mxbai-embed-large-v1

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

mxbai-embed-large-v1 ni an Ìjádé àwọn ààtòjútó ti a ṣe nipasẹ mixedbread.ai. O ti wa ni 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.

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 ìṣàmúlò-ètò tí a lò ní 384, 768, 1024, tàbí 1536 nípa ìpéwọ̀n. BGE-M3 ń fi 1024-dim pamọ́; OpenAI Ada ń fi 1536 pamọ́. Àwọn ìṣàmúlò-ètò API ń fi ìṣàmúlò-ètò pamọ́ láti jẹ́ pé àwọn DB vector rẹ̀ ń yan àwọn ìpéwọ̀n àìdálẹ̀ tó yẹ.

Àwọn àwòrán ìdáràn tí a tí fi pamọ́ (gẹ́gẹ́ bí àwọn àwọn ìkúndùǹ tí o jú lọ ni Free.ai) tí a tí kọ́ nípa àwọn àwọn ìtàn 100+. Àwọn iṣẹ́ ìṣàfilọ́lẹ̀ ìtàn-ìtàn - ìṣàfihàn nínú Ingẹ̀ẹ̀lì, ìdáràn àwọn àkọsílẹ̀ nínú Espáñ.

512 si 8,192 tokens dáju àwọn móòdù. Àwọn ìṣàmúlò-ètò tí o jú jú ní àwọn àgbèwọlé - àwọn àkọsílẹ̀ ìjánu-ìjánú nínú àwọn àpéjúwe tí o jú jú ìṣàfarawé.

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

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-normalized nípa ìpéwọ̀n - cosine similarity = dot product. Pass `normalize=false` tí o bà fẹ́ àwọn bètórí àìdálẹ̀ fún àwọn ààyè-iṣẹ́ àwọn ààyè-iṣẹ́ àìdálẹ̀.

Àwọn - Pinecone, Weaviate, Qdrant, Chroma, pgvector, FAISS, LanceDB. mxbai-embed-large-v1 gba àwọn ààyè JSON àìdálẹ̀ padà; DB kò rí àwọn ìṣàmúlò-ètò.

Ya - POST sí /v1/embeddings/ láti inú àwòrán "mxbai-embed-large-v1". Àwọn àgbékalẹ̀ àwọn àgbékalẹ̀ OpenAI, láti jẹ́ pé àwọn láàbùrẹ̀tì klìentì tí wa ní àwọn iṣẹ́ àìdá. /api/ ní àwọn àgbèkalẹ̀ ìṣàfarawé.

Self-hosted models keep your text on our GPUs and discard it after the call returns. Premium pass through with a DPA. We do not train on your inputs.

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 lórí ìlòòdù àwọn ìṣàmúlò-ètò. Ṣẹ̀dà ìṣàfihàn ìṣàfarawékọ́, àwọn pánẹ́ẹ̀lì RAG, àwọn ìṣàmúlò-ètò ìṣàmúlò-ètò àti àwọn à

mxbai-embed-large-v1

Àwọn Àgbègbè Tí A Fẹ̀

Kini mxbai-embed-large-v1 n ṣe?

Ìwọ̀n wo nínú àwọn ìṣàfarawé àwọn ààyè-iṣẹ́ mxbai-embed-large-v1?

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

What is the max input length for mxbai-embed-large-v1?

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

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

Does mxbai-embed-large-v1 normalize the vectors?

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

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

Kini ìròyìn tí mo bá fi àkọlé àìdálẹ̀ pamọ́ láti inú mxbai-embed-large-v1?

Ìgbà wo ní mxbai-embed-large-v1 náà gba nínú awáròyìn kan?

Ń lè ló àwọn àkóónú mxbai-embed-large-v1 nípa ìṣàfihàn àìṣàmúlò-ètò?

30,000 Free Tokens, Gbogbo ọjọ

Wá: 30K tí a fi pamọ́/ọ̀jọ́!

Kò lè fi pamọ́