[ML] Bedrock Cohere does not support InputType, Truncate

[prwhelan]

This currently returns an error:

PUT _inference/text_embedding/multilingual-cohere-v3-search
{
  "service": "amazonbedrock",
  "service_settings": {
    "access_key": "***",
    "secret_key": "***",
    "region": "us-east-1",
    "provider": "Cohere",
    "model": "cohere.embed-multilingual-v3"
  },
  "task_settings": {
    "truncate": "START"
  }
}

POST _inference/text_embedding/multilingual-cohere-v3-search
{
  "input": "The sky above the port was the color of television tuned to a dead channel.",
  "input_type": "search"
}

Though AWS supports it: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-embed.html

[elasticsearchmachine]

Pinging @elastic/ml-core (Team:ML)

[prwhelan]

from elasticsearch import Elasticsearch
import os
import requests
import json

# Initialize Client
es_url = os.getenv("ES_URL", "http://localhost:9200")
es_user = os.getenv("ES_USERNAME", "elastic")
es_password = os.getenv("ES_PASSWORD", "password")

es = Elasticsearch(
    hosts=es_url, basic_auth=(es_user, es_password), verify_certs=False, ssl_show_warn=False, request_timeout=120
)

resp = es.inference.put_model(
    task_type="text_embedding",
    inference_id="multilingual-cohere-v3-search",
    body={
        "service": "amazonbedrock",
        "service_settings": {
            "access_key": accessKey,
            "secret_key": secretKey,
            "region": "us-east-1",
            "provider": "cohere",
            "model": "cohere.embed-multilingual-v3"
        },
        "task_settings": {
            "input_type": "search"
        }
    },
)
print(resp)

{'inference_id': 'multilingual-cohere-v3-search', 'task_type': 'text_embedding', 'service': 'amazonbedrock', 'service_settings': {'region': 'us-east-1', 'model': 'cohere.embed-multilingual-v3', 'provider': 'COHERE', 'rate_limit': {'requests_per_minute': 240}, 'dimensions': 1024, 'similarity': 'dot_product'}, 'task_settings': {'input_type': 'search'}, 'chunking_settings': {'strategy': 'sentence', 'max_chunk_size': 250, 'sentence_overlap': 1}}

resp = es.inference.inference(
    task_type="text_embedding",
    inference_id="multilingual-cohere-v3-search",
    input="The sky above the port was the color of television tuned to a dead channel."
)
print(resp)

{'text_embedding': [{'embedding': [-0.019317627, 0.006565094, 0.0060272217, 0.0206604, -0.049102783, -0.03729248, -0.012084961, -0.013671875, -0.04006958, 0.034057617, 0.036102295, -0.09869385, 0.0501709, -0.0028839111, 0.012519836, -0.003921509, -0.0068893433, -0.050933838, -0.016799927, -0.009361267, -0.0011634827, 0.056732178, 0.057006836, 0.016830444, 0.029541016, 0.036834717, 0.008117676, -0.019134521, 0.06439209, -0.045684814, 0.012863159, -0.030548096, 0.020568848, 0.035736084, -0.019470215, 0.018707275, -0.015266418, -0.053710938, -0.021072388, 0.0703125, -0.06384277, -0.020980835, -0.044769287, 0.042541504, -0.019851685, 0.0021743774, 0.027404785, 0.041259766, -0.054138184, 0.0012512207, 0.018798828, 0.029785156, -0.0003578663, -0.011817932, 0.019332886, 0.05090332, -0.01373291, -0.011306763, 0.041168213, 0.005794525, 0.0075531006, -0.018493652, -0.037872314, 4.1127205e-06, 0.04360962, -0.0026836395, 0.021514893, 0.010238647, -0.013946533, 0.0040359497, 0.013839722, 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resp = es.inference.inference(
    task_type="text_embedding",
    inference_id="multilingual-cohere-v3-search",
    input="The sky above the port was the color of television tuned to a dead channel.",
    task_settings={
        "input_type": "ingest"
    },
)
print(resp)

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[prwhelan]

We are in the process of migrating input_type out of task_settings since all text embedding providers support it, so we will instead support:

POST _inference/text_embedding/multilingual-cohere-v3-search
{
  "input": "The sky above the port was the color of television tuned to a dead channel.",
  "input_type": "search"
}