Conceptual search

Models 17

• Gen 7 LFM
  LFM2.5 Embedding 350M

  By Liquid AI

  350M-parameter bidirectional multilingual bi-encoder that maps each document to a single dense vector for fast, cost-efficient multilingual and cross-lingual search. Supports 11 languages. Built from LFM2.5-350M-Base. Produces the smallest, cheapest index among LFM2.5 retrieval models. Available in GGUF format for CPU and edge deployment.

  🔍Information retrieval🔍Document search🔍Conceptual search
  NewText

  Released 23d ago
• Gen 7 LFM
  LFM2.5 ColBERT 350M

  By Liquid AI

  350M-parameter bidirectional multilingual retrieval model using per-token ColBERT-style late interaction (MaxSim) for accurate multilingual and cross-lingual search. Supports 11 languages. Built from LFM2.5-350M-Base with bidirectional attention and non-causal convolutions. Available in GGUF format for CPU and edge deployment.

  🔍Information retrieval🔍Document search🔍Conceptual search
  NewText

  Released 23d ago
• Gen 3
  jina embeddings v5 omni

  By Jina AI

  jina-embeddings-v5-omni is Jina AI’s multimodal embedding model suite for text, image, audio, video, and PDFs. It extends Jina’s v5 text embedding models into one shared semantic space while preserving identical text embeddings, so existing text indexes do not need rebuilding.

  🔍Multimodal search🔍Information retrieval📂Content categorization🔍Conceptual search
  NewMultimodal

  Released 1mo ago
• Gen 7
  Harrier OSS v1

  By Microsoft

  Harrier is Microsoft’s new open-source text embedding model series for retrieval, grounding, and agentic systems. It is designed for the “agentic web,” supports over 100 languages, uses a 32K context window, and was announced as ranking first on multilingual MTEB-v2 as of 06-04-2026.

  🔍Information retrieval📂Content categorization🌐Multilingual communication🔍Conceptual search
  Text

  Released 3mo ago
• Gen 3
  Chroma Context 1

  By Chroma

  Context-1 is Chroma’s 20B agentic search model trained as a self-editing search agent. It is designed to decompose complex queries, prune irrelevant context, and deliver high retrieval quality at lower latency and cost than much larger frontier models.

  🔍Information retrieval📂Content categorization🔍Conceptual search
  Text

  Released 3mo ago
• Gen 7
  Zembed 1

  By ZeroEntropy

  ZeroEntropy is a closed-source AI retrieval platform offering specialized rerankers, embeddings, and custom models for search, RAG, agentic coding, context compression, and production AI systems.

  🔍Information retrieval📂Content categorization📄Document analysis🔍Conceptual search
  Text

  Released 4mo ago
• Gen 7 pplx
  Pplx Embed

  By Perplexity

  pplx-embed is a family of text embedding models built for real-world, web-scale retrieval.

  🔍Information retrieval📂Content categorization🔍Conceptual search
  Text

  Released 4mo ago
• Gen 7 pplx
  pplx embed v1 4b

  By Perplexity

  A 4B-parameter text embedding model built on diffusion-pretrained Qwen3. Produces 2560-dimensional dense embeddings with INT8 and binary quantization, 32K context window, and Matryoshka Representation Learning (MRL) support. Designed for semantic search and web-scale retrieval. Multilingual. Available via API, SentenceTransformers, ONNX, and Text Embeddings Inference.

  🔗Embeds🔍Information retrieval🔍Conceptual search
  Text

  Released 5mo ago
• Gen 7 Gemini
  Gemini Embedding

  By Google

  Gemini Embedding 1 is Google’s text embedding model in the Gemini family that converts text into dense vectors for retrieval, clustering, ranking, and classification across 100+ languages

  🔍Information retrieval📂Content categorization🔍Conceptual search🔍Semantic content analysis
  Text

  Released 1y ago
• Gen 7 Rerank
  Rerank 2 lite

  By Voyage AI

  rerank-2-lite is a lightweight multilingual cross-encoder reranker from Voyage AI. It scores and reorders candidate documents by relevance to a query, optimized for low latency while maintaining strong retrieval quality. Supports an 8K-token combined context length per query-document pair and natively handles 31+ languages.

  🔍Information retrieval🔍Document search🔍Conceptual search
  Text

  Released 1y ago
• Gen 7 BGE
  BGE M3

  By Beijing Academy of Artificial Intelligence (BAAI)

  BGE M3 is BAAI’s multilingual retrieval embedding model built around three strengths: multi-functionality, multi-linguality, and multi-granularity. It supports dense retrieval, sparse retrieval, and multi-vector retrieval in one model, works across 100+ languages, and handles inputs up to 8192 tokens.

  🔍Information retrieval🌐Multilingual communication🔍Conceptual search
  Text

  Released 2y ago
• Gen 7 BGE
  BGE large v1.5

  By Beijing Academy of Artificial Intelligence (BAAI)

  BGE large v1.5 is BAAI’s top English BGE v1.5 embedding model. It is the large-scale Sep-2023 update to bge-large-en, with the docs highlighting more reasonable similarity distribution and better performance than the original version.

  🔍Information retrieval📂Content categorization🔍Conceptual search
  Text

  Released 2y ago
• Gen 7 BGE
  BGE Base v1.5

  By Beijing Academy of Artificial Intelligence (BAAI)

  BGE Base v1.5 is BAAI’s mid-size English BGE v1.5 embedding model. It belongs to the Sep-2023 v1.5 update that improved retrieval behavior and similarity distribution, while keeping a balanced base-scale footprint for practical deployment.

  🔍Information retrieval📂Content categorization🔍Conceptual search
  Text

  Released 2y ago
• Gen 7 BGE
  BGE small v1.5

  By Beijing Academy of Artificial Intelligence (BAAI)

  BGE small v1.5 is BAAI’s smallest English BGE v1.5 embedding model. It keeps the compact small-model footprint while belonging to the Sep-2023 v1.5 update, which was introduced to improve retrieval without instruction tuning and to make similarity scores more reasonable.

  🔍Information retrieval📂Content categorization🔍Conceptual search
  Text

  Released 2y ago
• Gen 7 BGE
  BGE base

  By Beijing Academy of Artificial Intelligence (BAAI)

  BGE base is BAAI’s mid-size English BGE v1 embedding model. It is an encoder-only BERT-based text embedder for mapping text into vectors, and the docs position it as a base-scale model with ability similar to the larger bge-large-en.

  🔍Information retrieval📂Content categorization🔍Conceptual search
  Text

  Released 2y ago
• Gen 7 BGE
  BGE small

  By Beijing Academy of Artificial Intelligence (BAAI)

  BGE small is BAAI’s smallest English BGE v1 embedding model. It is an encoder-only BERT-based text embedder designed to map text into vectors for retrieval, and is positioned as a compact model with competitive performance despite its small size.

  🔍Information retrieval📂Content categorization🔍Conceptual search
  Text

  Released 2y ago
• Gen 7 BGE
  BGE large

  By Beijing Academy of Artificial Intelligence (BAAI)

  BGE large is BAAI’s flagship English model in the original BGE v1 series. It is an encoder-only BERT-based embedding model for turning text into vectors, and the docs state it ranked first on MTEB at the time of release.

  🔍Information retrieval📂Content categorization🔍Conceptual search
  Text

  Released 2y ago