7). Open-Reasoner-Zero – an open-source large-scale minimalist RL framework to enhance reasoning. Achieves significant scalability requiring only 1/30th of the training steps of DeepSeek-R1-Zero-Qwen-32B to outperform it on GPQA Diamond.
OPEN SOURCE
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Complete RAG Guide: LangChain Implementations and Advanced Retrieval
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bRAG: Complete RAG Guide A comprehensive project showcasing RAG implementations with LangChain – from basics to advanced features like multi-query retrieval, ColBERT indexing, and RAG-Fusion. Check out this 1.7K+ starred guide https://
github.com/bRAGAI/bRAG-la
ngchain/
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Closed Source Licensing Challenges in AI-Driven Technology Success
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How closed source licensing makes it harder for technologies to succeed in an AI-driven world (that depends on open training data)
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LangChain Korean Tutorial: RAG, Agents and Core Technologies
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LangChain Korean Tutorial A comprehensive guide combining documentation, videos, and hands-on examples for mastering LangChain in Korean. Features practical implementations of RAG, agents, and core LangChain technologies. Check it out here https://
github.com/teddylee777/la
ngchain-kr
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RAG Tutorial: Enhancing LLMs with Retrieval-Augmented Generation
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RAG Tutorial Dive into Retrieval-Augmented Generation with this tutorial using Hugging Face and OpenAI. Build powerful RAG systems with LangChain's vector stores to enhance LLM capabilities and reduce hallucinations. Watch the tutorial: https://
youtube.com/watch?v=i0wcc8
qjfJw
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Building Local Agentic RAG Systems with LangChain Qdrant
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Local Agentic RAG Tutorial Learn to build a privacy-focused RAG system that runs 100% locally using LangChain, Qdrant, and Ollama. This tutorial shows you how to implement agentic RAG with local embeddings and multiple data source support. Watch the full tutorial:
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Self-Supervised Learning: Automatic vs Human-Labeled Similarity Graphs
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Correct.
You need a similarity-dissimilarity graph between samples.
If the graph is automatically constructed (e.g. through data augmentation, multiview, corruption/masking, etc), it's SSL.
If the graph uses human-provided labels, it's supervised. -
MCP Adapters: LangChain-Compatible Tool Wrapper
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MCP Adapters A lightweight wrapper that converts Model Context Protocol tools into LangChain-compatible tools, with multi-server support and React agent compatibility. Seamlessly integrates with LangChain Core and LangGraph. Check it out https://
github.com/langchain-ai/l
angchain-mcp-adapters
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Unstructured.io PDF Library Gains Popularity via LangChain Integration
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1) Unstructured(.)io @UnstructuredIO Unstructured started out as a PDF library, which gained popularity thanks to its early integration with LangChain.
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Llama 3.3 Now Supports 16K Context Windows for RAG
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16K Context Length for @AIatMeta
's Llama 3.3 Larger context lengths are now available on Llama 3.3 70B up to 16K context windows. This was a major request from the dev community to make these models more usable for RAG & Agentic workflows.