Results for “embeddings”
27 skills
orchestra-research Sentence Transformers
Generate high-quality sentence and text embeddings for semantic similarity, clustering, and retrieval using 5000+ pre-trained models. Supports multilingual and domain-specific embeddings for RAG and semantic search.
10.4k · bundle
antigravity Vexor
Enables semantic file search using vector embeddings from the command line, integrated with Claude/Codex.
42.4k
lord1egypt Chroma
Store and query embeddings with metadata, vector and full-text search, and filtering. Integrates with LangChain and LlamaIndex for RAG and semantic search applications.
2
orchestra-research Chroma
Store and query embeddings with metadata filtering, vector search, and full-text search using an open-source database that scales from notebooks to production.
10.4k · bundle
phoroth LLM Ops
Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.
3
jrennie99-glitch Embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
0
More results
mariadb-corporation Mariadb Vector Functions
Reference for MariaDB's vector functions and VECTOR data type, covering VEC_Distance, VEC_Distance_Euclidean, VEC_Distance_Cosine, VEC_FromText, VEC_ToText, and MHNSW vector index usage for SQL queries over embeddings.
0
qhjqhj00 Tensorboard
Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard.
3 · bundle
majiayu000 RAG
Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.
567 · bundle
oyi77 Zvec
Provides guidance on using the ZVec in-process vector database for efficient similarity search and embedding storage in agent memory systems.
10
joshuashepherd Book Chunk
Chunks a book into canonical retrieval units with heading-aware structure splitting, recursive token targets, and contextual prefixes for downstream RAG ingestion.
1
nvidia Tao Mine Aoi Images
Embeds target and source image parquets, then mines nearest-neighbour source images for augmentation in VCN AOI workflows.
2.2k · bundle
github Qdrant Model Migration
Guides embedding model migration in Qdrant without downtime, covering alias swap, side-by-side, and hybrid search strategies.
36.2k
lord1egypt Faiss
Enables fast similarity search and clustering of dense vectors using FAISS, covering index types, GPU acceleration, and integrations with LangChain and LlamaIndex.
2
nvidia Nemotron Retrieval Recipes
Plan, debug, tune, evaluate, export, or deploy public Nemotron embedding and reranking retrieval recipes using the current checkout.
2.2k · bundle
joshuashepherd Book Ingest
Upserts a validated MDX book corpus into Supabase via Drizzle, hydrating books, chapters, sections, and chunks tables while preserving stable bookmark anchors and only re-embedding changed content.
1
k-dense-ai Umap Learn
Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.
30.2k · bundle
qhjqhj00 Umap Learn
Reduce high-dimensional data with UMAP for visualization, clustering preprocessing, and supervised or semi-supervised learning, including parameter tuning guidance.
3 · bundle
oyi77 RAG Builder
Designs and implements RAG pipelines, covering document chunking, embedding strategies, hybrid search, answer synthesis with source attribution, and evaluation using RAGAS metrics.
10
lingxling Geniml
Trains machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.
253 · bundle
antigravity LLM Ops
Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.
42.4k
k-dense-ai Geniml
Train unsupervised machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.
30.2k · bundle
jeffallan RAG Architect
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.
10.4k · bundle
antigravity Mesh Memory
Provides persistent, self-hosted semantic memory for AI agents via MCP, storing worklogs, decisions, and notes in PostgreSQL with pgvector for meaning-based retrieval across sessions.
42.4k
mariadb-corporation Mariadb Vector
Provides best practices for using MariaDB's built-in vector support for AI workloads, including SQL syntax for vector columns, indexes, distance functions, and RAG patterns.
0
oyi77 Vector DB Ops
Manage vector database operations across Pinecone, Weaviate, Qdrant, and ChromaDB, including embedding generation, index creation, metadata filtering, hybrid search, and production deployment for RAG and similarity search.
10
k-dense-ai Cellxgene Census
Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data, enabling efficient access to cell metadata, gene expression slices, summary counts, and embeddings without downloading whole datasets.
30.2k · bundle