examples**
|
||||
- **`examples/basic_usage.py`**: Perfect beginner example showing step-by-step usage
|
||||
- **Test files**: Like `tests/01_basic_integration_test.py` that create sample code and show how everything works together
|
||||
- **Configuration examples**: The YAML config in `examples/config.yaml` has helpful comments explaining each setting
|
||||
|
||||
## What Could Use **IMPROVEMENT** 📝
|
||||
|
||||
### **Configuration Complexity**
|
||||
- **Too many options**: The `config.py` file has 6 different configuration classes (ChunkingConfig, StreamingConfig, etc.) - overwhelming for beginners
|
||||
- **YAML complexity**: The config file has lots of technical terms like "threshold_bytes", "similarity_threshold" without beginner explanations
|
||||
- **Default confusion**: Hard to know which settings to change as a beginner
|
||||
|
||||
### **Technical Jargon Without Explanation**
|
||||
- **"Embeddings"**: Used everywhere but never explained in simple terms
|
||||
- **"Vector database"**: Mentioned but not explained what it actually does
|
||||
- **"Chunking strategy"**: Options like "semantic" vs "fixed" need plain English explanations
|
||||
- **"BM25"**, **"similarity_threshold"**: Very technical terms without context
|
||||
|
||||
### **Complex Installation Options**
|
||||
- **Three different installation methods**: The README shows experimental copy & run, full installation, AND manual setup - confusing which to pick
|
||||
- **Ollama dependency**: Not clear what Ollama actually is or why you need it
|
||||
- **Requirements confusion**: Two different requirements files (`requirements.txt` and `requirements-full.txt`)
|
||||
|
||||
### **Code Complexity in Core Modules**
|
||||
- **`ollama_embeddings.py`**: 200+ lines with complex fallback logic - hard to understand the flow
|
||||
- **`llm_synthesizer.py`**: Model selection logic with long lists of model rankings - overwhelming
|
||||
- **Error handling**: Lots of try/catch blocks without explaining what could go wrong and why
|
||||
|
||||
### **Documentation Gaps**
|
||||
- **Missing beginner glossary**: No simple definitions of key terms
|
||||
- **No troubleshooting guide**: What to do when things don't work
|
||||
- **Limited examples**: Only one basic usage example, need more scenarios
|
||||
- **No visual guide**: Could use screenshots or diagrams of what the TUI looks like
|
||||
|
||||
## What I Found **EASY** ✅
|
||||
|
||||
### **Getting Started Flow**
|
||||
- **Installation script**: `./install_mini_rag.sh` handles everything automatically
|
||||
- **TUI interface**: Menu-driven, no need to memorize commands
|
||||
- **Basic CLI commands**: `./rag-mini index /path` and `./rag-mini search /path "query"` are intuitive
|
||||
|
||||
### **Project Structure**
|
||||
- **Logical file organization**: Everything related to chunking is in `chunker.py`, search stuff in `search.py`
|
||||
- **Clear entry points**: `rag-mini.py` and `rag-tui.py` are obvious starting points
|
||||
- **Documentation location**: All docs in `docs/` folder, examples in `examples/`
|
||||
|
||||
### **Configuration Files**
|
||||
- **YAML format**: Much easier than JSON or code-based config
|
||||
- **Comments in config**: The example config has helpful explanations
|
||||
- **Default values**: Works out of the box without any configuration
|
||||
|
||||
### **Basic Usage Pattern**
|
||||
- **Index first, then search**: Clear two-step process
|
||||
- **Consistent commands**: All CLI commands follow the same pattern
|
||||
- **Status checking**: `./rag-mini status /path` shows what's happening
|
||||
|
||||
## What I Found **HARD** 😰
|
||||
|
||||
### **Understanding the Core Concepts**
|
||||
- **What is RAG?**: The acronym is never explained in beginner terms
|
||||
- **How embeddings work**: The system creates "768-dimension vectors" - what does that even mean?
