To use OpenAI’s embeddings for semantic search, you first convert text into numerical vectors (embeddings) that capture semantic meaning, then compare these vectors to find similar content. OpenAI provides API endpoints for generating embeddings using models like text-embedding-3-small or text-embedding-3-large. These models map text to high-dimensional vectors (e.g., 1536 dimensions for text-embedding-3-small), where closer distances in the vector space indicate greater semantic similarity. The workflow involves three steps: generating embeddings for your dataset, storing them efficiently, and querying them using a search input’s embedding to find the closest matches.
To implement this, start by generating embeddings for your documents. For example, using Python and OpenAI’s library, you’d call openai.embeddings.create with the model name and input text. Each document (e.g., a product description or article) is converted into a vector and stored alongside its original text. Next, store these embeddings in a database optimized for vector search, such as Pinecone, Chroma, or FAISS. These tools index vectors to enable fast similarity comparisons. When a user submits a search query, generate its embedding using the same model, then use the database to find the nearest vectors—typically via cosine similarity, dot product, or Euclidean distance. For example, a query like “affordable wireless headphones” might match a product titled “Budget Bluetooth Earbuds” even if no keywords overlap.
A practical example: imagine building a FAQ search system. First, generate embeddings for all FAQ answers. Store them in a local FAISS index for low latency. When a user asks, "How do I reset my password?", convert the query to an embedding and search the index for the top three closest FAQ vectors. The results might include answers about “account recovery steps” or “troubleshooting login issues,” even if they don’t mention “password reset.” This approach ensures relevant results based on context, not just exact terms. Tools like LangChain can simplify this workflow by handling embedding generation and vector storage in a few lines of code. The key is ensuring consistent preprocessing (e.g., trimming text to the model’s token limit) and choosing a similarity metric aligned with your use case.