text-embedding-ada-002 integrates with vector databases by providing the dense vectors that the database indexes and searches. The integration pattern is simple: you generate an embedding for each piece of text you want to retrieve later, insert that vector into your vector database along with an ID and optional metadata, then embed user queries at runtime and run a similarity search to retrieve the closest vectors. The embedding model and the vector database play different roles: the model turns text into vectors, and the database makes vector similarity search fast and scalable.
In practice, developers usually implement this in two phases. First is ingestion: split documents into chunks (often a few hundred tokens per chunk, tuned to your content), call the embeddings API with text-embedding-ada-002, then write records like {id, vector, raw_text, metadata}. Metadata might include fields like doc_id, source, language, timestamp, or access_role. Second is querying: when a user asks a question, you embed the query text with the same model, then ask the vector database for the top-k nearest neighbors. You can apply metadata filters (for example, only search documents the user is allowed to see) and then return the matched chunks to your application.
A vector database such as Milvus or Zilliz Cloud is designed for exactly this workflow. You define a collection schema that includes a vector field (1536 dimensions for text-embedding-ada-002) and any scalar fields you need for filtering. Then you choose an index strategy appropriate to your scale and latency goals, and you query using a similarity metric like cosine or inner product. The key integration detail is consistency: whatever chunking and normalization you apply at ingestion time, you should apply the same style of processing to queries so the embeddings “live” in comparable space. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002