|
||||
- **Why chunking matters**: Not clear why text needs to be split up at all
|
||||
- **Vector similarity**: How does the system actually find relevant results?
|
||||
|
||||
### **Complex Configuration Options**
|
||||
- **Embedding methods**: "ollama", "ml", "hash", "auto" - which one should I use?
|
||||
- **Chunking strategies**: "semantic" vs "fixed" - no clear guidance on when to use which
|
||||
- **Model selection**: In `llm_synthesizer.py`, there's a huge list of model names like "qwen2.5:1.5b" - how do I know what's good?
|
||||
|
||||
### **Error Debugging**
|
||||
- **Dependency issues**: If Ollama isn't installed, error messages assume I know what Ollama is
|
||||
- **Import errors**: Complex fallback logic means errors could come from many places
|
||||
- **Performance problems**: No guidance on what to do if indexing is slow or search results are poor
|
||||
|
||||
### **Advanced Features**
|
||||
- **LLM synthesis**: The `--synthesize` flag does something but it's not clear what or when to use it
|
||||
- **Query expansion**: Happens automatically but no explanation of why or how to control it
|
||||
- **Streaming mode**: For large files but no guidance on when it matters
|
||||
|
||||
### **Code Architecture**
|
||||
- **Multiple inheritance**: Classes inherit from each other in complex ways
|
||||
- **Async patterns**: Some threading and concurrent processing that's hard to follow
|
||||
- **Caching logic**: Complex caching systems in multiple places
|
||||
|
||||
## What Might Work or Might Not Work ⚖️
|
||||
|
||||
### **Features That Seem Well-Implemented** ✅
|
||||
|
||||
#### **Fallback System**
|
||||
- **Multiple backup options**: Ollama → ML → Hash means it should always work
|
||||
- **Clear status reporting**: System tells you which method is active
|
||||
- **Graceful degradation**: Falls back to simpler methods if complex ones fail
|
||||
|
||||
#### **Error Handling**
|
||||
- **Input validation**: Checks if paths exist, handles missing files gracefully
|
||||
- **Clear error messages**: Most errors include suggested solutions
|
||||
- **Safe defaults**: System works out of the box without configuration
|
||||
|
||||
#### **Multi-Interface Design**
|
||||
- **TUI for beginners**: Menu-driven interface with help
|
||||
- **CLI for power users**: Direct commands for efficiency
|
||||
- **Python API**: Can be integrated into other tools
|
||||
|
||||
### **Features That Look Questionable** ⚠️
|
||||
|
||||
#### **Complex Model Selection Logic**
|
||||
- **Too many options**: 20+ different model preferences in `llm_synthesizer.py`
|
||||
- **Auto-selection might fail**: Complex ranking logic could pick wrong model
|
||||
- **No fallback validation**: If model selection fails, unclear what happens
|
||||
|
||||
#### **Caching Strategy**
|
||||
- **Multiple cache layers**: Query expansion cache, embedding cache, search cache
|
||||
- **No cache management**: No clear way to clear or manage cache size
|
||||
- **Potential memory issues**: Caches could grow large over time
|
||||
|
||||
#### **Configuration Complexity**
|
||||
- **Too many knobs**: 20+ configuration options across 6 different sections
|
||||
- **Unclear interactions**: Changing one setting might affect others in unexpected ways
|
||||
- **No validation**: System might accept invalid configurations
|
||||
|
||||
### **Areas of Uncertainty** ❓
|
||||
|
||||
#### **Performance and Scalability**
|
||||
- **Large project handling**: Streaming mode exists but unclear when it kicks in
|
||||
- **Memory usage**: No guidance on memory requirements for different project sizes
|
||||
- **Concurrent usage**: Multiple users or processes might conflict
|
||||
|
||||
#### **AI Model Dependencies**
|
||||
- **Ollama reliability**: Heavy dependence on external Ollama service
|
||||
- **Model availability**: Code references specific models that might not exist
|
||||
- **Version compatibility**: No clear versioning strategy for AI models
|
||||
|
||||
#### **Cross-Platform Support**
|
||||
- **Windows compatibility**: Some shell scripts and path handling might not work
|
||||
- **Python version requirements**: Claims Python 3.8+ but some features might need newer versions
|
||||
- **Dependency conflicts**: Complex ML dependencies could have version conflicts
|
||||
|
||||
## **Summary Assessment** 🎯
|
||||
|
||||
This is a **well-architected system with excellent educational intent**, but it suffers from **complexity creep** that makes it intimidating for true beginners.
