Semantic Code Search Engine

Objectives

• Parse and chunk a codebase into searchable units (functions, classes, modules)
• Generate embeddings for each code chunk using an embedding model
• Store embeddings in a vector database for fast similarity search
• Support natural language queries with ranked results and code previews
• Display search results with context, file paths, and relevance scores

Hints

• Use tree-sitter or AST parsing to split code into meaningful chunks
• OpenAI embeddings or Cohere for generating code embeddings
• Pinecone, Weaviate, or pgvector for the vector storage layer

Resources

• platform.openai.com/docs/guides/embeddings

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