Yes, text-embedding-ada-002 can effectively power semantic search systems by enabling meaning-based retrieval instead of keyword matching. In a semantic search setup, both documents and user queries are converted into embeddings, and search results are determined by vector similarity rather than shared words. This allows the system to return relevant results even when the query uses different phrasing than the stored content.
In practice, a semantic search pipeline typically involves splitting documents into manageable chunks, generating embeddings for each chunk, and storing them for retrieval. When a user submits a query, the query is embedded using the same model and compared against stored vectors. This approach works well for documentation search, knowledge bases, internal tools, and content discovery features where users may not know the exact terms used in the source material.
To run this at scale, developers usually rely on a vector database such as Milvus or Zilliz Cloud. These systems handle fast nearest-neighbor search and support filtering and ranking. text-embedding-ada-002 provides the semantic representation, while the vector database ensures that search remains fast and reliable as the dataset grows. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002