From a0dbfa691221a5d340ec89bba00c2566e6b5bbfd Mon Sep 17 00:00:00 2001 From: Michael Date: Fri, 25 Oct 2024 15:36:54 -0400 Subject: [PATCH 1/2] Update cohere-openapi.yaml Signed-off-by: Michael --- cohere-openapi.yaml | 2574 +++++++++++++++++++++---------------------- 1 file changed, 1287 insertions(+), 1287 deletions(-) diff --git a/cohere-openapi.yaml b/cohere-openapi.yaml index c42b3f43..13686555 100644 --- a/cohere-openapi.yaml +++ b/cohere-openapi.yaml @@ -8071,53 +8071,27 @@ paths: x-fern-examples: - code-samples: - sdk: go - name: Images - code: > + name: Texts + code: | package main - import ( "context" - "encoding/base64" - "fmt" - "io" "log" - "net/http" cohere "github.com/cohere-ai/cohere-go/v2" client "github.com/cohere-ai/cohere-go/v2/client" ) - func main() { - // Fetch the image - resp, err := http.Get("https://cohere.com/favicon-32x32.png") - if err != nil { - log.Println("Error fetching the image:", err) - return - } - defer resp.Body.Close() - - // Read the image content - buffer, err := io.ReadAll(resp.Body) - if err != nil { - log.Println("Error reading the image content:", err) - return - } - - stringifiedBuffer := base64.StdEncoding.EncodeToString(buffer) - contentType := resp.Header.Get("Content-Type") - imageBase64 := fmt.Sprintf("data:%s;base64,%s", contentType, stringifiedBuffer) - co := client.NewClient(client.WithToken("<>")) - embed, err := co.Embed( + resp, err := co.Embed( context.TODO(), &cohere.EmbedRequest{ - Images: []string{imageBase64}, - Model: cohere.String("embed-english-v3.0"), - InputType: cohere.EmbedInputTypeImage.Ptr(), - EmbeddingTypes: []cohere.EmbeddingType{cohere.EmbeddingTypeFloat}, + Texts: []string{"hello", "goodbye"}, + Model: cohere.String("embed-english-v3.0"), + InputType: cohere.EmbedInputTypeSearchDocument.Ptr(), }, ) @@ -8125,69 +8099,61 @@ paths: log.Fatal(err) } - log.Printf("%+v", embed) + log.Printf("%+v", resp) } - sdk: typescript - name: Images - code: > + name: Texts + code: | const { CohereClient } = require('cohere-ai'); - const cohere = new CohereClient({ token: '<>', }); - (async () => { - const image = await fetch('https://cohere.com/favicon-32x32.png'); - const buffer = await image.arrayBuffer(); - const stringifiedBuffer = Buffer.from(buffer).toString('base64'); - const contentType = image.headers.get('content-type'); - const imageBase64 = `data:${contentType};base64,${stringifiedBuffer}`; - const embed = await cohere.embed({ + texts: ['hello', 'goodbye'], model: 'embed-english-v3.0', - inputType: 'image', + inputType: 'classification', embeddingTypes: ['float'], - images: [imageBase64], }); console.log(embed); })(); - sdk: python - name: Images + name: Texts code: > import cohere - import requests - - import base64 - co = cohere.Client("<>") - image = requests.get("https://cohere.com/favicon-32x32.png") + response = co.embed( + texts=["hello", "goodbye"], model="embed-english-v3.0", input_type="classification" + ) - stringified_buffer = - base64.b64encode(image.content).decode('utf-8') + print(response) + - sdk: python + name: Texts (async) + code: > + import cohere - content_type = image.headers['Content-Type'] + import asyncio - image_base64 = - f"data:{content_type};base64,{stringified_buffer}" + co = cohere.AsyncClient("<>") - response = co.embed( - model="embed-english-v3.0", - input_type="image", - embedding_types=["float"], - images=[image_base64] - ) - print(response) + async def main(): + response = await co.embed( + texts=["hello", "goodbye"], model="embed-english-v3.0", input_type="classification" + ) + print(response) + + asyncio.run(main()) - sdk: java - name: Images + name: Texts code: > package embedpost; /* (C)2024 */ @@ -8200,55 +8166,27 @@ paths: import com.cohere.api.types.EmbedResponse; - import com.cohere.api.types.EmbeddingType; - - import java.io.InputStream; - - import java.net.HttpURLConnection; - - import java.net.URI; - - import java.net.URL; - - import java.util.Base64; - import java.util.List; - public class EmbedImagePost { + public class EmbedPost { public static void main(String[] args) { Cohere cohere = Cohere.builder().token("<>").clientName("snippet").build(); - URL url = - URI.toUrl( - "https://cohere.com/favicon-32x32.png"); - HttpURLConnection connection = (HttpURLConnection) url.openConnection(); - connection.connect(); - - InputStream inputStream = connection.getInputStream(); - byte[] buffer = inputStream.readAllBytes(); - inputStream.close(); - - String imageBase64 = - String.format( - "data:%s;base64,%s", - connection.getHeaderField("Content-Type"), Base64.getEncoder().encodeToString(buffer)); - EmbedResponse response = cohere.embed( EmbedRequest.builder() - .images(List.of(imageBase64)) + .texts(List.of("hello", "goodbye")) .model("embed-english-v3.0") - .inputType(EmbedInputType.IMAGE) - .embeddingTypes(List.of(EmbeddingType.FLOAT)) + .inputType(EmbedInputType.CLASSIFICATION) .build()); System.out.println(response); } } - sdk: curl - name: Images - code: >- + name: Texts + code: |- curl --request POST \ --url https://api.cohere.com/v1/embed \ --header 'accept: application/json' \ @@ -8256,1198 +8194,21 @@ paths: --header "Authorization: bearer $CO_API_KEY" \ --data '{ "model": "embed-english-v3.0", - "input_type": "image", - "embedding_types": ["float"], - "images": ["data:image/jpeg;base64,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"] + "texts": ["hello", "goodbye"], + "input_type": "classification" }' request: + texts: + - hello + - goodbye model: embed-english-v3.0 - input_type: image - embedding_types: - - float - images: - - data:image/jpeg;base64,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 + input_type: classification response: body: - id: 5807ee2e-0cda-445a-9ec8-864c60a06606 - texts: [] - images: - - width: 400 - height: 400 - format: jpeg - bit_depth: 24 - embeddings: - float: - - - -0.007247925 - - -0.041229248 - - -0.023223877 - - -0.08392334 - - -0.03378296 - - -0.008308411 - - -0.049926758 - - 0.041625977 - - 0.043151855 - - 0.03652954 - - -0.05154419 - - 0.011787415 - - -0.02784729 - - -0.024230957 - - -0.018295288 - - -0.0440979 - - 0.032928467 - - -0.015007019 - - 0.009315491 - - -0.028213501 - - -0.00022602081 - - -0.0074157715 - - -0.000975132 - - 0.05783081 - - 0.029510498 - - 0.024871826 - - -0.009422302 - - -0.028701782 - - -0.021118164 - - -0.019088745 - - -0.0038433075 - - 0.04083252 - - 0.03024292 - - -0.010154724 - - -0.008163452 - - 0.04269409 - - 0.017471313 - - -0.010017395 - - 0.006629944 - - 0.011047363 - - 0.013542175 - - -0.007926941 - - -0.024932861 - - -0.05960083 - - -0.05404663 - - 0.037384033 - - -0.049621582 - - -0.024002075 - - 0.040039062 - - 0.02645874 - - 0.010261536 - - -0.028244019 - - 0.016479492 - - 0.014266968 - - -0.043823242 - - -0.022262573 - - -0.0057678223 - - -0.04800415 - - 0.041015625 - - 0.01537323 - - -0.021530151 - - -0.014663696 - - 0.051849365 - - -0.025558472 - - 0.045776367 - - -0.025665283 - - -0.005821228 - - 0.02973938 - - 0.053131104 - - 0.020706177 - - -0.004600525 - - 0.0046920776 - - 0.02558899 - - -0.05319214 - - -0.058013916 - - 0.080444336 - - -0.00068187714 - - 0.031311035 - - 0.032440186 - - -0.051086426 - - -0.003534317 - - 0.046325684 - - -0.032440186 - - -0.03894043 - - -0.0071907043 - - -0.004627228 - - -0.01826477 - - -0.027755737 - - 0.040802002 - - 0.019363403 - - -0.009727478 - - 0.0064468384 - - 0.056488037 - - 0.018585205 - - -0.017974854 - - -0.08514404 - - 5.0604343e-5 - - -0.014839172 - - 0.01586914 - - 0.00017666817 - - 0.02267456 - - -0.05105591 - - 0.007785797 - - -0.02684021 - - 0.0064849854 - - 0.014411926 - - 0.0013427734 - - -0.012611389 - - 0.043701172 - - 0.012290955 - - -0.030731201 - - 0.034729004 - - 0.015289307 - - -0.037475586 - - -0.030838013 - - 0.010009766 - - -0.028244019 - - 0.051635742 - - 0.01725769 - - 0.013977051 - - 0.008102417 - - 0.028121948 - - 0.02079773 - - 0.0027256012 - - 0.009185791 - - 0.0016012192 - - -0.038116455 - - -0.008331299 - - -0.028076172 - - 0.018463135 - - -0.02154541 - - 0.021240234 - - 0.023376465 - - 0.02961731 - - -0.028305054 - - -0.023101807 - - -0.010681152 - - -0.0072021484 - - -0.04321289 - - 0.0058517456 - - 0.030792236 - - -0.021102905 - - 0.050933838 - - 0.0060157776 - - 0.0128479 - - 0.025146484 - - -0.006099701 - - 0.023345947 - - 0.023971558 - - 0.015510559 - - -0.009895325 - - -0.04071045 - - 0.049835205 - - 0.0053100586 - - -0.028930664 - - 0.017578125 - - -0.0048217773 - - -0.0042762756 - - -0.034240723 - - -0.03253174 - - 0.035827637 - - 0.01574707 - - 0.034851074 - - 0.070129395 - - 0.011749268 - - -0.009223938 - - 0.02470398 - - -0.005115509 - - 0.016723633 - - 0.04937744 - - -0.032928467 - - 0.031280518 - - -0.00023400784 - - 0.010169983 - - -0.01071167 - - 0.010520935 - - 0.022338867 - - -0.0259552 - - 0.044769287 - - 0.0070610046 - - -0.012451172 - - -0.04156494 - - 0.047088623 - - -0.017578125 - - 0.012741089 - - -0.016479492 - - 0.0023078918 - - -0.008331299 - - 0.021591187 - - 0.01473999 - - -0.018081665 - - 0.033081055 - - -0.057556152 - - 0.008621216 - - 0.013954163 - - -0.009742737 - - -0.015548706 - - 0.015281677 - - -0.005958557 - - 0.0065307617 - - 0.01979065 - - 0.041778564 - - -0.02684021 - - 0.027709961 - - -0.07672119 - - 0.023406982 - - -0.037902832 - - 0.035339355 - - -0.021881104 - - 0.056732178 - - 0.03466797 - - 0.0059318542 - - -0.058654785 - - 0.025375366 - - 0.015029907 - - 0.002380371 - - -0.024230957 - - 0.014541626 - - -0.006641388 - - -0.01864624 - - 0.012290955 - - 0.0007929802 - - -0.009277344 - - 0.04953003 - - -0.004081726 - - 0.0029258728 - - -0.017181396 - - 0.0074920654 - - -0.0001707077 - - 0.04220581 - - 0.008972168 - - -0.0071525574 - - 0.0015583038 - - 0.034362793 - - -0.019058228 - - 0.013626099 - - 0.022613525 - - -0.0061149597 - - 0.017669678 - - 0.015586853 - - 0.034973145 - - 0.02217102 - - -0.045013428 - - -0.009864807 - - 0.07244873 - - 0.010177612 - - 0.029724121 - - -0.018829346 - - -0.034057617 - - -0.018859863 - - 0.059936523 - - -0.0076408386 - - 0.021331787 - - -0.013786316 - - 0.015281677 - - 0.016235352 - - -0.039855957 - - -0.02748108 - - -0.033416748 - - 0.016174316 - - 0.026489258 - - 0.0049095154 - - -0.026000977 - - 0.00831604 - - -0.019851685 - - -0.021408081 - - 0.023010254 - - 0.030075073 - - 0.0335083 - - -0.05493164 - - 0.019515991 - - -0.020401001 - - -0.0061073303 - - 0.018997192 - - 0.020126343 - - -0.027740479 - - -0.038116455 - - 0.0052948 - - -0.008613586 - - -0.016494751 - - -0.001247406 - - 0.022644043 - - 0.008300781 - - -0.02104187 - - 0.016693115 - - -0.0032901764 - - 0.012046814 - - -0.023468018 - - -0.007259369 - - 0.031234741 - - 0.06604004 - - 0.051635742 - - 0.0009441376 - - -0.006084442 - - 0.025619507 - - -0.006881714 - - 0.02999878 - - 0.050964355 - - 0.017715454 - - -0.024856567 - - -0.010070801 - - 0.05319214 - - -0.03652954 - - 0.011810303 - - -0.011978149 - - 0.013046265 - - -0.016662598 - - 0.017166138 - - -0.005542755 - - -0.07989502 - - 0.029220581 - - 0.056488037 - - 0.015914917 - - -0.011184692 - - -0.018203735 - - -0.03894043 - - -0.026626587 - - 0.0010070801 - - -0.07397461 - - -0.060333252 - - 0.046020508 - - -0.017440796 - - -0.020385742 - - -0.0211792 - - -0.018295288 - - -0.01802063 - - 0.003211975 - - -0.012969971 - - -0.034576416 - - -0.022079468 - - 0.034606934 - - -0.022079468 - - -0.02154541 - - -0.0039367676 - - 0.015419006 - - -0.027023315 - - 0.024642944 - - -0.0007047653 - - -0.008293152 - - 0.02708435 - - 0.05267334 - - 0.010177612 - - 0.017822266 - - -0.021759033 - - -0.051116943 - - -0.02583313 - - -0.06427002 - - 0.03213501 - - -0.009635925 - - -0.04547119 - - 0.018997192 - - -0.024032593 - - -0.011024475 - - 0.033935547 - - 0.050842285 - - 0.011009216 - - -0.002527237 - - 0.04852295 - - 0.038360596 - - -0.035583496 - - -0.021377563 - - -0.016052246 - - -0.072143555 - - 0.03665161 - - 0.02897644 - - -0.03842163 - - -0.00068187714 - - 0.022415161 - - -0.0030879974 - - 0.043762207 - - 0.05392456 - - -0.0362854 - - -0.04647827 - - -0.034057617 - - -0.040374756 - - -0.03942871 - - 0.030761719 - - -0.068115234 - - 0.011329651 - - 0.011413574 - - -0.012435913 - - 0.01576233 - - 0.022766113 - - 0.05609131 - - 0.07092285 - - 0.017593384 - - 0.024337769 - - 0.027923584 - - 0.06994629 - - 0.00655365 - - -0.020248413 - - -0.03945923 - - -0.0491333 - - -0.049194336 - - 0.020050049 - - 0.010910034 - - 0.013511658 - - 0.01676941 - - -0.041900635 - - -0.046142578 - - 0.012268066 - - 0.026748657 - - -0.036499023 - - 0.021713257 - - -0.036590576 - - 0.014411926 - - 0.029174805 - - -0.029388428 - - 0.04119873 - - 0.04852295 - - 0.007068634 - - -0.00090408325 - - 0.0048332214 - - -0.015777588 - - -0.01499939 - - -0.0068206787 - - -0.02708435 - - 0.010543823 - - 0.004085541 - - -0.026901245 - - -0.0045661926 - - 0.0061912537 - - -0.0014343262 - - 0.028945923 - - -0.03552246 - - 0.030441284 - - -0.029281616 - - 0.050628662 - - -0.033599854 - - -0.085510254 - - -0.052520752 - - -0.07507324 - - -0.008003235 - - -0.026382446 - - -0.078063965 - - -0.025161743 - - -0.025421143 - - -0.0073165894 - - 0.01889038 - - -0.05999756 - - -0.0051612854 - - 0.0072517395 - - -0.011497498 - - 0.01687622 - - 0.002231598 - - -0.034423828 - - -0.0013084412 - - -0.012413025 - - 0.008888245 - - 0.017486572 - - -0.03353882 - - 0.0069885254 - - -0.02722168 - - 0.02015686 - - -0.04510498 - - -0.038726807 - - -0.0031356812 - - 0.033233643 - - 0.025268555 - - -0.015106201 - - 0.02407837 - - -0.00024700165 - - -0.07409668 - - -0.012367249 - - 0.014785767 - - -0.04486084 - - 0.074401855 - - -0.020690918 - - -0.025222778 - - 0.029083252 - - -0.018997192 - - 0.0017557144 - - 0.03857422 - - -0.020111084 - - 0.03338623 - - -0.028213501 - - 0.0063705444 - - -0.010124207 - - -0.03112793 - - -0.03286743 - - 0.0046043396 - - -0.0052223206 - - 0.00023317337 - - 0.0423584 - - 0.028030396 - - 0.0005788803 - - -0.02708435 - - 0.006324768 - - 0.019821167 - - -0.0042686462 - - -0.026428223 - - -0.02293396 - - 0.036590576 - - -0.023376465 - - -0.022537231 - - 0.032226562 - - -0.020629883 - - 0.017929077 - - 0.0440979 - - -0.014038086 - - -0.022216797 - - 0.020446777 - - -0.05496216 - - -0.018859863 - - -0.039855957 - - 0.008300781 - - 0.07281494 - - 0.018295288 - - 0.042114258 - - 0.005519867 - - 0.017990112 - - -0.008773804 - - 0.011123657 - - -0.008239746 - - -0.045532227 - - 0.026153564 - - -0.015853882 - - 0.027557373 - - -0.049041748 - - -0.0022945404 - - -0.009399414 - - -0.045898438 - - 0.05053711 - - 0.038513184 - - -0.031799316 - - 0.012329102 - - 0.024871826 - - 0.04348755 - - -0.04788208 - - 0.01423645 - - 0.021240234 - - 0.05493164 - - 0.008956909 - - -0.056243896 - - 0.032043457 - - -0.01574707 - - -0.01285553 - - -0.009498596 - - -0.018951416 - - -0.029556274 - - 0.0069274902 - - -0.032348633 - - -0.022445679 - - -0.00093603134 - - -0.015808105 - - -0.027175903 - - 0.014091492 - - 0.025665283 - - -0.023468018 - - -0.03250122 - - -0.0004544258 - - 0.042633057 - - -0.06036377 - - -0.039611816 - - -0.042938232 - - -0.02418518 - - -0.0703125 - - 0.045135498 - - -0.001036644 - - -0.017913818 - - -0.004043579 - - 0.0138549805 - - -0.02532959 - - 0.010765076 - - 0.021575928 - - 0.013114929 - - 0.033935547 - - -0.010574341 - - 0.017990112 - - -0.026107788 - - -0.029144287 - - -0.046569824 - - -0.0030517578 - - -0.022994995 - - -0.017471313 - - -0.0070495605 - - -9.846687e-5 - - 0.029281616 - - 0.017440796 - - 0.045532227 - - 0.025650024 - - 0.0491333 - - -0.013145447 - - 0.070129395 - - -0.0051879883 - - -0.04043579 - - 0.023864746 - - 0.016830444 - - -0.014152527 - - -0.06359863 - - -0.005065918 - - -0.009880066 - - -0.0034618378 - - -0.081726074 - - -0.0289917 - - -0.007461548 - - -0.0013504028 - - 0.020523071 - - 0.0076446533 - - -0.011650085 - - 0.014549255 - - 0.010955811 - - 0.02180481 - - -0.027572632 - - -0.012252808 - - 0.009033203 - - -0.0048980713 - - 0.031173706 - - -0.020309448 - - 0.022979736 - - -0.013900757 - - -0.004108429 - - 0.018325806 - - -0.031402588 - - 0.01737976 - - 0.03201294 - - -0.02508545 - - -0.015625 - - -0.04626465 - - -0.014656067 - - 0.016036987 - - -0.030639648 - - 0.041748047 - - -0.0032978058 - - -0.03277588 - - 0.037719727 - - 0.023788452 - - -0.008140564 - - -0.041809082 - - 0.034698486 - - -0.022994995 - - -0.009979248 - - -0.03729248 - - -0.0904541 - - 0.00028443336 - - 0.080566406 - - -0.035125732 - - -0.054229736 - - -0.017700195 - - 0.060668945 - - 0.008979797 - - 0.015052795 - - -0.0072364807 - - -0.001490593 - - 0.0065231323 - - -0.014579773 - - 0.016067505 - - -0.020339966 - - -0.020217896 - - 0.02909851 - - 0.050628662 - - 0.04510498 - - -0.01979065 - - 0.008918762 - - 0.031799316 - - 0.031951904 - - -0.016906738 - - 0.031036377 - - 0.0040664673 - - -0.046905518 - - -0.04928589 - - 0.044403076 - - -0.0524292 - - -0.012832642 - - 0.049835205 - - 0.0040283203 - - -0.012649536 - - 0.06878662 - - -0.02859497 - - -0.014137268 - - 0.0036144257 - - -0.06262207 - - 0.046813965 - - 0.024978638 - - 0.0017976761 - - -0.032409668 - - -0.004108429 - - -0.013557434 - - -0.07196045 - - 0.026733398 - - 0.0024261475 - - -0.022735596 - - -0.0022182465 - - -0.0064315796 - - -0.03652954 - - 0.04135132 - - -0.032562256 - - 0.004524231 - - 0.020019531 - - -0.0113220215 - - -0.071777344 - - -0.03451538 - - 0.0022583008 - - -0.06512451 - - -0.005317688 - - 0.020248413 - - -0.036712646 - - 0.005809784 - - -0.018951416 - - -0.0026855469 - - 0.027572632 - - -0.00036668777 - - 0.0073623657 - - -0.018829346 - - 0.009101868 - - 0.051971436 - - 0.023132324 - - -0.022537231 - - 0.00932312 - - 0.00944519 - - 0.014183044 - - 0.020889282 - - 0.0032844543 - - -0.0073776245 - - -0.05807495 - - -0.032440186 - - 0.033996582 - - 0.0423584 - - 0.014259338 - - 0.061676025 - - -0.02154541 - - -0.031982422 - - 0.005493164 - - -0.01512146 - - 0.023101807 - - -0.011383057 - - -0.059539795 - - 0.021820068 - - 0.015487671 - - -0.004875183 - - -0.015640259 - - 0.015319824 - - -0.0054359436 - - -0.026229858 - - 0.0061454773 - - -0.032348633 - - 0.038513184 - - 0.004840851 - - -0.016021729 - - -0.017608643 - - -0.019577026 - - -0.009178162 - - 0.045013428 - - -0.01007843 - - 0.022323608 - - 0.034179688 - - 0.00566864 - - 0.055511475 - - -0.033355713 - - -0.019317627 - - -8.481741e-5 - - 0.017547607 - - -0.053344727 - - 0.012229919 - - 0.022384644 - - 0.018051147 - - 0.010734558 - - 0.004501343 - - -0.05911255 - - -0.0030918121 - - -0.0513916 - - -0.0050086975 - - -0.01600647 - - 0.05343628 - - -0.0008234978 - - 0.07293701 - - -0.056610107 - - -0.06549072 - - -0.01776123 - - -0.0022678375 - - 0.023239136 - - 0.01020813 - - -0.005153656 - - -0.00630188 - - -0.009880066 - - 0.022109985 - - 0.033203125 - - -0.03567505 - - -0.014129639 - - 0.015625 - - 0.022888184 - - -0.038726807 - - -0.026321411 - - -0.007259369 - - 0.005924225 - - 0.0010814667 - - 0.06665039 - - -0.008880615 - - 0.053771973 - - 0.062194824 - - 0.018981934 - - 0.022338867 - - 0.01361084 - - 0.025604248 - - 0.022109985 - - 0.0044288635 - - -0.008331299 - - -0.0019416809 - - 0.006454468 - - -0.045013428 - - -0.02519226 - - -0.012268066 - - -0.032165527 - - 7.2181225e-5 - - -0.021575928 - - -0.006324768 - - 0.029785156 - - 0.0063438416 - - -0.01210022 - - 0.029403687 - - 0.00592041 - - 0.008369446 - - 0.00818634 - - -0.04498291 - - -0.041809082 - - 0.0078086853 - - -0.05935669 - - -0.043518066 - - 0.007270813 - - 0.060424805 - - 0.033996582 - - 0.055908203 - - 0.013755798 - - 0.03982544 - - 0.014640808 - - -0.01373291 - - 0.033325195 - - -0.0047073364 - - 0.015899658 - - -0.00043344498 - - 0.022338867 - - -0.007095337 - - 0.02949524 - - 0.042633057 - - 0.030670166 - - 0.022415161 - - -0.0033683777 - - 0.018814087 - - -0.013031006 - - 0.031951904 - - 0.022094727 - - -0.009986877 - - 0.025665283 - - -0.0138168335 - - 0.049743652 - - 0.024307251 - - 0.0088272095 - - -0.03479004 - - 0.07318115 - - 0.009849548 - - 0.051635742 - - -0.05331421 - - -0.053131104 - - -0.0044898987 - - 0.029342651 - - 0.005596161 - - 0.044189453 - - -0.042388916 - - -0.012939453 - - -0.0007529259 - - -0.06088257 - - 0.036010742 - - -0.02355957 - - 0.004497528 - - -0.0023822784 - - -0.053588867 - - -0.04168701 - - -0.017868042 - - -0.01927185 - - -0.06011963 - - 0.028884888 - - 0.061401367 - - -0.005584717 - - 0.014823914 - - -0.02255249 - - 4.631281e-5 - - 0.039031982 - - -0.0055389404 - - 0.007194519 - - 0.0037631989 - - 0.008834839 - - 0.018692017 - - 0.033111572 - - -0.056274414 - - -0.021774292 - - 0.04727173 - - -0.03265381 - - 0.022140503 - - 0.027801514 - - 0.004043579 - - -0.016525269 - - -0.041809082 - - 0.024520874 - - 0.008529663 - - 0.049072266 - - 0.033447266 - - -0.028839111 - - 0.048675537 - - 0.021453857 - - -0.08087158 - - 0.034606934 - - -0.002910614 - - 0.012176514 - - 0.035705566 - - 0.040161133 - - -0.02355957 - - -0.01626587 - - -0.033721924 - - -0.013893127 - - -0.04156494 - - 0.06719971 - - 0.043151855 - - -0.033813477 - - 0.028045654 - - 0.0029525757 - - -0.022033691 - - -0.093811035 - - -0.0056114197 - - 0.00026154518 - - 0.058746338 - - -0.05065918 - - 0.02897644 - - -0.01550293 - - -0.02947998 - - -0.018249512 - - 0.034942627 - - -0.04574585 - - -0.037109375 - - -0.006160736 - - 0.006149292 - - -0.0012207031 - - -0.042907715 - - -0.016448975 - - 0.0052719116 - - 0.036590576 - - -0.045318604 - - -0.04220581 - - -0.018859863 - - -0.031021118 - - 0.06439209 - - -0.0056533813 - - -0.037200928 - - -0.026550293 - - 0.027786255 - - -0.028427124 - - 0.09161377 - - -0.0088272095 - - -0.003643036 - - -0.053253174 - - -0.01826477 - - -0.016540527 - - -0.012535095 - - -0.03942871 - - -0.0049095154 - - 0.031311035 - - 0.049468994 - - -0.066589355 - - -0.05029297 - - 7.5519085e-5 - - -0.0017404556 - - -0.013214111 - - -0.03756714 - - -0.009147644 - - -0.025466919 - - 0.026672363 - - 0.020965576 - - -0.0073432922 - - 0.0011005402 - - -0.04937744 - - -0.018463135 - - 0.00274086 - - -0.013252258 - - 0.0126953125 - - -0.077697754 - - 0.014045715 - - 0.00039935112 - - -0.019515991 - - -0.0027618408 - - -0.011672974 - - -0.043884277 - - 0.009231567 - - 0.062805176 - - -0.0137786865 - - -0.026229858 - - -0.034362793 - - -0.015090942 - - 0.016937256 - - 0.030639648 - - -0.02420044 - - 0.02482605 - - -0.0033740997 - - 0.046417236 - - -0.012008667 - - -0.04031372 - - -0.00032520294 - - 0.01525116 - - -0.0066375732 - - 0.0062713623 - - -0.01171875 - - -0.027191162 - - -0.014137268 - - -0.025390625 - - 0.002111435 - - -0.06561279 - - 0.031555176 - - -0.07519531 - - -0.04547119 - - 0.014472961 - - -0.0158844 - - -0.091552734 - - -0.03366089 - - 0.050323486 - - -0.0013589859 - - -0.033203125 - - 0.046539307 - - -0.030288696 - - 0.0046195984 - - 0.049835205 - - 0.02003479 - - -0.004196167 - - 0.013168335 - - -0.016403198 - - 0.01676941 - - -0.00340271 - meta: - api_version: - version: '2' - billed_units: - images: 1 - response_type: embeddings_by_type - - code-samples: - - sdk: go - name: Texts - code: | - package main - - import ( - "context" - "log" - - cohere "github.com/cohere-ai/cohere-go/v2" - client "github.com/cohere-ai/cohere-go/v2/client" - ) - - func main() { - co := client.NewClient(client.WithToken("<>")) - - resp, err := co.Embed( - context.TODO(), - &cohere.EmbedRequest{ - Texts: []string{"hello", "goodbye"}, - Model: cohere.String("embed-english-v3.0"), - InputType: cohere.EmbedInputTypeSearchDocument.Ptr(), - }, - ) - - if err != nil { - log.Fatal(err) - } - - log.Printf("%+v", resp) - } - - sdk: typescript - name: Texts - code: | - const { CohereClient } = require('cohere-ai'); - - const cohere = new CohereClient({ - token: '<>', - }); - - (async () => { - const embed = await cohere.embed({ - texts: ['hello', 'goodbye'], - model: 'embed-english-v3.0', - inputType: 'classification', - embeddingTypes: ['float'], - }); - console.log(embed); - })(); - - sdk: python - name: Texts - code: > - import cohere - - - co = cohere.Client("<>") - - - response = co.embed( - texts=["hello", "goodbye"], model="embed-english-v3.0", input_type="classification" - ) - - print(response) - - sdk: python - name: Texts (async) - code: > - import cohere - - import asyncio - - - co = cohere.AsyncClient("<>") - - - - async def main(): - response = await co.embed( - texts=["hello", "goodbye"], model="embed-english-v3.0", input_type="classification" - ) - print(response) - - asyncio.run(main()) - - sdk: java - name: Texts - code: > - package embedpost; /* (C)2024 */ - - - import com.cohere.api.Cohere; - - import com.cohere.api.requests.EmbedRequest; - - import com.cohere.api.types.EmbedInputType; - - import com.cohere.api.types.EmbedResponse; - - import java.util.List; - - - public class EmbedPost { - public static void main(String[] args) { - Cohere cohere = Cohere.builder().token("<>").clientName("snippet").build(); - - EmbedResponse response = - cohere.embed( - EmbedRequest.builder() - .texts(List.of("hello", "goodbye")) - .model("embed-english-v3.0") - .inputType(EmbedInputType.CLASSIFICATION) - .build()); - - System.out.println(response); - } - } - - sdk: curl - name: Texts - code: |- - curl --request POST \ - --url https://api.cohere.com/v1/embed \ - --header 'accept: application/json' \ - --header 'content-type: application/json' \ - --header "Authorization: bearer $CO_API_KEY" \ - --data '{ - "model": "embed-english-v3.0", - "texts": ["hello", "goodbye"], - "input_type": "classification" - }' - request: - texts: - - hello - - goodbye - model: embed-english-v3.0 - input_type: classification - response: - body: - id: 1c62213a-1f15-46f1-ac62-36f6bbaf3972 - texts: - - hello - - goodbye + id: 1c62213a-1f15-46f1-ac62-36f6bbaf3972 + texts: + - hello + - goodbye embeddings: - - 0.016296387 - -0.008354187 @@ -11499,10 +10260,1249 @@ paths: - 0.0052719116 meta: api_version: - version: "1" + version: "1" + billed_units: + input_tokens: 2 + response_type: embeddings_floats + - code-samples: + - sdk: go + name: Images + code: > + package main + + + import ( + "context" + "encoding/base64" + "fmt" + "io" + "log" + "net/http" + + cohere "github.com/cohere-ai/cohere-go/v2" + client "github.com/cohere-ai/cohere-go/v2/client" + ) + + + func main() { + // Fetch the image + resp, err := http.Get("https://cohere.com/favicon-32x32.png") + if err != nil { + log.Println("Error fetching the image:", err) + return + } + defer resp.Body.Close() + + // Read the image content + buffer, err := io.ReadAll(resp.Body) + if err != nil { + log.Println("Error reading the image content:", err) + return + } + + stringifiedBuffer := base64.StdEncoding.EncodeToString(buffer) + contentType := resp.Header.Get("Content-Type") + imageBase64 := fmt.Sprintf("data:%s;base64,%s", contentType, stringifiedBuffer) + + co := client.NewClient(client.WithToken("<>")) + + embed, err := co.Embed( + context.TODO(), + &cohere.EmbedRequest{ + Images: []string{imageBase64}, + Model: cohere.String("embed-english-v3.0"), + InputType: cohere.EmbedInputTypeImage.Ptr(), + EmbeddingTypes: []cohere.EmbeddingType{cohere.EmbeddingTypeFloat}, + }, + ) + + if err != nil { + log.Fatal(err) + } + + log.Printf("%+v", embed) + } + - sdk: typescript + name: Images + code: > + const { CohereClient } = require('cohere-ai'); + + + const cohere = new CohereClient({ + token: '<>', + }); + + + (async () => { + const image = await fetch('https://cohere.com/favicon-32x32.png'); + const buffer = await image.arrayBuffer(); + const stringifiedBuffer = Buffer.from(buffer).toString('base64'); + const contentType = image.headers.get('content-type'); + const imageBase64 = `data:${contentType};base64,${stringifiedBuffer}`; + + const embed = await cohere.embed({ + model: 'embed-english-v3.0', + inputType: 'image', + embeddingTypes: ['float'], + images: [imageBase64], + }); + console.log(embed); + })(); + - sdk: python + name: Images + code: > + import cohere + + import requests + + import base64 + + + co = cohere.Client("<>") + + + image = requests.get("https://cohere.com/favicon-32x32.png") + + stringified_buffer = + base64.b64encode(image.content).decode('utf-8') + + content_type = image.headers['Content-Type'] + + image_base64 = + f"data:{content_type};base64,{stringified_buffer}" + + + response = co.embed( + model="embed-english-v3.0", + input_type="image", + embedding_types=["float"], + images=[image_base64] + ) + + + print(response) + - sdk: java + name: Images + code: > + package embedpost; /* (C)2024 */ + + + import com.cohere.api.Cohere; + + import com.cohere.api.requests.EmbedRequest; + + import com.cohere.api.types.EmbedInputType; + + import com.cohere.api.types.EmbedResponse; + + import com.cohere.api.types.EmbeddingType; + + import java.io.InputStream; + + import java.net.HttpURLConnection; + + import java.net.URI; + + import java.net.URL; + + import java.util.Base64; + + import java.util.List; + + + public class EmbedImagePost { + public static void main(String[] args) { + Cohere cohere = Cohere.builder().token("<>").clientName("snippet").build(); + + URL url = + URI.toUrl( + "https://cohere.com/favicon-32x32.png"); + HttpURLConnection connection = (HttpURLConnection) url.openConnection(); + connection.connect(); + + InputStream inputStream = connection.getInputStream(); + byte[] buffer = inputStream.readAllBytes(); + inputStream.close(); + + String imageBase64 = + String.format( + "data:%s;base64,%s", + connection.getHeaderField("Content-Type"), Base64.getEncoder().encodeToString(buffer)); + + EmbedResponse response = + cohere.embed( + EmbedRequest.builder() + .images(List.of(imageBase64)) + .model("embed-english-v3.0") + .inputType(EmbedInputType.IMAGE) + .embeddingTypes(List.of(EmbeddingType.FLOAT)) + .build()); + + System.out.println(response); + } + } + - sdk: curl + name: Images + code: >- + curl --request POST \ + --url https://api.cohere.com/v1/embed \ + --header 'accept: application/json' \ + --header 'content-type: application/json' \ + --header "Authorization: bearer $CO_API_KEY" \ + --data '{ + "model": "embed-english-v3.0", + "input_type": "image", + "embedding_types": ["float"], + "images": ["data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD//gAfQ29tcHJlc3NlZCBieSBqcGVnLXJlY29tcHJlc3P/2wCEAAQEBAQEBAQEBAQGBgUGBggHBwcHCAwJCQkJCQwTDA4MDA4MExEUEA8QFBEeFxUVFx4iHRsdIiolJSo0MjRERFwBBAQEBAQEBAQEBAYGBQYGCAcHBwcIDAkJCQkJDBMMDgwMDgwTERQQDxAUER4XFRUXHiIdGx0iKiUlKjQyNEREXP/CABEIAZABkAMBIgACEQEDEQH/xAAdAAEAAQQDAQAAAAAAAAAAAAAABwEFBggCAwQJ/9oACAEBAAAAAN/gAAAAAAAAAAAAAAAAAAAAAAAAAAHTg9j6agAAp23/ADjsAAAPFrlAUYeagAAArdZ12uzcAAKax6jWUAAAAO/bna+oAC1aBxAAAAAAbM7rVABYvnRgYAAAAAbwbIABw+cMYAAAAAAvH1CuwA091RAAAAAAbpbPAGJfMXzAAAAAAJk+hdQGlmsQAAAAABk31JqBx+V1iAAAAAALp9W6gRp826AAAAAAGS/UqoGuGjwAAAAAAl76I1A1K1EAAAAAAG5G1ADUHU0AAAAAAu/1Cu4DVbTgAAAAAA3n2JAIG0IAAAAAArt3toAMV+XfEAAAAAL1uzPlQBT5qR2AAAAAenZDbm/AAa06SgAAAAerYra/LQADp+YmIAAAAC77J7Q5KAACIPnjwAAAAzbZzY24gAAGq+m4AAA7Zo2cmaoAAANWdOOAAAMl2N2TysAAAApEOj2HgAOyYtl5w5jw4zZPJyuGQ5H2AAAdes+suDUAVyfYbZTLajG8HxjgD153n3IAABH8QxxiVo4XPKpGlyTKjowvCbUAF4mD3AAACgqCzYPiPQAA900XAACmN4favRk+a9wB0xdiNAAAvU1cgAxeDcUoPdL0s1B44atQAACSs8AEewD0gM72I5jjDFiAAAPfO1QGL6z9IAlGdRgkaAAABMmRANZsSADls7k6kFW8AAAJIz4DHtW6AAk+d1jhUAAAGdyWBFcGgAX/AGnYZFgAAAM4k4CF4hAA9u3FcKi4AAAEiSEBCsRgAe3biuGxWAAACXsoAiKFgALttgs0J0AAAHpnvkBhOt4AGebE1pBtsAAAGeySA4an2wAGwEjGFxaAAAe+c+wAjKBgAyfZ3kUh3HAAAO6Yb+AKQLGgBctmb2HXDNjAAD1yzkQAENRF1gyvYG9AcI2wjgAByyuSveAAWWMcQtnoyOQs8qAPFhVh8HADt999y65gAAKKgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAf/8QAGgEBAAMBAQEAAAAAAAAAAAAAAAEFBgIEA//aAAgBAhAAAAAAAAAAAAABEAAJkBEAAB0CIAABMhyAAA6EQAAA6EQAABMiIAAAmREAAAmQiAABMgOQAEyAHIATIACIBMu7H3fT419eACEnps7DoPFQch889Wd3V2TeWIBV0o+eF8I0OrXVoAIyvBm8uDe2Wp6ADO+Mw9WDV6rSgAzvjMNWA1Op1AARlvmZbOA3NnpfSAK6iHnwfnFttZ9Wh7AeXPcB5cxWd3Wk7Pvb+uR8q+rgAAAAAAAAP//EABsBAQABBQEAAAAAAAAAAAAAAAAEAQIDBQYH/9oACAEDEAAAAAAAAAC20AL6gCNDxAArnn3gpro4AAv2l4QIgAAJWwGLVAAAX7cQYYAAFdyNZgAAAy7UazAAABsZI18UAAE6YEfWgACRNygavCACsmZkALNZjAMkqVcAC2FFoKyJWe+fMyYoMAAUw2L8t0jYzqhE0dAzd70eHj+PK7mcAa7UDN7VvBwXmDb7EAU5uw9C9KCnh2n6WoAaKIey9ODy/jN+ADRRD2fpQeY8P0QAU5zGel+gg8V53oc4AgaYTfcJ45Tx5I31wCPobQ2PpPRYuP8APMZm2kqoxQddQAAAAAAAAP/EAFMQAAEDAgIDCQkMBwUIAwAAAAECAwQFEQAGBzFREhMhMEBBYXGBCBQYIjJCRlDSFSBSVGJygpGTobHREDRDc6LBwiMzU3CyFiQlNVVkdISSlLP/2gAIAQEAAT8A/wAo74nVaBAb32bNYitfDfcS2PrURiZpU0dwVFMjN1OVY8O8u7//APkFYc076LmfSVSvmQpB/ox4QGjH/r7v/wBGR7OPCA0YH0ge7IMj2ceEBowPpA92QZHs48IDRgfSB7sgyPZx4QGjA+kD3ZBkezjwgNGB9IHuyDI9nHhAaMD6QPdkGR7OPCA0YH0ge7IMj2ceEBowPpA92QZHs48IDRgfSB7sgyPZx4QGjA+kD3ZBkezjwgNGB9IHuyDI9nHhAaMD6QPdkGR7OPCA0YH0ge7IMj2ceEBowPpA92QZHs48IDRgfSB7sgyPZx4QGjA+kD3ZBkezjwgNGB9IHuyDI9nHhAaMD6QPdkGR7OPCA0Y89fd7IMj2cN6e9GDpCTmRaOuFI9nEDSlo9qakpj5upoJNgH3d4+50JxGlxpbSH4r7bzSvJW0sLSeop5NWsw0fL8RU2rVGPDjJ4C6+4EAnYnaegYzV3StDhFcfK1LdqDuoSZBLDHWlPlqxXtNmkOulaVVxcFg3/sYA73A+kLrxKnTJrpfmSXX3jrcdWVqPWVYudvJ7nbil16s0R7vikVSVDduCVR3lNk9e5IvjKfdG5rpKmo+Yo7NXi8ALlgxJH0kiysZL0l5Uzsz/AMFn2l7m7kJ8BuSj6PnAbU8ieeZitOPPuoQ22krWtZCUpSkXJJOoDGkHui4MBT1MyW2ibITdJnuA97o/dJ1uHFczFXMyzV1Gu1N+bJV57yr7kbEjUkdA5dGlSYb7UqJIcZfaUFtuNLKFoUNRSocIONF3dBb6tih58eSCQEM1PUOqT7eELS4lK0KCkkAgg3BB4/M2Z6NlKlSKtWJiI8VoWueFS1nUhA85ZxpJ0v13Pj7kNorg0NC7tw0K4XNi3yPKPRqHqLQnpkeoD8XKmZZJVSHCG4klw/qijqQs/wCF/pwDfjc1ZqpOUKNLrVXf3qMyLJSLFbrh8ltA51qxn7P9az9V1z6istxWypMSIhRLbCD+Kj5yvUYJHCMdz7pLXWoByfWJBXUILV4bizwvRk+Z0qa4yoTodKgyZ859DEWO0t11xZslCEC5UrGlHSNOz/XVvBa26RFKkQY+xHO4v5a/UtArU3LlZptbpzm4lQ30ut7DbWk9ChwHGXq5EzHQ6ZWoCv8AdpsdDyRrIKtaFdKTwHi+6I0hrffGRKU/ZloodqSkngW5rQz1I1n1P3M2ZzJpFYyvIXdUJ0SowP8AhP8AAtI6AvitIWbWclZVqlbWElxpvcRmz+0kOcDaf5nEyXJnypM2Y8p2Q+6t11xRupa1m6lHpJ9T6B6uaVpHo7alEMz0PQnepxN0/wASRgauJ7pTNZmVynZTjuXZpzYkSRtkPDgB6UI9UZMlrgZsy1MQqxZqkRy/QHRfA4iZIaiRX5D6ghpptTi1bEIFycZmrL2YcwVitvk7ubLdfsfNClcCewcHqiiX91qbbX3yz/rGBxGmKse4ujnMz6F2dfjiGj/2VBs/ccE3J9UZOirm5ry3EQm5eqkRu3Qp0YHEd01PLGUqPT0mxk1QLV0oZaPteqdBtKNV0kUIkXah77Md6mkcH8RGBq4jupH7JyXG/wDPcP1tj1T3MuWVMQK5mt9FjJWmDGO1tHjuHqJ4nupEnvrJa+beZ4/jR6ooNGnZhrFOotNa3yXMeS02OvWo9CRwk4ytQIeWKDS6HC/V4TCWgq1itWtSz0rPCeJ7qKNenZSl2/upEtonpcShXqcC+NA+jFeW4H+1NbYKatOaswysWMaOrbscc4rujaYZuj/vzccMCpR3yehwFn+r1MAVGwGNDOhVbK4ubc4xLLFnYMB1PCNjrw/BHF58opzDk7MlHSndOSID28ja6gbtH3jChZRHqShZerOZag1S6JT3pcpzUhsahtUTwJTtJxow0G0vKRYreYS1PrIAUhNrx4yvkA+WsfCONXFnGlTLZytnqvU5KLRlvmTG2Fl/xwB0J1eookOXPkNRYUZ1991W5baaQVrWdiUi5JxkbudKzVCzOzg+abE196NWXKWOnWlvGW8p0DKMEU6g01qKzwFe5F1uEDynFnhUeO7pTJ5n0aBmyK3d+mneJVtZjOnxVfQX6ghwZtRktQ4EV6RJcNkNMoK1qOwJTcnGTe5yr9V3qXmuSKXFNj3uizkpY/0oxlbIOVslRt6oVKaZdIst9XjyHPnOK4ezkFVgw6vAmU2ewHYsllbDiFaloWNyoYz1lKZknMtRoEu6gyvdMO8zrC/IXy2j0Cs5glpg0WmyJkk+YwgrIG1WwdJxk7uap75amZyqQit6zChkLe6lueSnGWcl5ayjGEegUliKCAFuAbp5z57irqPI9NOjVOdqB31T2x7tU5KlxNryNa2CenWnDra2XFtOoUhaFFKkqFiCOAgg8qyro7zdnJwCh0Z5xi9lSVje46etarA22DGUe5spEPe5ebqgue78Ui3aj9Sl+WvFIodHoMREGj02PDjJ1NMNhAJ2m2s8m07aIHJi5WdMsxSZFiuoxG08LoGt9sDz/hjGrkzLD0hxDLDSluLISlKQSpRPMAMZU0C54zFvcidHTR4Sv2k24dI+SyPG+u2MqaBskZc3qRLimrzEftZoBaB+S0PFw0y2y2hppCUIQAEpSAAAOYAauU6XtBJmuycy5LjASVXcl05sWDu1bGxe1GHWnGXFtOoUhxCilSVAghSTYgg6iOR5eyfmXNT/AHvQKNJmKBspTaLNo+es2SntOMq9zNIc3uTm+sBoazEgWWvtdWLDGWchZTyk2E0KiR4zlrKkEbt9XW4u6uW6SNDNAzwHZ7BTTq3YkSm0XS7sS+ka/na8ZuyJmbJMwxK9T1NJJs1IR47D3S2vj2mXXlobabUtaiAlKRcknUAMZV0F56zJvT8iEKVCVY77PuhZHyWvLxlTuesl0Te3qqlysy08JMnxI4PQ0n+onEWDFhMNxokdphhsWQ20gIQkbEpFgPeyqnBg/rMhCCBfc3ur6hw4lZ1hNbpMdlbpGokhKT+OHs7zVf3EdpHzgVfzGDnGqnnbHUkYGcqqOZo/OT+VsMZ5eBG/w0K2lJKPaxDzfTJBCXFLZUTbxk3+q2GJTEhAcYdQtB1KSoEckqdLp1ThvQqnEZkxXU7lbLyAtCusKxnPubKVNU9NyhOMB03Pekm7kfsXwqRjM+jfOWUVLNZochEcapLY31gj56LgduLHZxNjjL+TM0ZpcDdCokuWL2LiEWaSflOKskYyt3M8t0tSM31hLCNZiwbLc7XVCwxljR9lHKDaRQ6Kww6BZUlQ32Qr6a7nAAHvFLSkEqUAAMT81UyGClDm/r2N6u1WKhm2oywpDKt4bPMjX/8ALC3HHCVLWSSbm+338adLhuB2O+tChzg4pOdOFDVRRbm31A/EflhiQ1IbS6y4laFaik3HJCkKBBAII4RjMOibIOYCtc/LkZD6tb0W8Zy+0luwVisdzDRX925RMyS4uxMtlD46gUFGKj3NWdY11wajSpbf71bS/qUnErQTpPjXIy2Xk7WZLCv68L0R6R2/KylO+ikK/A4Tom0jL1ZRqHa3bEXQjpPlkBGVXkDa48yj8V4p/c358lEGW/TIaOcOSCtfYG0qxSO5gp6AldczQ+9tbhsBr+NwqxRNDWjygFDjGXmpL4N99nEyVH6K/FGGmGY7SGm20oQgAJSkAJAHMAPeyJ8WEjfJD6EX1XP4DWTioZ1ZRdEBndnmWvgT2DE6tVCoE98SFFPMgGyR2DBN+E8XSq3MpToUyu7ZIK0HUcUmsRapGK46wlfBuknWnk5AOsY3I2YsNmLAagPf1HMFNp+6S68FOD9mjhV+QxUM5THrohJDKNutWHpL8halvOqWo6yokk8fT58inSESI6ylST2EbDtGKRU49VitvtkJI8tOsg7OOJA1nFSzhQKaVIkT21OA23DV3Fdu51Yk6VICCREpzznS4pKPw3WDpXk34KOgD9+fZwxpWB4JNIIG1D1/xTinaSMvylJDy3YyjwDfUXH1pviFPhTGw/FkNuoOpbagofdxU2fHhMqekOBDadus4q+bJcwqahkssfxnrOFKKjckk8iodWcpUxDySS2rgcTfWMMPtvstvNKCkLSFJI5weMzFm6mZfQUvL32UQCiOg+N1q2DFbzlWa2paXHyzGOplolKbfKOtWLnb72FUp9NeD8GU4y4OdBtfr2jGW9JTbqm4tdQlCr2D6fIPzxzYadbdQhxpYUlQBBBuCD7+pVKPTIq5D6uAcCUjWpWwYqtWlVV9Tr6yE6kIHkpHJcl1cqS5TXjfc+O3f7xxedc6IoqTAgEKnqHCdYZB5ztVsGH5D0p5x+Q6px1ZKlKUbknico5zk0J5EWWtTtPWeFOstdKejaMR5TMxhuQw4lbTiQpKkm4UD7151thtbriwlCElSidQAxXaw7VZalXsyglLadg/M8mpstcKbHko1oWDbb0duGXEOtIcQbpUkKB2g8Tm3MSMv0xbySDJduhhB+FtPQMSJD0p5yRIcK3XFFSlK1kni9HealU+UijzFjvZ5X9iVHyHDzdSve5yqqm2kU5pViuynCNnMOUZVld80lgKsVNEtns4QPqPEKNgTjOdbVWq0+tC7xmCWmRzWTrV2njEqUhQUkkEG4Ixk6ue7dFjPuuXeau08Plp5+0cP6VrS22pSiAACSdgGKpMXPnSJK/PWSBsHMOzlGRX/EmsW8koWOs3B4jONTNNoNQkIUUr3ve27awpzxb4PCTxujGpKYqkinKV4klvdJ+e3+nMkjvakS1DWtIb7FcB+7BNyTyjI67S5CDzsqP1EcRpUkqRTqfFBtvr6l9iE2/nx2V5XeeYKS9/3CEdizuD+OEm4/RnVak0+OhJtd256gm38+U5JTeY+rYyofeniNKyjv8AR0c24f8AxTx1NJTUYKhrD7Z/iGEeSP0Z63Pe8Xc6hur9dxynI7JtNeOqyAO0m/EaVv1mj/Mf/FPHU7/mEL98j8cI8gfozq2pdOZWnmdseopJ5TlKIWKShZFi8tSz2eL/AC4jSsx/Y0qR8FbqD9IA8dQmFSK1S2UjypTQ7N0L4SLJ/RmOOJVIloSk+Ijdjb4nCcEWJB5PDjrlSWWGxdS1hI7TiHHRGjsso8htCUDqSLcRpDppl5ckLABXHUl8DYBwH7jx2juAZeYmXyk7iM2t07L23I/HA/QtIWkpULggjFXgqp8+RHINkrO5O0axyfJlLK3l1F1Pit3S3cecRr7BxMqM3IjusOpCkOoKVjakixGKzTXaTU5cB4HdNOEAnzk6we0cbo3o5g0hU91FnZhCh+7T5PvM6UjfWkTmE3W0LObSnmPZyanQHqjKajMjhUeE2uANpxAhNQYzTDabNtpsOk85PXxWkjLJmRk1mGjdPR0WdA85rb9HjMqUByv1Rtgg97N2W+vYjZ1qww02y2htCQlCEhKUjUAPeLQlxCkLAUlQsQdRBxmKiOUqWopSox1m6FHht0HkjDDsl1DLKCpajYAYoFFRSYw3dlSF8K1bPkji1JCgUkXBxnjJTlJecqVOZvCWbrQn9kT/AEniqVSplYmNQoTRW4s9iRzqUeYDGXaBFoFPbiMC6/KdctYrVt/Ie+qECNMjKjyE7oLHaOkYrVEkUl8hQKmVE7hY1HkUOFInPoYjtla1bMUDLzNKb3xyy5KvKXzDoTxrjaHEKQ4gKSoWIIuCDzYzTo5WlTk2ggEG6lxr6vmH+WHmXWHFtPNqQ4k2UlQIIOwg+/y/lCq19xKm2yzFv4z7g8X6I844oOXoFBiiPDb4TYuOny1kbTxEmOxKaVHebS4hXlA4rWTpEdSnqfdxu5JR5w6tuFtONKKXEFJBsQeOShSzZIvilZTnTShySCwyfhDxj1DFPpcSmtBuM0B8JR4VK6zyCr5apFaQROiJWsCwdT4qx1KGKloseG7XSp4UnmQ+LfxJxJyLmaMoj3OU4n4TakqwrLVfSbGjy/sV4ZyhmN/yKRI+kncf6rYhaM64+QZa2YyOk7tQ7E4o+jyiU0h2SgzHhzu+R2I/PCEIbASgAJAsAOLqFFp84HvphKlkCyhwK4OnZiXkcElUKV9Fz2hh/KdZataPuwfOSoEYXQqog2MJ49Taj/LHuNVPiEj7Jf5Y9xqp8QkfZL/LHuNVPiEj7Jf5Y9xqp8QkfZL/ACx7jVT4hI+yX+WPcaqfEJH2S/yx7jVT4hI+yX+WEUCquaoTw+chQ/EYYyjWHQSpgN9K1C33XOIuR0+VMlfRbH8ziFRKdTwksRkhY89XjK+/VyWwxYf5ef/EADgRAAIBAgMDCQUHBQAAAAAAAAECAwQRAAUgMUFhEhMhIjBAUXGREDJQU6EGFDNCYoGSUnKiwdH/2gAIAQIBAT8A+L37e/wE9zHfj3k90Gk90Gk9ztqPcbd3t3e3b2129qRySGyIScRZY56ZXtwGFoKZfyX8zj7rT/JX0w+X0zbFKngcTZdLHdozyx9cbOg9pbFtENJPNYqlh4nEOWxJYykufQYVFQWRQBw1VVGk4LKAJPHxwysjFWFiNUsscKGSVwqjecVOfgErSxX/AFNhs5r2P4oHkoxHndchHKZXHFf+YpM7gnISYc0/+J0KpYhVFycUtCkQDygM/huHZZjThl59R1l97iNMsqQxvLIbKoucV1dLWykkkRg9VdOUZmyOtLO10PQhO4+Hty6mCrz7jpPu+XZsoZSp2EEYkQxyOh/KSNGf1JAipVO3rNq2EHGW1P3mkikJ6w6reYxGpd0QbyBhVCqFGwC3aV4tUycbHRnLFq+UeAUfTX9nmJhqE3BwfUYoxeqi8+1ryDVPwA0ZwCMwm4hT9Nf2eB5qobcWUfTFM3Inib9Q7QkAEnYMSvzkrv4knRn8BEkVQB0Ecg+Y15RTmCij5Qsz9c/v7KWYTQo28dDefZ5hUBI+aU9Z9vAaamnSqheF9jD0OKmmlpZWilFiNh3Eacqy9quUSSLaFDc8T4YAt7KWpNPJfap94YR1kUOhuD2NTVJTr4vuGHdpHZ3NydVVSQVaciZfIjaMVOR1URJhtKvocNSVSmzU8gP9pxHQVkhASnf9xbFJkJuHq2Fv6F/2cIiRoqIoVQLADRBUSwG6Ho3g7DiLMYX6Huh9RgTwtslT1GOdi+YnqMc7F8xP5DHOxfMT+Qxz0XzE9Rh6ymTbKD5dOJsyY3WFbcThmZiWYkk7z8W//8QAOREAAgECAgYHBwMDBQAAAAAAAQIDAAQFERITICExkQYwQVFSYXEQFCJAQlOBMlChI4KSYnJzsbL/2gAIAQMBAT8A/YCyjiwFa2PxjnWtj8Y51rY/GOda2PxjnWtj8Y51rY/GOda2PxjnWtj8Y51rY/GOda2PxjnWtj8YoMp4EHq5LlV3LvNPNI/FuXW5kcDUdw6cd4pJFkGanbJABJqacvmq7l+RR2Rgy0jiRQw2rmXM6CncOPydq+T6B4HZmfQjJ7eA+UQ6LqfMbN229V/Pyg4j1GzcnOVvlIV0pFH52bgZSt8pbRaC6TcTs3YycHvHyQBJAFQ2+WTyfgbVymlHmOI+Rjt3fe3wio4kj4Df39RNGY38jw60AscgMzSWrHe5yFJEkfBd/f1UiLIpU1JG0ZyPVJE7/pWktRxc/gUqKgyVQOtZVcZMMxUlqw3pvHdRBU5EEbIBO4CktpG3t8IpLeNOzM+fsSN5DkikmosPY75Wy8hS2duv0Z+te7wfaXlT2Nu3BSvoalsJE3xnTH81vG49UVVtzAGjbRH6cq90TxGvdE8RoW0Q7M6Cqu5VA9kVrNLvC5DvNRWEa75CWPIUqqgyVQB5bVzarMCy7n7++mUoxVhkRtW9tPdypBbRNJI3BVFYf0FdlWTErnQP24uP5JqLojgUYyNqznvZ2q46GYLKDq0khPejk/8ArOsU6HX1irTWre8xDeQBk4/FHduPtALEKozJq3skjAaQaT/wOqv4NJdco3jj6bNtby3c8VtAulJIwVRWCYJb4PbKqqGnYDWSdpPcPLZ6V9HEmikxOxjAlQaUqL9Q7x5+2xgCrrmG8/p9OrIDAg8CKkTQd07iRsdBcPV3ucSkX9H9KP1O8naIBBBG410gsBh2K3MCDKNjrE/2tSLpuqDtIFKAqhRwA6y9GVw/mAdjohEEwK2I4u0jH/Lb6exgXljL2tEwP9pq0GdzF69bfHO4fyAGx0ScPgVpl9JkB/yO309cG6w9O0ROeZq3bQnib/UOsJyBJqV9ZI7952Ogl8DDdYezfEra1B5HcdvpTfC+xicoc44QIl/t4/z7LaUTRK3bwPr1d9PoJqlPxN/A2cOvpsNvIbyA/Eh3jvHaDWHYjbYnapdWzgg/qHap7js9JseTDLZreBwbuVSAB9AP1GiSSSeJ9ltcGB8/pPEUjq6hlOYPU3FykC97dgp3aRi7HMnaw3FbzCptdaSZeJDvVh5isO6aYdcqq3gNvJ25705ikxXDJAGS/gI/5FqfHMIt10pb+H0DBjyGdYr03XRaLCojnw1sg/6FTTSzyPNNIXkc5szHMnYhuJIDmh3doPCo7+F9z5oaE0R4SrzrWR/cXnWsj+4vOtZH9xeYrWx/cXmKe6gTjID6b6lxAnMQrl5mmYsSzEkn92//2Q=="] + }' + request: + model: embed-english-v3.0 + input_type: image + embedding_types: + - float + images: + - data:image/jpeg;base64,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 + response: + body: + id: 5807ee2e-0cda-445a-9ec8-864c60a06606 + texts: [] + images: + - width: 400 + height: 400 + format: jpeg + bit_depth: 24 + embeddings: + float: + - - -0.007247925 + - -0.041229248 + - -0.023223877 + - -0.08392334 + - -0.03378296 + - -0.008308411 + - -0.049926758 + - 0.041625977 + - 0.043151855 + - 0.03652954 + - -0.05154419 + - 0.011787415 + - -0.02784729 + - -0.024230957 + - -0.018295288 + - -0.0440979 + - 0.032928467 + - -0.015007019 + - 0.009315491 + - -0.028213501 + - -0.00022602081 + - -0.0074157715 + - -0.000975132 + - 0.05783081 + - 0.029510498 + - 0.024871826 + - -0.009422302 + - -0.028701782 + - -0.021118164 + - -0.019088745 + - -0.0038433075 + - 0.04083252 + - 0.03024292 + - -0.010154724 + - -0.008163452 + - 0.04269409 + - 0.017471313 + - -0.010017395 + - 0.006629944 + - 0.011047363 + - 0.013542175 + - -0.007926941 + - -0.024932861 + - -0.05960083 + - -0.05404663 + - 0.037384033 + - -0.049621582 + - -0.024002075 + - 0.040039062 + - 0.02645874 + - 0.010261536 + - -0.028244019 + - 0.016479492 + - 0.014266968 + - -0.043823242 + - -0.022262573 + - -0.0057678223 + - -0.04800415 + - 0.041015625 + - 0.01537323 + - -0.021530151 + - -0.014663696 + - 0.051849365 + - -0.025558472 + - 0.045776367 + - -0.025665283 + - -0.005821228 + - 0.02973938 + - 0.053131104 + - 0.020706177 + - -0.004600525 + - 0.0046920776 + - 0.02558899 + - -0.05319214 + - -0.058013916 + - 0.080444336 + - -0.00068187714 + - 0.031311035 + - 0.032440186 + - -0.051086426 + - -0.003534317 + - 0.046325684 + - -0.032440186 + - -0.03894043 + - -0.0071907043 + - -0.004627228 + - -0.01826477 + - -0.027755737 + - 0.040802002 + - 0.019363403 + - -0.009727478 + - 0.0064468384 + - 0.056488037 + - 0.018585205 + - -0.017974854 + - -0.08514404 + - 5.0604343e-5 + - -0.014839172 + - 0.01586914 + - 0.00017666817 + - 0.02267456 + - -0.05105591 + - 0.007785797 + - -0.02684021 + - 0.0064849854 + - 0.014411926 + - 0.0013427734 + - -0.012611389 + - 0.043701172 + - 0.012290955 + - -0.030731201 + - 0.034729004 + - 0.015289307 + - -0.037475586 + - -0.030838013 + - 0.010009766 + - -0.028244019 + - 0.051635742 + - 0.01725769 + - 0.013977051 + - 0.008102417 + - 0.028121948 + - 0.02079773 + - 0.0027256012 + - 0.009185791 + - 0.0016012192 + - -0.038116455 + - -0.008331299 + - -0.028076172 + - 0.018463135 + - -0.02154541 + - 0.021240234 + - 0.023376465 + - 0.02961731 + - -0.028305054 + - -0.023101807 + - -0.010681152 + - -0.0072021484 + - -0.04321289 + - 0.0058517456 + - 0.030792236 + - -0.021102905 + - 0.050933838 + - 0.0060157776 + - 0.0128479 + - 0.025146484 + - -0.006099701 + - 0.023345947 + - 0.023971558 + - 0.015510559 + - -0.009895325 + - -0.04071045 + - 0.049835205 + - 0.0053100586 + - -0.028930664 + - 0.017578125 + - -0.0048217773 + - -0.0042762756 + - -0.034240723 + - -0.03253174 + - 0.035827637 + - 0.01574707 + - 0.034851074 + - 0.070129395 + - 0.011749268 + - -0.009223938 + - 0.02470398 + - -0.005115509 + - 0.016723633 + - 0.04937744 + - -0.032928467 + - 0.031280518 + - -0.00023400784 + - 0.010169983 + - -0.01071167 + - 0.010520935 + - 0.022338867 + - -0.0259552 + - 0.044769287 + - 0.0070610046 + - -0.012451172 + - -0.04156494 + - 0.047088623 + - -0.017578125 + - 0.012741089 + - -0.016479492 + - 0.0023078918 + - -0.008331299 + - 0.021591187 + - 0.01473999 + - -0.018081665 + - 0.033081055 + - -0.057556152 + - 0.008621216 + - 0.013954163 + - -0.009742737 + - -0.015548706 + - 0.015281677 + - -0.005958557 + - 0.0065307617 + - 0.01979065 + - 0.041778564 + - -0.02684021 + - 0.027709961 + - -0.07672119 + - 0.023406982 + - -0.037902832 + - 0.035339355 + - -0.021881104 + - 0.056732178 + - 0.03466797 + - 0.0059318542 + - -0.058654785 + - 0.025375366 + - 0.015029907 + - 0.002380371 + - -0.024230957 + - 0.014541626 + - -0.006641388 + - -0.01864624 + - 0.012290955 + - 0.0007929802 + - -0.009277344 + - 0.04953003 + - -0.004081726 + - 0.0029258728 + - -0.017181396 + - 0.0074920654 + - -0.0001707077 + - 0.04220581 + - 0.008972168 + - -0.0071525574 + - 0.0015583038 + - 0.034362793 + - -0.019058228 + - 0.013626099 + - 0.022613525 + - -0.0061149597 + - 0.017669678 + - 0.015586853 + - 0.034973145 + - 0.02217102 + - -0.045013428 + - -0.009864807 + - 0.07244873 + - 0.010177612 + - 0.029724121 + - -0.018829346 + - -0.034057617 + - -0.018859863 + - 0.059936523 + - -0.0076408386 + - 0.021331787 + - -0.013786316 + - 0.015281677 + - 0.016235352 + - -0.039855957 + - -0.02748108 + - -0.033416748 + - 0.016174316 + - 0.026489258 + - 0.0049095154 + - -0.026000977 + - 0.00831604 + - -0.019851685 + - -0.021408081 + - 0.023010254 + - 0.030075073 + - 0.0335083 + - -0.05493164 + - 0.019515991 + - -0.020401001 + - -0.0061073303 + - 0.018997192 + - 0.020126343 + - -0.027740479 + - -0.038116455 + - 0.0052948 + - -0.008613586 + - -0.016494751 + - -0.001247406 + - 0.022644043 + - 0.008300781 + - -0.02104187 + - 0.016693115 + - -0.0032901764 + - 0.012046814 + - -0.023468018 + - -0.007259369 + - 0.031234741 + - 0.06604004 + - 0.051635742 + - 0.0009441376 + - -0.006084442 + - 0.025619507 + - -0.006881714 + - 0.02999878 + - 0.050964355 + - 0.017715454 + - -0.024856567 + - -0.010070801 + - 0.05319214 + - -0.03652954 + - 0.011810303 + - -0.011978149 + - 0.013046265 + - -0.016662598 + - 0.017166138 + - -0.005542755 + - -0.07989502 + - 0.029220581 + - 0.056488037 + - 0.015914917 + - -0.011184692 + - -0.018203735 + - -0.03894043 + - -0.026626587 + - 0.0010070801 + - -0.07397461 + - -0.060333252 + - 0.046020508 + - -0.017440796 + - -0.020385742 + - -0.0211792 + - -0.018295288 + - -0.01802063 + - 0.003211975 + - -0.012969971 + - -0.034576416 + - -0.022079468 + - 0.034606934 + - -0.022079468 + - -0.02154541 + - -0.0039367676 + - 0.015419006 + - -0.027023315 + - 0.024642944 + - -0.0007047653 + - -0.008293152 + - 0.02708435 + - 0.05267334 + - 0.010177612 + - 0.017822266 + - -0.021759033 + - -0.051116943 + - -0.02583313 + - -0.06427002 + - 0.03213501 + - -0.009635925 + - -0.04547119 + - 0.018997192 + - -0.024032593 + - -0.011024475 + - 0.033935547 + - 0.050842285 + - 0.011009216 + - -0.002527237 + - 0.04852295 + - 0.038360596 + - -0.035583496 + - -0.021377563 + - -0.016052246 + - -0.072143555 + - 0.03665161 + - 0.02897644 + - -0.03842163 + - -0.00068187714 + - 0.022415161 + - -0.0030879974 + - 0.043762207 + - 0.05392456 + - -0.0362854 + - -0.04647827 + - -0.034057617 + - -0.040374756 + - -0.03942871 + - 0.030761719 + - -0.068115234 + - 0.011329651 + - 0.011413574 + - -0.012435913 + - 0.01576233 + - 0.022766113 + - 0.05609131 + - 0.07092285 + - 0.017593384 + - 0.024337769 + - 0.027923584 + - 0.06994629 + - 0.00655365 + - -0.020248413 + - -0.03945923 + - -0.0491333 + - -0.049194336 + - 0.020050049 + - 0.010910034 + - 0.013511658 + - 0.01676941 + - -0.041900635 + - -0.046142578 + - 0.012268066 + - 0.026748657 + - -0.036499023 + - 0.021713257 + - -0.036590576 + - 0.014411926 + - 0.029174805 + - -0.029388428 + - 0.04119873 + - 0.04852295 + - 0.007068634 + - -0.00090408325 + - 0.0048332214 + - -0.015777588 + - -0.01499939 + - -0.0068206787 + - -0.02708435 + - 0.010543823 + - 0.004085541 + - -0.026901245 + - -0.0045661926 + - 0.0061912537 + - -0.0014343262 + - 0.028945923 + - -0.03552246 + - 0.030441284 + - -0.029281616 + - 0.050628662 + - -0.033599854 + - -0.085510254 + - -0.052520752 + - -0.07507324 + - -0.008003235 + - -0.026382446 + - -0.078063965 + - -0.025161743 + - -0.025421143 + - -0.0073165894 + - 0.01889038 + - -0.05999756 + - -0.0051612854 + - 0.0072517395 + - -0.011497498 + - 0.01687622 + - 0.002231598 + - -0.034423828 + - -0.0013084412 + - -0.012413025 + - 0.008888245 + - 0.017486572 + - -0.03353882 + - 0.0069885254 + - -0.02722168 + - 0.02015686 + - -0.04510498 + - -0.038726807 + - -0.0031356812 + - 0.033233643 + - 0.025268555 + - -0.015106201 + - 0.02407837 + - -0.00024700165 + - -0.07409668 + - -0.012367249 + - 0.014785767 + - -0.04486084 + - 0.074401855 + - -0.020690918 + - -0.025222778 + - 0.029083252 + - -0.018997192 + - 0.0017557144 + - 0.03857422 + - -0.020111084 + - 0.03338623 + - -0.028213501 + - 0.0063705444 + - -0.010124207 + - -0.03112793 + - -0.03286743 + - 0.0046043396 + - -0.0052223206 + - 0.00023317337 + - 0.0423584 + - 0.028030396 + - 0.0005788803 + - -0.02708435 + - 0.006324768 + - 0.019821167 + - -0.0042686462 + - -0.026428223 + - -0.02293396 + - 0.036590576 + - -0.023376465 + - -0.022537231 + - 0.032226562 + - -0.020629883 + - 0.017929077 + - 0.0440979 + - -0.014038086 + - -0.022216797 + - 0.020446777 + - -0.05496216 + - -0.018859863 + - -0.039855957 + - 0.008300781 + - 0.07281494 + - 0.018295288 + - 0.042114258 + - 0.005519867 + - 0.017990112 + - -0.008773804 + - 0.011123657 + - -0.008239746 + - -0.045532227 + - 0.026153564 + - -0.015853882 + - 0.027557373 + - -0.049041748 + - -0.0022945404 + - -0.009399414 + - -0.045898438 + - 0.05053711 + - 0.038513184 + - -0.031799316 + - 0.012329102 + - 0.024871826 + - 0.04348755 + - -0.04788208 + - 0.01423645 + - 0.021240234 + - 0.05493164 + - 0.008956909 + - -0.056243896 + - 0.032043457 + - -0.01574707 + - -0.01285553 + - -0.009498596 + - -0.018951416 + - -0.029556274 + - 0.0069274902 + - -0.032348633 + - -0.022445679 + - -0.00093603134 + - -0.015808105 + - -0.027175903 + - 0.014091492 + - 0.025665283 + - -0.023468018 + - -0.03250122 + - -0.0004544258 + - 0.042633057 + - -0.06036377 + - -0.039611816 + - -0.042938232 + - -0.02418518 + - -0.0703125 + - 0.045135498 + - -0.001036644 + - -0.017913818 + - -0.004043579 + - 0.0138549805 + - -0.02532959 + - 0.010765076 + - 0.021575928 + - 0.013114929 + - 0.033935547 + - -0.010574341 + - 0.017990112 + - -0.026107788 + - -0.029144287 + - -0.046569824 + - -0.0030517578 + - -0.022994995 + - -0.017471313 + - -0.0070495605 + - -9.846687e-5 + - 0.029281616 + - 0.017440796 + - 0.045532227 + - 0.025650024 + - 0.0491333 + - -0.013145447 + - 0.070129395 + - -0.0051879883 + - -0.04043579 + - 0.023864746 + - 0.016830444 + - -0.014152527 + - -0.06359863 + - -0.005065918 + - -0.009880066 + - -0.0034618378 + - -0.081726074 + - -0.0289917 + - -0.007461548 + - -0.0013504028 + - 0.020523071 + - 0.0076446533 + - -0.011650085 + - 0.014549255 + - 0.010955811 + - 0.02180481 + - -0.027572632 + - -0.012252808 + - 0.009033203 + - -0.0048980713 + - 0.031173706 + - -0.020309448 + - 0.022979736 + - -0.013900757 + - -0.004108429 + - 0.018325806 + - -0.031402588 + - 0.01737976 + - 0.03201294 + - -0.02508545 + - -0.015625 + - -0.04626465 + - -0.014656067 + - 0.016036987 + - -0.030639648 + - 0.041748047 + - -0.0032978058 + - -0.03277588 + - 0.037719727 + - 0.023788452 + - -0.008140564 + - -0.041809082 + - 0.034698486 + - -0.022994995 + - -0.009979248 + - -0.03729248 + - -0.0904541 + - 0.00028443336 + - 0.080566406 + - -0.035125732 + - -0.054229736 + - -0.017700195 + - 0.060668945 + - 0.008979797 + - 0.015052795 + - -0.0072364807 + - -0.001490593 + - 0.0065231323 + - -0.014579773 + - 0.016067505 + - -0.020339966 + - -0.020217896 + - 0.02909851 + - 0.050628662 + - 0.04510498 + - -0.01979065 + - 0.008918762 + - 0.031799316 + - 0.031951904 + - -0.016906738 + - 0.031036377 + - 0.0040664673 + - -0.046905518 + - -0.04928589 + - 0.044403076 + - -0.0524292 + - -0.012832642 + - 0.049835205 + - 0.0040283203 + - -0.012649536 + - 0.06878662 + - -0.02859497 + - -0.014137268 + - 0.0036144257 + - -0.06262207 + - 0.046813965 + - 0.024978638 + - 