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Chain of Agents (COA): Large Language Models Collaborating on Long-Context Tasks
Discover how Chain-of-Agents enhances Large Language Models by effectively managing context injection, improving response quality while addressing token limitations.
Community
Cosmos World Foundation Model Platform for Physical AI
NVIDIA’s Cosmos platform pioneers GenAI for physical applications by enabling safe digital twin training to overcome data and safety challenges in physical AI modeling.
Community
OpenAI o1: What Developers Need to Know
In this article, we will talk about the o1 series from a developer's perspective, exploring how these models can be implemented for sophisticated use cases.
Community
Building RAG with Dify and Milvus
Learn how to build Retrieval Augmented Generation (RAG) applications using Dify for orchestration and Milvus for vector storage in this step-by-step guide.
Community
Why DeepSeek V3 is Taking the AI World by Storm: A Developer’s Perspective
Explore how DeepSeek V3 achieves GPT-4 level performance at fraction of the cost. Learn about MLA, MoE, and MTP innovations driving this open-source breakthrough.
Community
3 Key Patterns to Building Multimodal RAG: A Comprehensive Guide
These multimodal RAG patterns include grounding all modalities into a primary modality, embedding them into a unified vector space, or employing hybrid retrieval with raw data access.
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Semantic Search vs. Lexical Search vs. Full-text Search
Lexical search offers exact term matching; full-text search allows for fuzzy matching; semantic search understands context and intent.
Paper Reading
Enhancing RAG with RA-DIT: A Fine-Tuning Approach to Minimize LLM Hallucinations
RA-DIT, or Retrieval-Augmented Dual Instruction Tuning, is a method for fine-tuning both the LLM and the retriever in a RAG setup to enhance overall response quality.
Community
Matryoshka Representation Learning Explained: The Method Behind OpenAI’s Efficient Text Embeddings
Matryoshka Representation Learning (MRL) is a method for generating hierarchical, nested embeddings that capture information at multiple levels of abstraction.