|
||||
|
||||
### **Strengths for Beginners:**
|
||||
- Excellent progressive disclosure from TUI to CLI to Python API
|
||||
- Good documentation structure and helpful error messages
|
||||
- Smart fallback systems ensure it works in most environments
|
||||
- Clear, logical code organization
|
||||
|
||||
### **Main Barriers for Beginners:**
|
||||
- Too much technical jargon without explanation
|
||||
- Configuration options are overwhelming
|
||||
- Core concepts (embeddings, vectors, chunking) not explained in simple terms
|
||||
- Installation has too many paths and options
|
||||
|
||||
### **Recommendations:**
|
||||
1. **Add a glossary** explaining RAG, embeddings, chunking, vectors in plain English
|
||||
2. **Simplify configuration** with "beginner", "intermediate", "advanced" presets
|
||||
3. **More examples** showing different use cases and project types
|
||||
4. **Visual guide** with screenshots of the TUI and expected outputs
|
||||
5. **Troubleshooting section** with common problems and solutions
|
||||
|
||||
The foundation is excellent - this just needs some beginner-focused documentation and simplification to reach its educational potential.
|
||||
@ -1,322 +0,0 @@
|
||||
# FSS-Mini-RAG Technical Analysis
|
||||
## Experienced Developer's Assessment
|
||||
|
||||
### Executive Summary
|
||||
|
||||
This is a **well-architected, production-ready RAG system** that successfully bridges the gap between oversimplified tutorials and enterprise-complexity implementations. The codebase demonstrates solid engineering practices with a clear focus on educational value without sacrificing technical quality.
|
||||
|
||||
**Overall Rating: 8.5/10** - Impressive for an educational project with production aspirations.
|
||||
|
||||
---
|
||||
|
||||
## What I Found GOOD
|
||||
|
||||
### 🏗️ **Excellent Architecture Decisions**
|
||||
|
||||
**Modular Design Pattern**
|
||||
- Clean separation of concerns: `chunker.py`, `indexer.py`, `search.py`, `embedder.py`
|
||||
- Each module has a single, well-defined responsibility
|
||||
- Proper dependency injection throughout (e.g., `ProjectIndexer` accepts optional `embedder` and `chunker`)
|
||||
- Interface-driven design allows easy testing and extension
|
||||
|
||||
**Robust Embedding Strategy**
|
||||
- **Multi-tier fallback system**: Ollama → ML models → Hash-based embeddings
|
||||
- Graceful degradation prevents system failure when components are unavailable
|
||||
- Smart model selection with performance rankings (`qwen3:0.6b` first for CPU efficiency)
|
||||
- Caching and connection pooling for performance
|
||||
|
||||
**Advanced Chunking Algorithm**
|
||||
- **AST-based chunking for Python** - preserves semantic boundaries
|
||||
- Language-aware parsing for JavaScript, Go, Java, Markdown
|
||||
- Smart size constraints with overflow handling
|
||||
- Metadata tracking (parent class, next/previous chunks, file context)
|
||||
|
||||
### 🚀 **Production-Ready Features**
|
||||
|
||||
**Streaming Architecture**
|
||||
- Large file processing with configurable thresholds (1MB default)
|
||||
- Memory-efficient batch processing with concurrent embedding
|
||||
- Queue-based file watching with debouncing and deduplication
|
||||
|
||||
**Comprehensive Error Handling**
|
||||
- Specific exception types with actionable error messages
|
||||
- Multiple encoding fallbacks (`utf-8` → `latin-1` → `cp1252`)
|
||||
- Database schema validation and automatic migration
|
||||
- Graceful fallbacks for every external dependency
|
||||
|
||||
**Performance Optimizations**
|
||||
- LanceDB with fixed-dimension vectors for optimal indexing
