e5-large-v2 is a text embedding model, and the sources here do not show it being offered directly; the closest matches are a multilingual E5-large embedding model hosted on Replicate 111, and general embedding platforms that can serve or store its vectors such as Sentence Transformers 212 and Qdrant 414.
- 1
Replicate Beautyyuyanli E5 Large Multilingualreplicate.comverified and signed
Hosts a multilingual E5-large text embedding model that can be called through the Replicate API, as its multilingual-e5-large action describes it as a multi-language text embedding model 11.
for agents: beautyyuyanli/multilingual-e5-large
- 2
Sentence Transformerssbert.netverified and signed
A Python module for computing text embeddings with state-of-the-art embedding models, which can load E5-family models for embedding generation 212.
official:SDK · sentence-transformers+24 community tools built for it
for agents: embedding · XLNet · deep
- 3
Qdrantqdrant.techverified and signed
An open-source vector search engine that stores embeddings and searches points by vector similarity, with guidance for migrating between embedding models 414.
official:MCP server · mcp-server-qdrantSDK · qdrant-clientskill · qdrant-clients-sdkSDK · @qdrant/js-client-restskill · qdrant-search-quality+45 community tools built for it
for agents: qdrant-model-migration · search_points · qdrant-search-quality
- 4
Supabasesupabase.comverified and signed
A Postgres development platform with vector embedding support for storing and querying embeddings 3.
official:SDK · @supabase/supabase-jsCLI · supabasepackage · Supabaserepository · supabaseskill · supabase-postgres-best-practices+95 community tools built for it
for agents: e2e-studio-tests · supabase · typescript
Sources
- 1Replicate - Run AI with an APIreplicate.com · read 2026-10-29
- 2SentenceTransformers Documentation — Sentence Transformers documentationsbert.net · read 2026-10-29
- 3Supabase | The Postgres Development Platformsupabase.com · read 2026-10-29
- 4Qdrant - Vector Search Engineqdrant.tech · read 2026-10-29
- 11beautyyuyanli/multilingual-e5-large: declares "beautyyuyanli/multilingual-e5-large" (the record we read and signed)unfragile.ai · read 2026-10-24
- 12sentence-transformers: declares "embedding" (the record we read and signed)unfragile.ai · read 2026-10-24
- 14qdrant-model-migration: declares "qdrant-model-migration" (the record we read and signed)unfragile.ai · read 2026-10-24
For agents: the 10 actions behind this answer
beautyyuyanli/multilingual-e5-large · beautyyuyanli/multilingual-e5-large verified and signed
multilingual-e5-large: A multi-language text embedding model
saattrupdan/multilingual-e5-large-instruct · saattrupdan/multilingual-e5-large-instruct verified and signed
multilingual-e5-large-instruct: A multi-language text embedding model with custom query instructions.
beautyyuyanli/multilingual-e5-small · beautyyuyanli/multilingual-e5-small verified and signed
multilingual-e5-small: A multi-language text embedding model
adirik/multilingual-e5-large · adirik/multilingual-e5-large verified and signed
Multilingual E5-large language embedding model
embedding · sentence-transformers verified and signed
e2e-studio-tests · e2e-studio-tests verified and signed
Run e2e tests in the Studio app. Use when asked to run e2e tests, run studio tests, playwright tests, or test the feature.
qdrant-model-migration · qdrant-model-migration verified and signed
Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dim
llm · mistral-vibe verified and signed
create_embedding · OpenAI (Activepieces) verified and signed
Create Embedding — Turn text into a vector for semantic search or clustering.
model-recommendation · model-recommendation verified and signed
Analyze chatmode or prompt files and recommend optimal AI models based on task complexity, required capabilities, and cost-efficiency
Labels are the instruments' verdict and are never changed by ranking. Agents and robots get this same answer as JSON at /api/v1/find?q=…&answer=1 and through the MCP server.