Aspose.LLM for .NET

A library for integrating large language models (LLMs) into your .NET applications.

Publié par Aspose
Distribué par ComponentSource depuis 2006

Prix à partir de : $ 391.02 Version : V26.3 NOUVEAU Mise à jour : Mar 26, 2026

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À propos de Aspose.LLM for .NET

A library for integrating large language models (LLMs) into your .NET applications.

Aspose.LLM for .NET is a library for integrating large language models (LLMs) into your .NET applications. You run models on your own infrastructure (on-premise or controlled cloud) and interact with them through a simple API: create an instance from a preset, start chat sessions, send messages, and optionally save or load conversation state.

Key features - Text and code generation, natural language understanding, chat sessions, and flexible configuration through presets.

  • Text generation and completion - Generate and complete text for a wide range of use cases, from content creation to summarization and expansion.
  • Code generation and analysis - Use LLMs for code generation, refactoring suggestions, and code analysis directly from your .NET applications.
  • Natural language understanding - Integrate natural language understanding into your apps for classification, extraction, and question answering.
  • Chat sessions and multimodality - Run stateful chat sessions and optionally combine text with images for multimodal inference.
  • Easy to use - Install the package from NuGet, create the API with a config, load a model, and start a chat session — a few lines of code are enough to get a response from the LLM. Aspose.LLM handles model loading, session state, and inference so you can focus on your application logic. Use default presets or customize model, context, and sampler parameters to fit your use case.- Run stateful chat sessions and optionally combine text with images for multimodal inference.
  • Multiple languages and use cases - Use Aspose.LLM for text and code generation, summarization, and natural language understanding in many languages and scenarios. Combine with presets and parameters to adapt the model to your application.