|
||||
- Hybrid search combining vector similarity + BM25 keyword matching
|
||||
- Smart re-ranking with file importance and recency boosts
|
||||
- Connection pooling and query caching
|
||||
|
||||
**Operational Excellence**
|
||||
- Incremental indexing with file change detection (hash + mtime)
|
||||
- Comprehensive statistics and monitoring
|
||||
- Configuration management with YAML validation
|
||||
- Clean logging with different verbosity levels
|
||||
|
||||
### 📚 **Educational Value**
|
||||
|
||||
**Code Quality for Learning**
|
||||
- Extensive documentation and type hints throughout
|
||||
- Clear variable naming and logical flow
|
||||
- Educational tests that demonstrate capabilities
|
||||
- Progressive complexity from basic to advanced features
|
||||
|
||||
**Multiple Interface Design**
|
||||
- CLI for power users
|
||||
- TUI for beginners (shows CLI commands as you use it)
|
||||
- Python API for integration
|
||||
- Server mode for persistent usage
|
||||
|
||||
---
|
||||
|
||||
## What Could Use IMPROVEMENT
|
||||
|
||||
### ⚠️ **Architectural Weaknesses**
|
||||
|
||||
**Database Abstraction Missing**
|
||||
- Direct LanceDB coupling throughout `indexer.py` and `search.py`
|
||||
- No database interface layer makes switching vector stores difficult
|
||||
- Schema changes require dropping/recreating entire table
|
||||
|
||||
**Configuration Complexity**
|
||||
- Nested dataclass configuration is verbose and hard to extend
|
||||
- No runtime configuration validation beyond YAML parsing
|
||||
- Configuration changes require restart (no hot-reloading)
|
||||
|
||||
**Limited Scalability Architecture**
|
||||
- Single-process design with threading (not multi-process)
|
||||
- No distributed processing capabilities
|
||||
- Memory usage could spike with very large codebases
|
||||
|
||||
### 🐛 **Code Quality Issues**
|
||||
|
||||
**Error Handling Inconsistencies**
|
||||
```python
|
||||
# Some functions return None on error, others raise exceptions
|
||||
# This makes client code error handling unpredictable
|
||||
try:
|
||||
records = self._process_file(file_path)
|
||||
if records: # Could be None or empty list
|
||||
# Handle success
|
||||
except Exception as e:
|
||||
# Also need to handle exceptions
|
||||
```
|
||||
|
||||
**Thread Safety Concerns**
|
||||
- File watcher uses shared state between threads without proper locking
|
||||
- LanceDB connection sharing across threads not explicitly handled
|
||||
- Cache operations in `QueryExpander` may have race conditions
|
||||
|
||||
**Testing Coverage Gaps**
|
||||
- Integration tests exist but limited unit test coverage
|
||||
- No performance regression tests
|
||||
- Error path testing is minimal
|
||||
|
||||
### 🏗️ **Missing Enterprise Features**
|
||||
|
||||
**Security Considerations**
|
||||
- No input sanitization for search queries
|
||||
- File path traversal protection could be stronger
|
||||
- No authentication/authorization for server mode
|
||||
|
||||
**Monitoring and Observability**
|
||||
- Basic logging but no structured logging (JSON)
|
||||
- No metrics export (Prometheus/StatsD)
|
||||
- Limited distributed tracing capabilities
|
||||
|
||||
**Deployment Support**
|
||||
- No containerization (Docker)
|
||||
- No service discovery or load balancing support
|
||||
- Configuration management for multiple environments
|
||||
|
||||
---
|
||||
|
||||
## What I Found EASY
|
||||
|
||||
### 🎯 **Well-Designed APIs**
|
||||
|
||||
**Intuitive Class Interfaces**
|
||||
```python
|
||||
# Clean, predictable API design
|
||||
searcher = CodeSearcher(project_path)
|
||||
