mxbai-embed-large-v1

Free.ai (self-hosted) · embeddings · ~100 tokens per call

mxbai-embed-large-v1 ni an Ìṣàmúlò-ètò tí mixedbread.ai kọ̀. Ó ní ìmọ́ nínú Semantic search, clustering, similarity.. Ààyè àìṣàfilọ́lẹ̀ fún ara rẹ̀ ní àwọn GPUs Free.ai — láàyè láti pàtó àwọn ààyè àìṣàfilọ́lẹ̀ rẹ̀ ní ọjọ́ (100 ààyè Àwọn àgbéwọlé). Ti fi silẹ labẹ Apache 2.0 — lilo iṣowo ti gba laaye lori Free.ai.

Lò nípa API

API REST tí a bá fẹ́ OpenAI. Ṣẹ̀dá bọ́tìnì náà láti pé ìṣàmúlò-ètò yìí nínú àwọn ìsàlẹ̀-ètò.

curl -X POST https://api.free.ai/v1/image/generate/ \
  -H "Authorization: Bearer sk-free-..." \
  -H "Content-Type: application/json" \
  -d '{"model":"mxbai-embed-large-v1","prompt":"your prompt here"}'

Àwọn Àkọsílẹ̀ Gba àwọn àyọkà API

Àwọn Àgbègbè Tí A Fẹ̀

mxbai-embed-large-v1 converts text into a dense vector (a list of floats) that captures meaning. Use it for semantic search, clustering, recommendation, retrieval-augmented generation (RAG), and any task where "is this text similar to that text" matters.

Àwọn ìṣàfarawégbègbè jẹ́ 384, 768, 1024, tàbí 1536 nípa ìpéwọ̀n. BGE-M3 fi 1024-dim pamọ́; OpenAI Ada fi 1536 pamọ́. Àwọn ìṣàfilọ́lẹ̀ API ní àwọn ìṣàfilọ́lẹ̀ láti jẹ́ pé vector DB rẹ̀ tí wọ́n yan ińdírẹ̀sì tó yẹ.

Àwọn àwọn àwòrán ìdáràn tí a tí kọ́ nínú àwọn ìṣàmúlò-ètò tuntun (gẹ́gẹ́ bí àwọn ìkúndùǹ tí o jú lọ ní Free.ai) ní a tí kọ́ nínú àwọn ìtàn 100+. Àwọn iṣẹ́ ìṣàfihàn àwọn ìtàn-ìdáràn - ìṣàfihàn nínú Àgbà, ìdáràn àwọn àkọsílẹ̀ nínú Espáñ.

512 si 8,192 token ní pàtó nípa ìṣàmúlò-ètò náà. Àwọn ìṣàfihàn tí o jú lọ ní pàtó - àwọn àkọsílẹ̀ ìgbà nínú àwọn àpáríǹtì láti fi pamọ́.

mxbai-embed-large-v1 n ṣiṣẹ lori awọn GPUs wa ati pe o wa laarin awọn irinṣẹ ti o dara julọ - nipa ~100 awọn tokeni fun ipe kan ti a ṣe lati pool ọfẹ ọjọgbọn rẹ. $5 = 200K awọn tokeni.

Yes - POST a list of strings to /v1/embeddings/ and mxbai-embed-large-v1 returns a list of vectors in the same order. Batch size up to 2,048 per request.

L2-àṣàfilọ́lẹ̀ láti inú ìpéwọ̀n - àkóónú àkóónú = àwọn

Àwọn mìíràn — Pinecone, Weaviate, Qdrant, Chroma, pgvector, FAISS, LanceDB. mxbai-embed-large-v1 yí àwọn ààyè JSON àìdálẹ̀ padà; DB kò rí àwọn módè́lì.

Ya — POST si /v1/embeddings/ pẹlú módè́lọ̀ọ̀kan="mxbai-embed-large-v1". Ìṣàfihàn àwọn àgbékalẹ̀ tí a gbọ́dọ̀ kọ̀ọ̀kan, láti jẹ́ pé àwọn láíbùrẹ́dì klìǹtì tí wà tí n ṣiṣẹ́ láìfilọ́lẹ̀. /api/ ní àgbèkalẹ̀ ìparí.

[Translation temporarily unavailable. Please try again.]

Sub-100ms fun àkọlé àkókó nípa ààyè-ara-ẹni-nípa, 100-500ms nípa àwọn. Agbègbè àwọn ayẹyẹ́ tí a fi pọ̀ jú lọ nípa àwọn - 1,000 chunks tí a tí pari nínú àwọn ìsàlẹ̀-ilà 2-10.

Ya — Free.ai gba ìlòòrò àwọn ìṣàfihàn àwọn ìṣàfihàn. Ṣẹ̀dá ìṣàfihàn ìṣàfihàn, àwọn ìṣàfihàn RAG, àwọn ìṣàfihàn àwọn ìṣàfihàn àwọn ìṣàfihàn àti àwọn ìṣàfihàn àwọn ìṣàfihàn àwọn ìṣàfihàn.

mxbai-embed-large-v1

Lò nípa API

Àwọn Àgbègbè Tí A Fẹ̀

Àwọn àṣẹ mxbai-embed-large-v1 wo ní?

Ìwọ̀n wo ní àwọn àgbékalẹ̀ mxbai-embed-large-v1 ní?

Àwọn àwọn ìṣàmúlò-ètò mxbai-embed-large-v1

Àwọn àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò àwọn ààtò

Ìgbà wo ní mxbai-embed-large-v1 náà ló?

Can I batch embed with mxbai-embed-large-v1?

Àwọn àwọn àgbékalẹ̀ àwọn bètórí

Àwọn DB vector wo ní n ṣiṣẹ́ láti mxbai-embed-large-v1?

Àwọn ààyè-iṣẹ́ fún mxbai-embed-large-v1

Kini nipa ìdàjọ́ àwọn àkọlé tí mo bá fi àwọn àkọlé àìdálẹ̀ pamọ́ láti mxbai-embed-large-v1?

Ìgbà wo ní mxbai-embed-large-v1 gba nígbà kan?

Ń lè ló àwọn ààtòjọ-ẹ̀yàn mxbai-embed-large-v1 nípa ìtàn àwọn iṣẹ́?

30,000 Free Tokens, Gbogbo ọjọ

Wá - 30K tí a fi pamọ́/ọ̀jọ́!

Kò lè fi pamọ́