每个推荐都保留与其仓库、审计和安装路径的明确关联。
Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
AI powered open source recommender system engine supports classical/LLM rankers and multimodal content via embedding
A highly extensible Markdown editor. Version control, AI Copilot, mind map, documents encryption, code snippet running, integrated terminal, chart embedding, HTML applets, Reveal.js, plug-in, and macro replacement.
A query and indexing engine for Redis, providing secondary indexing, full-text search, vector similarity search and aggregations.
Find related notes and excerpts while writing. Your link building copilot displays relevant content in graph + list view. A local embedding model powers semantic search. Zero setup. No API key.
Semantic search over videos using Gemini Embedding 2 or Qwen3-VL.
MTEB: Massive Text Embedding Benchmark
Fast, Accurate, Lightweight Python library to make State of the Art Embedding
Fine-tune LLMs on your Mac with Apple Silicon. SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.
Unify Efficient Fine-tuning of RAG Retrieval, including Embedding, ColBERT, ReRanker.
KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs. It is used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It can effectively overcome the shortcomings of the traditional RAG vector similarity calculation model.
每个推荐都保留与其仓库、审计和安装路径的明确关联。
Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
AI powered open source recommender system engine supports classical/LLM rankers and multimodal content via embedding
A highly extensible Markdown editor. Version control, AI Copilot, mind map, documents encryption, code snippet running, integrated terminal, chart embedding, HTML applets, Reveal.js, plug-in, and macro replacement.
A query and indexing engine for Redis, providing secondary indexing, full-text search, vector similarity search and aggregations.
Find related notes and excerpts while writing. Your link building copilot displays relevant content in graph + list view. A local embedding model powers semantic search. Zero setup. No API key.
Semantic search over videos using Gemini Embedding 2 or Qwen3-VL.
MTEB: Massive Text Embedding Benchmark
Fast, Accurate, Lightweight Python library to make State of the Art Embedding
Fine-tune LLMs on your Mac with Apple Silicon. SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.
Unify Efficient Fine-tuning of RAG Retrieval, including Embedding, ColBERT, ReRanker.
KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs. It is used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It can effectively overcome the shortcomings of the traditional RAG vector similarity calculation model.
每个推荐都保留与其仓库、审计和安装路径的明确关联。
Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
AI powered open source recommender system engine supports classical/LLM rankers and multimodal content via embedding
A highly extensible Markdown editor. Version control, AI Copilot, mind map, documents encryption, code snippet running, integrated terminal, chart embedding, HTML applets, Reveal.js, plug-in, and macro replacement.
A query and indexing engine for Redis, providing secondary indexing, full-text search, vector similarity search and aggregations.
Find related notes and excerpts while writing. Your link building copilot displays relevant content in graph + list view. A local embedding model powers semantic search. Zero setup. No API key.
Semantic search over videos using Gemini Embedding 2 or Qwen3-VL.
MTEB: Massive Text Embedding Benchmark
Fast, Accurate, Lightweight Python library to make State of the Art Embedding
Fine-tune LLMs on your Mac with Apple Silicon. SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.
Unify Efficient Fine-tuning of RAG Retrieval, including Embedding, ColBERT, ReRanker.
KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs. It is used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It can effectively overcome the shortcomings of the traditional RAG vector similarity calculation model.
每个推荐都保留与其仓库、审计和安装路径的明确关联。
Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
AI powered open source recommender system engine supports classical/LLM rankers and multimodal content via embedding
A highly extensible Markdown editor. Version control, AI Copilot, mind map, documents encryption, code snippet running, integrated terminal, chart embedding, HTML applets, Reveal.js, plug-in, and macro replacement.
A query and indexing engine for Redis, providing secondary indexing, full-text search, vector similarity search and aggregations.
Find related notes and excerpts while writing. Your link building copilot displays relevant content in graph + list view. A local embedding model powers semantic search. Zero setup. No API key.
Semantic search over videos using Gemini Embedding 2 or Qwen3-VL.
MTEB: Massive Text Embedding Benchmark
Fast, Accurate, Lightweight Python library to make State of the Art Embedding
Fine-tune LLMs on your Mac with Apple Silicon. SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.
Unify Efficient Fine-tuning of RAG Retrieval, including Embedding, ColBERT, ReRanker.
KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs. It is used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It can effectively overcome the shortcomings of the traditional RAG vector similarity calculation model.