results = searcher.search("authentication logic", top_k=10)
|
||||
```
|
||||
|
||||
**Consistent Method Signatures**
|
||||
- Similar parameter patterns across classes
|
||||
- Good defaults that work out of the box
|
||||
- Optional parameters that don't break existing code
|
||||
|
||||
**Clear Extension Points**
|
||||
- `CodeEmbedder` interface allows custom embedding implementations
|
||||
- `CodeChunker` can be extended for new languages
|
||||
- Plugin architecture through configuration
|
||||
|
||||
### 📦 **Excellent Abstraction Layers**
|
||||
|
||||
**Configuration Management**
|
||||
- Single `RAGConfig` object handles all settings
|
||||
- Environment variable support
|
||||
- Validation with helpful error messages
|
||||
|
||||
**Path Handling**
|
||||
- Consistent normalization across the system
|
||||
- Cross-platform compatibility
|
||||
- Proper relative/absolute path handling
|
||||
|
||||
---
|
||||
|
||||
## What I Found HARD
|
||||
|
||||
### 😤 **Complex Implementation Areas**
|
||||
|
||||
**Vector Database Schema Management**
|
||||
```python
|
||||
# Schema evolution is complex and brittle
|
||||
if not required_fields.issubset(existing_fields):
|
||||
logger.warning("Schema mismatch detected. Dropping and recreating table.")
|
||||
self.db.drop_table("code_vectors") # Loses all data!
|
||||
```
|
||||
|
||||
**Hybrid Search Algorithm**
|
||||
- Complex scoring calculation combining semantic + BM25 + ranking boosts
|
||||
- Difficult to tune weights for different use cases
|
||||
- Performance tuning requires deep understanding of the algorithm
|
||||
|
||||
**File Watching Complexity**
|
||||
- Queue-based processing with batching logic
|
||||
- Debouncing and deduplication across multiple threads
|
||||
- Race condition potential between file changes and indexing
|
||||
|
||||
### 🧩 **Architectural Complexity**
|
||||
|
||||
**Multi-tier Embedding Fallbacks**
|
||||
- Complex initialization logic across multiple embedding providers
|
||||
- Model selection heuristics are hard-coded and inflexible
|
||||
- Error recovery paths are numerous and hard to test comprehensively
|
||||
|
||||
**Configuration Hierarchy**
|
||||
- Multiple configuration sources (YAML, defaults, runtime)
|
||||
- Precedence rules not always clear
|
||||
- Validation happens at different levels
|
||||
|
||||
---
|
||||
|
||||
## What Might Work vs. Might Not Work
|
||||
|
||||
### ✅ **Likely to Work Well**
|
||||
|
||||
**Small to Medium Projects (< 10k files)**
|
||||
- Architecture handles this scale efficiently
|
||||
- Memory usage remains reasonable
|
||||
- Performance is excellent
|
||||
|
||||
**Educational and Development Use**
|
||||
- Great for learning RAG concepts
|
||||
- Easy to modify and experiment with
|
||||
- Good debugging capabilities
|
||||
|
||||
**Local Development Workflows**
|
||||
- File watching works well for active development
|
||||
- Fast incremental updates
|
||||
- Good integration with existing tools
|
||||
|
||||
### ❓ **Questionable at Scale**
|
||||
|
||||
**Very Large Codebases (>50k files)**
|
||||
- Single-process architecture may become bottleneck
|
||||
- Memory usage could become problematic
|
||||
- Indexing time might be excessive
|
||||
|
||||
**Production Web Services**
|
||||
- No built-in rate limiting or request queuing
|
||||
- Single point of failure design
|
||||
- Limited monitoring and alerting
|
||||
|
||||
**Multi-tenant Environments**
|
||||
- No isolation between projects
|
||||
- Resource sharing concerns
|
||||
- Security isolation gaps
|
||||
|
||||
---
|
||||
|
||||
## Technical Implementation Assessment
|
||||
|
||||
