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Integrations that enable the retrieval of vector data for use in Retrieval-Augmented Generation workflows.
Distinct from Database-Integrated Workflows: Focuses on the specific RAG pattern of retrieval for LLM augmentation, rather than general data transformation workflows.
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LLamaSharp is a .NET LLM inference library and local runtime that enables the execution of large language models on CPU and GPU hardware. It serves as a multimodal AI library capable of processing both text and image inputs to generate analytical textual responses without relying on external APIs. The project distinguishes itself as a grammar-based text generator that enforces specific output formats, such as JSON, through constrained sampling pipelines. It also functions as a retrieval augmented generation framework integration, allowing the combination of local inference with external data
Integrates local inference with external data indexing to implement retrieval-augmented generation workflows.