RAG and knowledge retrieval
Find models, tools, and techniques for retrieval-augmented generation, vector search, knowledge bases, and document QA.
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RAG is not just a product name. Start from chunking, embeddings, retrieval, reranking, context assembly, or evaluation, then compose a path around data scale and latency requirements.
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RAG
Retrieval-Augmented Generation, target workload for document ingestion.
Modeldatagemma-rag-27b-it
Hugging Face metadata identifies datagemma-rag-27b-it as text-generation；transformers；text generation；text input/output. No readable Model Card summary was captured; suitability still needs verification.
MechanismHybrid RAG
KG-recorded mechanism related to Hybrid RAG; detailed English notes are not yet available.
Modeldatagemma-rag-it
KG-recorded model related to datagemma-rag-it; detailed English notes are not yet available.
MechanismSQLite
Lightweight disk-based database format used by Datasette.
AgentAlways-On Memory Agent
KG-recorded agent related to Always-On Memory Agent; detailed English notes are not yet available.
MechanismContextual Retrieval
KG-recorded mechanism related to Contextual Retrieval; detailed English notes are not yet available.
ModelLlama-3_3-Nemotron-Super-49B-v1_5
Llama-3.3-Nemotron-Super-49B-v1.5-NVFP4 is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and agentic tasks, such as RAG and tool calling. The model supports a context length of 128K tokens.
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