README.md

RAG code sorting search

RAG First Principles

• The essence of rag is knowledge organization, which is convenient for accurate search again. If there is not enough context, it should be completed in the rag knowledge organization process, such as using tree_sitter in the code to complete the problem of insufficient keywords for insufficient types.

Init

• Setup python env

conda create -n rag-code-sorting-search python=3.11
conda activate rag-code-sorting-search
poetry install

• Ollama run embed model

ollama run nomic-embed-text

Usage

• index

$ poetry run python rag_search_code.py build /Users/clojure/Desktop/rag-code-sorting-search

$ ls .promptlycode/code_search_index/
code.index   metadata.pkl

• search

$ poetry run python  rag_search_code.py search /Users/clojure/Desktop/rag-code-sorting-search "search code"

Rank: 1
Score: 0.0037
File: /Users/clojure/Desktop/1112AIlearn-interview/rag-code-sorting-search/rag_search_code.py
Function: search_code (lines 118-161)

Code with context:
 115 |         'context_end_line': context_end
 116 |     }
 117 |
 118 | def search_code(query, top_k=5):
 119 |     """Search for similar code using the built index."""
 120 |     try:
 121 |         # Load index and metadata
 122 |         index = faiss.read_index('code_search_index/code.index')
 123 |         with open('code_search_index/metadata.pkl', 'rb') as f:
 159 |
...
 160 |     except FileNotFoundError:
 161 |         return "Index not found. Please build the index first using the build command."
 162 |
 163 | def main():
 164 |     parser = argparse.ArgumentParser(description='Code Search Tool')
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