To implement semantic search for code repositories, you need to focus on understanding the meaning behind code snippets and queries rather than relying solely on keyword matching. Start by converting code into numerical representations (embeddings) using models trained to capture semantic relationships. Store these embeddings in a vector database optimized for similarity searches. When a user submits a query, convert it into an embedding and search the database for the closest matches. This approach allows finding code that shares functional similarities, even if it uses different variable names or syntax.
First, choose a model to generate code embeddings. Models like CodeBERT, UniXcoder, or OpenAI’s text-embedding-3-small (fine-tuned on code) are designed to handle programming languages. For example, using the sentence-transformers library, you can embed a Python function with model.encode(code_snippet). Preprocess code by splitting it into logical units (functions, classes, or blocks) and stripping unnecessary comments or whitespace. Store these embeddings in a vector database like FAISS, Milvus, or Pinecone. These databases index vectors for fast nearest-neighbor searches. For instance, with FAISS, you can build an index using faiss.IndexFlatL2(embedding_dim) and add embeddings with index.add(code_embeddings).
When handling queries, convert natural language questions like “How to read a CSV file in Python?” into embeddings using the same model. Search the database with index.search(query_embedding, k=5) to retrieve the top-k similar code snippets. To improve accuracy, consider context: a query about “database connections” might relate to code snippets containing psycopg2.connect() in Python or mongoose.connect() in JavaScript, even if the keywords don’t match. Experiment with hybrid approaches—combine semantic results with keyword-based filters (e.g., file type or function names) using libraries like Elasticsearch. For example, filter results to only show Python files after the semantic search step.
Finally, test and iterate. Evaluate results by checking if queries like “sort a list of dictionaries by a key” return relevant sorted() examples in Python or .sort() in JavaScript. Use metrics like recall@k (how often the correct result is in the top-k matches) and adjust the model or preprocessing as needed. For large repositories, optimize performance by batching embeddings and using distributed databases like Weaviate. Keep the system maintainable by versioning embeddings when code changes and retraining models periodically. This approach balances accuracy with scalability, letting developers find code by intent rather than memorized syntax.