### 📊 **Code Metrics**
|
||||
- **~12,000 lines** of Python code (excluding tests/docs)
|
||||
- **Good module size distribution** (largest file: `search.py` at ~780 lines)
|
||||
- **Reasonable complexity** per function
|
||||
- **Strong type hint coverage** (~85%+)
|
||||
|
||||
### 🔧 **Engineering Practices**
|
||||
|
||||
**Version Control & Organization**
|
||||
- Clean git history with logical commits
|
||||
- Proper `.gitignore` with RAG-specific entries
|
||||
- Good directory structure following Python conventions
|
||||
|
||||
**Documentation Quality**
|
||||
- Comprehensive docstrings with examples
|
||||
- Architecture diagrams and visual guides
|
||||
- Progressive learning materials
|
||||
|
||||
**Dependency Management**
|
||||
- Minimal, well-chosen dependencies
|
||||
- Optional dependency handling for fallbacks
|
||||
- Clear requirements separation
|
||||
|
||||
### 🚦 **Performance Characteristics**
|
||||
|
||||
**Indexing Performance**
|
||||
- ~50-100 files/second (reasonable for the architecture)
|
||||
- Memory usage scales linearly with file size
|
||||
- Good for incremental updates
|
||||
|
||||
**Search Performance**
|
||||
- Sub-50ms search latency (excellent)
|
||||
- Vector similarity + keyword hybrid approach works well
|
||||
- Results quality is good for code search
|
||||
|
||||
**Resource Usage**
|
||||
- Moderate memory footprint (~200MB for 10k files)
|
||||
- CPU usage spikes during indexing, low during search
|
||||
- Disk usage reasonable with LanceDB compression
|
||||
|
||||
---
|
||||
|
||||
## Final Assessment
|
||||
|
||||
### 🌟 **Strengths**
|
||||
1. **Educational Excellence** - Best-in-class for learning RAG concepts
|
||||
2. **Production Patterns** - Uses real-world engineering practices
|
||||
3. **Graceful Degradation** - System works even when components fail
|
||||
4. **Code Quality** - Clean, readable, well-documented codebase
|
||||
5. **Performance** - Fast search with reasonable resource usage
|
||||
|
||||
### ⚠️ **Areas for Production Readiness**
|
||||
1. **Scalability** - Needs multi-process architecture for large scale
|
||||
2. **Security** - Add authentication and input validation
|
||||
3. **Monitoring** - Structured logging and metrics export
|
||||
4. **Testing** - Expand unit test coverage and error path testing
|
||||
5. **Deployment** - Add containerization and service management
|
||||
|
||||
### 💡 **Recommendations**
|
||||
|
||||
**For Learning/Development Use**: **Highly Recommended**
|
||||
- Excellent starting point for understanding RAG systems
|
||||
- Easy to modify and experiment with
|
||||
- Good balance of features and complexity
|
||||
|
||||
**For Production Use**: **Proceed with Caution**
|
||||
- Great for small-medium teams and projects
|
||||
- Requires additional hardening for enterprise use
|
||||
- Consider as a foundation, not a complete solution
|
||||
|
||||
**Overall Verdict**: This is a **mature, well-engineered educational project** that demonstrates production-quality patterns while remaining accessible to developers learning RAG concepts. It successfully avoids the "too simple to be useful" and "too complex to understand" extremes that plague most RAG implementations.
|
||||
|
||||
The codebase shows clear evidence of experienced engineering with attention to error handling, performance, and maintainability. It would serve well as either a learning resource or the foundation for a production RAG system with additional enterprise features.
|
||||
|
||||
**Score: 8.5/10** - Excellent work that achieves its stated goals admirably.
|
||||
Loading…
x
Reference in New Issue
Block a user