Amazon Titan.

Different models under one family name

Amazon Titan is a family of AWS models available through Amazon Bedrock. The current overview lists text embeddings, multimodal embeddings and image generation. These do not form a single chat with identical capabilities. Choose a model for the input and required result, then check its ID and availability in your environment.

An embedding model turns text into numbers representing semantic features. It does not answer the user or search a database itself. Other application components compare vectors and retrieve relevant passages. This distinction matters when planning a search across internal instructions or reference material.

A small knowledge base search example

For a practice check, take a few of your own service descriptions and questions in one language. Create embeddings for passages and questions with the same model, then compare them in the application. Keep each numerical representation linked to its source text; otherwise you cannot check whether a close match makes sense.

Try a question containing a negation and two similar descriptions with different conditions. The nearest vector is not necessarily the correct business answer. A complete search needs passage selection, access checks and your own quality assessment. The documentation also warns about cross language query limits: similar topics in different languages may not match equally well.

What to store with the index

Keep the model ID, vector dimensions and text splitting rule alongside the vectors. If you change one of these settings, old and new entries may no longer be comparable. Decide how to update the index before loading the complete document set.

Also keep each passage’s source and revision. Otherwise a semantically relevant match may return a condition withdrawn from an older instruction. Relevance over time and user permissions belong to the search application; the embedding model does not decide which document a person may see.

Access and technical limits

Bedrock offers programmatic model calls, but the final application response is not the only operation that can cost money. Document indexing, repeated calls and model choice also affect the budget. Before processing a large archive, determine which passages need indexing and how to update them when sources change.

Models differ in input limits, output dimensions and invocation methods. Do not mix vectors from different models or dimensions indiscriminately in one database. Image generation requires different input and evaluation. A search example does not establish image quality, and an image example does not establish search reliability.

Suitable tasks

• Build semantic search over your texts
• Generate embeddings for data processing
• Connect image models via Bedrock