Text Embedding
A text embedding is a numerical vector representation of a sentence or passage that captures semantic meaning, produced by an embedding model and used to power similarity search in retrieval-augmented generation.
A text embedding converts a sentence or passage into a vector of real numbers positioned so that passages with similar meaning sit close together in that vector space, which is what lets a system retrieve semantically related content instead of only exact keyword matches. watsonx.ai exposes embeddings through its /ml/v1/text/embeddings REST endpoint and a Python Embeddings class, accepting up to 1,000 lines of text per request. IBM's own encoder models include granite-embedding-278m-multilingual (768 dimensions, 512 max input tokens) and the older slate-125m-english-rtrvr-v2 and slate-30m-english-rtrvr-v2, both scheduled for withdrawal on August 8, 2026; Microsoft's multilingual-e5-large is also supported. For an IBM i shop, watsonx.ai is the source of these vectors, but as of September 2026 Db2 for i has no native vector store to hold them, so embeddings typically land in OpenSearch running on Power instead.
Related Terms
Vector Store
A vector store holds embeddings and finds the ones closest to a query. It is the retrieval half of RAG, and Db2 for i has no vector store as of 2026.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation hands a language model passages pulled from your own data, so it answers from your content. On IBM i, retrieval is the hard half.
watsonx.ai
The studio component of watsonx for building, tuning, and deploying foundation models and generative AI applications, including retrieval-augmented generation (RAG) against enterprise data.
OpenSearch
OpenSearch is the open-source hybrid search engine IBM has positioned as the vector, text and structured search layer for RAG on Power, since Db2 for i has none.
Large Language Model (LLM)
A machine learning model trained on large volumes of text, used as the underlying technology behind generative AI tools including IBM Bob and general-purpose assistants like ChatGPT and Claude.
Foundation Model
A large, general-purpose AI model trained on broad data and then adapted, through prompting, tuning, or fine-tuning, to specific tasks such as RPG code generation.