Adds support for Ollama embedding, enabling the use of Ollama as an embedding model for RAG.
This allows users to leverage Ollama's advanced embedding capabilities for better document understanding and retrieval.
Refactor embedding models and their handling to improve performance and simplify the process.
Add a new model selection mechanism, and enhance the UI for model selection, offering clearer and more user-friendly options for embedding models.
Refactor embeddings to use a common model for page assist and RAG, further improving performance and streamlining the workflow.
Add a provider selection dropdown to the OpenAI settings, enabling users to choose from pre-configured options like "Azure" or "Custom." This streamlines setup and allows for more flexibility in configuring OpenAI API endpoints. The dropdown pre-populates base URLs and names based on the selected provider.
The dropdown also automatically populates base URLs and names based on the selected provider, further simplifying the configuration process.
This commit introduces support for custom models in the message history generation process. Previously, the history would format messages using LangChain's standard message structure, which is not compatible with custom models. This change allows for correct history formatting regardless of the selected model type, enhancing compatibility and user experience.
