To use OpenAI’s embeddings for semantic search, you first need to generate embeddings for the text data you want to search through. An embedding is essentially a numerical representation of text, where similar texts are mapped to nearby points in a high-dimensional space. You can obtain embeddings by sending your text data to OpenAI’s API, which will return a vector for each piece of text. For instance, if you have a set of documents, you can create embeddings for each document's content, such as the title and body of articles or descriptions in a database.
Once you have the embeddings for your documents, the next step is to store them in a way that allows for efficient searching. You can use databases that support vector search, such as Pinecone or Weaviate, which are optimized for handling and querying embeddings. When a user performs a search query, you also generate an embedding for the query text using the same API. This ensures the query is represented in the same format as your document embeddings. By comparing the query's embedding with the embeddings of your documents, you can determine which documents are most semantically similar to the query.
Finally, you can retrieve and rank the documents based on their similarity scores. Many vector databases return a similarity measure, such as cosine similarity, allowing you to see how closely the documents match the user’s query. For example, if someone searches for "best coffee makers," the semantic search will find documents that are not only similar in terms of keywords but also capture the context and meaning behind the query. This leads to more relevant results, improving user experience significantly compared to traditional keyword-based searches.