0.0017976761 + - -0.032409668 + - -0.004108429 + - -0.013557434 + - -0.07196045 + - 0.026733398 + - 0.0024261475 + - -0.022735596 + - -0.0022182465 + - -0.0064315796 + - -0.03652954 + - 0.04135132 + - -0.032562256 + - 0.004524231 + - 0.020019531 + - -0.0113220215 + - -0.071777344 + - -0.03451538 + - 0.0022583008 + - -0.06512451 + - -0.005317688 + - 0.020248413 + - -0.036712646 + - 0.005809784 + - -0.018951416 + - -0.0026855469 + - 0.027572632 + - -0.00036668777 + - 0.0073623657 + - -0.018829346 + - 0.009101868 + - 0.051971436 + - 0.023132324 + - -0.022537231 + - 0.00932312 + - 0.00944519 + - 0.014183044 + - 0.020889282 + - 0.0032844543 + - -0.0073776245 + - -0.05807495 + - -0.032440186 + - 0.033996582 + - 0.0423584 + - 0.014259338 + - 0.061676025 + - -0.02154541 + - -0.031982422 + - 0.005493164 + - -0.01512146 + - 0.023101807 + - -0.011383057 + - -0.059539795 + - 0.021820068 + - 0.015487671 + - -0.004875183 + - -0.015640259 + - 0.015319824 + - -0.0054359436 + - -0.026229858 + - 0.0061454773 + - -0.032348633 + - 0.038513184 + - 0.004840851 + - -0.016021729 + - -0.017608643 + - -0.019577026 + - -0.009178162 + - 0.045013428 + - -0.01007843 + - 0.022323608 + - 0.034179688 + - 0.00566864 + - 0.055511475 + - -0.033355713 + - -0.019317627 + - -8.481741e-5 + - 0.017547607 + - -0.053344727 + - 0.012229919 + - 0.022384644 + - 0.018051147 + - 0.010734558 + - 0.004501343 + - -0.05911255 + - -0.0030918121 + - -0.0513916 + - -0.0050086975 + - -0.01600647 + - 0.05343628 + - -0.0008234978 + - 0.07293701 + - -0.056610107 + - -0.06549072 + - -0.01776123 + - -0.0022678375 + - 0.023239136 + - 0.01020813 + - -0.005153656 + - -0.00630188 + - -0.009880066 + - 0.022109985 + - 0.033203125 + - -0.03567505 + - -0.014129639 + - 0.015625 + - 0.022888184 + - -0.038726807 + - -0.026321411 + - -0.007259369 + - 0.005924225 + - 0.0010814667 + - 0.06665039 + - -0.008880615 + - 0.053771973 + - 0.062194824 + - 0.018981934 + - 0.022338867 + - 0.01361084 + - 0.025604248 + - 0.022109985 + - 0.0044288635 + - -0.008331299 + - -0.0019416809 + - 0.006454468 + - -0.045013428 + - -0.02519226 + - -0.012268066 + - -0.032165527 + - 7.2181225e-5 + - -0.021575928 + - -0.006324768 + - 0.029785156 + - 0.0063438416 + - -0.01210022 + - 0.029403687 + - 0.00592041 + - 0.008369446 + - 0.00818634 + - -0.04498291 + - -0.041809082 + - 0.0078086853 + - -0.05935669 + - -0.043518066 + - 0.007270813 + - 0.060424805 + - 0.033996582 + - 0.055908203 + - 0.013755798 + - 0.03982544 + - 0.014640808 + - -0.01373291 + - 0.033325195 + - -0.0047073364 + - 0.015899658 + - -0.00043344498 + - 0.022338867 + - -0.007095337 + - 0.02949524 + - 0.042633057 + - 0.030670166 + - 0.022415161 + - -0.0033683777 + - 0.018814087 + - -0.013031006 + - 0.031951904 + - 0.022094727 + - -0.009986877 + - 0.025665283 + - -0.0138168335 + - 0.049743652 + - 0.024307251 + - 0.0088272095 + - -0.03479004 + - 0.07318115 + - 0.009849548 + - 0.051635742 + - -0.05331421 + - -0.053131104 + - -0.0044898987 + - 0.029342651 + - 0.005596161 + - 0.044189453 + - -0.042388916 + - -0.012939453 + - -0.0007529259 + - -0.06088257 + - 0.036010742 + - -0.02355957 + - 0.004497528 + - -0.0023822784 + - -0.053588867 + - -0.04168701 + - -0.017868042 + - -0.01927185 + - -0.06011963 + - 0.028884888 + - 0.061401367 + - -0.005584717 + - 0.014823914 + - -0.02255249 + - 4.631281e-5 + - 0.039031982 + - -0.0055389404 + - 0.007194519 + - 0.0037631989 + - 0.008834839 + - 0.018692017 + - 0.033111572 + - -0.056274414 + - -0.021774292 + - 0.04727173 + - -0.03265381 + - 0.022140503 + - 0.027801514 + - 0.004043579 + - -0.016525269 + - -0.041809082 + - 0.024520874 + - 0.008529663 + - 0.049072266 + - 0.033447266 + - -0.028839111 + - 0.048675537 + - 0.021453857 + - -0.08087158 + - 0.034606934 + - -0.002910614 + - 0.012176514 + - 0.035705566 + - 0.040161133 + - -0.02355957 + - -0.01626587 + - -0.033721924 + - -0.013893127 + - -0.04156494 + - 0.06719971 + - 0.043151855 + - -0.033813477 + - 0.028045654 + - 0.0029525757 + - -0.022033691 + - -0.093811035 + - -0.0056114197 + - 0.00026154518 + - 0.058746338 + - -0.05065918 + - 0.02897644 + - -0.01550293 + - -0.02947998 + - -0.018249512 + - 0.034942627 + - -0.04574585 + - -0.037109375 + - -0.006160736 + - 0.006149292 + - -0.0012207031 + - -0.042907715 + - -0.016448975 + - 0.0052719116 + - 0.036590576 + - -0.045318604 + - -0.04220581 + - -0.018859863 + - -0.031021118 + - 0.06439209 + - -0.0056533813 + - -0.037200928 + - -0.026550293 + - 0.027786255 + - -0.028427124 + - 0.09161377 + - -0.0088272095 + - -0.003643036 + - -0.053253174 + - -0.01826477 + - -0.016540527 + - -0.012535095 + - -0.03942871 + - -0.0049095154 + - 0.031311035 + - 0.049468994 + - -0.066589355 + - -0.05029297 + - 7.5519085e-5 + - -0.0017404556 + - -0.013214111 + - -0.03756714 + - -0.009147644 + - -0.025466919 + - 0.026672363 + - 0.020965576 + - -0.0073432922 + - 0.0011005402 + - -0.04937744 + - -0.018463135 + - 0.00274086 + - -0.013252258 + - 0.0126953125 + - -0.077697754 + - 0.014045715 + - 0.00039935112 + - -0.019515991 + - -0.0027618408 + - -0.011672974 + - -0.043884277 + - 0.009231567 + - 0.062805176 + - -0.0137786865 + - -0.026229858 + - -0.034362793 + - -0.015090942 + - 0.016937256 + - 0.030639648 + - -0.02420044 + - 0.02482605 + - -0.0033740997 + - 0.046417236 + - -0.012008667 + - -0.04031372 + - -0.00032520294 + - 0.01525116 + - -0.0066375732 + - 0.0062713623 + - -0.01171875 + - -0.027191162 + - -0.014137268 + - -0.025390625 + - 0.002111435 + - -0.06561279 + - 0.031555176 + - -0.07519531 + - -0.04547119 + - 0.014472961 + - -0.0158844 + - -0.091552734 + - -0.03366089 + - 0.050323486 + - -0.0013589859 + - -0.033203125 + - 0.046539307 + - -0.030288696 + - 0.0046195984 + - 0.049835205 + - 0.02003479 + - -0.004196167 + - 0.013168335 + - -0.016403198 + - 0.01676941 + - -0.00340271 + meta: + api_version: + version: '2' billed_units: - input_tokens: 2 - response_type: embeddings_floats + images: 1 + response_type: embeddings_by_type responses: "200": description: OK From 9f38a9a1a577e16958cbb5010f90e37ea9b62545 Mon Sep 17 00:00:00 2001 From: Michael Date: Fri, 25 Oct 2024 15:42:22 -0400 Subject: [PATCH 2/2] Update cohere-openapi.yaml Signed-off-by: Michael --- cohere-openapi.yaml | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/cohere-openapi.yaml b/cohere-openapi.yaml index 13686555..24ddc9b3 100644 --- a/cohere-openapi.yaml +++ b/cohere-openapi.yaml @@ -8129,7 +8129,9 @@ paths: response = co.embed( - texts=["hello", "goodbye"], model="embed-english-v3.0", input_type="classification" + model="embed-english-v3.0", + texts=["hello", "goodbye"], + input_type="classification" ) print(response) @@ -8147,7 +8149,9 @@ paths: async def main(): response = await co.embed( - texts=["hello", "goodbye"], model="embed-english-v3.0", input_type="classification" + model="embed-english-v3.0", + texts=["hello", "goodbye"], + input_type="classification" ) print(response) @@ -12963,7 +12967,10 @@ paths: response = co.embed( - texts=["hello", "goodbye"], model="embed-english-v3.0", input_type="classification", embedding_types=["float"] + model="embed-english-v3.0", + texts=["hello", "goodbye"], + input_type="classification", + embedding_types=["float"] ) print(response) @@ -12981,7 +12988,9 @@ paths: async def main(): response = await co.embed( - texts=["hello", "goodbye"], model="embed-english-v3.0", input_type="classification" + model="embed-english-v3.0", + texts=["hello", "goodbye"], + input_type="classification" ) print(response)