bamaer opened a new pull request, #8454:
URL: https://github.com/apache/hop/pull/8454

   … vectors
   
   Completes the retrieval chain: chunk, embed, store. Nothing in Hop could 
call an embedding model, so #8415 and #8422 had no middle step.
   
   AI provider gains model roles. One provider can now serve a chat model, an 
embedding model and a reranker from the same endpoint and credentials, with a 
models table in its editor. A transform resolves the role it needs rather than 
asking the user to pick one.
   
   - AiModelRole, AiProviderModel and AiProvider.resolveModelName(role)
   - AiEmbeddingFactory alongside AiChatFactory, for Ollama and OpenAI 
compatible providers
   - AiChatFactory honours a CHAT row, still falling back to modelName
   - Embed text, in plugins/tech/ai next to the metadata type it needs, the way 
pgvector, parquet and avro carry their transforms
   - a dependencies.xml so the langchain4j libraries are on the class loader 
before the first row rather than once another plugin happens to load
   - an AI transform category, which did not exist
   
   Fully backward compatible. modelName and temperature keep their meaning, a 
provider with no rows behaves exactly as before, and AiChatFactory was the only 
production consumer of either field.
   
   Adds an integration test project for the transforms that call an AI model. 
They share one Ollama container, and are disabled by default because the image 
and model pulls are too expensive for every run.
   
   **Please** add a meaningful description for your change here
   
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