BobAi
c201b3badd
Major fixes: - Fix model selection to prioritize qwen3:1.7b instead of qwen3:4b for testing - Correct context length from 80,000 to 32,000 tokens (proper Qwen3 limit) - Implement content-preserving safeguards instead of dropping responses - Fix all test imports from claude_rag to mini_rag module naming - Add virtual environment warnings to all test entry points - Fix TUI EOF crash handling with proper error handling - Remove warmup delays that were causing startup lag and unwanted model calls - Fix command mappings between bash wrapper and Python script - Update documentation to reflect qwen3:1.7b as primary recommendation - Improve TUI box alignment and formatting - Make language generic for any documents, not just codebases - Add proper folder names in user feedback instead of generic terms Technical improvements: - Unified model rankings across all components - Better error handling for missing dependencies - Comprehensive testing and validation of all fixes - All tests now pass and system is deployment-ready All major crashes and deployment issues resolved.
68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
#!/usr/bin/env python3
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"""
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Basic usage example for FSS-Mini-RAG.
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Shows how to index a project and search it programmatically.
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"""
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from pathlib import Path
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from mini_rag import ProjectIndexer, CodeSearcher, CodeEmbedder
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def main():
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# Example project path - change this to your project
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project_path = Path(".") # Current directory
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print("=== FSS-Mini-RAG Basic Usage Example ===")
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print(f"Project: {project_path}")
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# Initialize the embedding system
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print("\n1. Initializing embedding system...")
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embedder = CodeEmbedder()
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print(f" Using: {embedder.get_embedding_info()['method']}")
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# Initialize indexer and searcher
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indexer = ProjectIndexer(project_path, embedder)
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searcher = CodeSearcher(project_path, embedder)
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# Index the project
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print("\n2. Indexing project...")
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result = indexer.index_project()
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print(f" Files processed: {result.get('files_processed', 0)}")
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print(f" Chunks created: {result.get('chunks_created', 0)}")
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print(f" Time taken: {result.get('indexing_time', 0):.2f}s")
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# Get index statistics
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print("\n3. Index statistics:")
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stats = indexer.get_stats()
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print(f" Total files: {stats.get('total_files', 0)}")
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print(f" Total chunks: {stats.get('total_chunks', 0)}")
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print(f" Languages: {', '.join(stats.get('languages', []))}")
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# Example searches
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queries = [
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"chunker function",
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"embedding system",
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"search implementation",
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"file watcher",
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"error handling"
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]
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print("\n4. Example searches:")
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for query in queries:
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print(f"\n Query: '{query}'")
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results = searcher.search(query, top_k=3)
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if results:
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for i, result in enumerate(results, 1):
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print(f" {i}. {result.file_path.name} (score: {result.score:.3f})")
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print(f" Type: {result.chunk_type}")
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# Show first 60 characters of content
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content_preview = result.content.replace('\n', ' ')[:60]
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print(f" Preview: {content_preview}...")
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else:
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print(" No results found")
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print("\n=== Example Complete ===")
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if __name__ == "__main__":
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main() |