Add updated Cohere Embedding blueprint (#1950) (#2063)

* Add updated Cohere embedding blueprint

* Add updated Cohere embedding blueprint

* Update Cohere docs

* PR comments

* add missing period

* Remove committed doc

* Embedding updates

(cherry picked from commit 6def544)

Co-authored-by: Tianjing Li <tianjinglimail@gmail.com>

Author: opensearch-trigger-bot[bot]

docs/remote_inference_blueprints/cohere_connector_embedding_blueprint.md

@@ -0,0 +1,324 @@
+### Cohere Embedding Connector Blueprint:
+
+This blueprint will show you how to connect a Cohere embedding model to your Opensearch cluster, including creating a k-nn index and your own Embedding pipeline. You will require a Cohere API key to create a connector.
+
+Cohere currently offers the following Embedding models (with model name and embedding dimensions). Note that only the following have been tested with the blueprint guide.
+
+- embed-english-v3.0 1024
+- embed-english-v2.0 4096
+
+See [Cohere's /embed API docs](https://docs.cohere.com/reference/embed) for more details.
+
+#### 1. Create a connector and model group
+
+##### 1a. Register model group
+
+```json
+POST /_plugins/_ml/model_groups/_register
+
+{
+  "name": "cohere_model_group",
+  "description": "Your Cohere model group"
+}
+```
+
+This request response will return the `model_group_id`, note it down.
+
+##### 1b. Create a connector
+
+See above for all the values the `parameters > model` parameter can take.
+
+```json
+POST /_plugins/_ml/connectors/_create
+{
+  "name": "Cohere Embed Model",
+  "description": "The connector to Cohere's public embed API",
+  "version": "1",
+  "protocol": "http",
+  "credential": {
+    "cohere_key": "<ENTER_COHERE_API_KEY_HERE>"
+  },
+  "parameters": {
+    "model": "<ENTER_MODEL_NAME_HERE>", // Choose a Model from the provided list above
+    "input_type":"search_document",
+    "truncate": "END"
+  },
+  "actions": [
+    {
+      "action_type": "predict",
+      "method": "POST",
+      "url": "https://api.cohere.ai/v1/embed",
+      "headers": {
+        "Authorization": "Bearer ${credential.cohere_key}",
+        "Request-Source": "unspecified:opensearch"
+      },
+      "request_body": "{ \"texts\": ${parameters.texts}, \"truncate\": \"${parameters.truncate}\", \"model\": \"${parameters.model}\", \"input_type\": \"${parameters.input_type}\" }",
+      "pre_process_function": "connector.pre_process.cohere.embedding",
+      "post_process_function": "connector.post_process.cohere.embedding"
+    }
+  ]
+}
+```
+
+This request response will return the `connector_id`, note it down.
+
+##### 1c. Register a model with your connector
+
+You can now register your model with the `model_group_id` and `connector_id` created from the previous steps.
+
+```json
+POST /_plugins/_ml/models/_register
+Content-Type: application/json
+
+{
+    "name": "Cohere Embed Model",
+    "function_name": "remote",
+    "model_group_id": "<MODEL_GROUP_ID>",
+    "description": "Your Cohere Embedding Model",
+    "connector_id": "<CONNECTOR_ID>"
+}
+```
+
+This will create a registration task, the response should look like:
+
+```json
+{
+  "task_id": "9bXpRY0BRil1qhQaUK-u",
+  "status": "CREATED",
+  "model_id": "9rXpRY0BRil1qhQaUK_8"
+}
+```
+
+##### 1d. Deploy model
+
+The last step is to deploy your model. Use the `model_id` returned by the registration request, and run:
+
+```json
+POST /_plugins/_ml/models/<MODEL_ID>/_deploy
+```
+
+This will once again spawn a task to deploy your Model, with a response that will look like:
+
+```json
+{
+  "task_id": "97XrRY0BRil1qhQaQK_c",
+  "task_type": "DEPLOY_MODEL",
+  "status": "COMPLETED"
+}
+```
+
+You can run the GET tasks request again to verify the status.
+
+```json
+GET /_plugins/_ml/tasks/<TASK_ID>
+```
+
+Once this is complete, your Model is deployed and ready!
+
+##### 1e. Test model
+
+You can try this request to test that the Model behaves correctly:
+
+```json
+POST /_plugins/_ml/models/<MODEL_ID_HERE>/_predict
+{
+  "parameters": {
+    "texts": ["Say this is a test"]
+  }
+}
+```
+
+It should return a response similar to this:
+
+```json
+{
+  "inference_results": [
+    {
+      "output": [
+        {
+          "name": "sentence_embedding",
+          "data_type": "FLOAT32",
+          "shape": [
+            1024
+          ],
+          "data": [
+            -0.0024547577,
+            0.0062217712,
+            -0.01675415,
+            -0.020736694,
+            -0.020263672,
+            ... ...
+            0.038635254
+          ]
+        }
+      ],
+      "status_code": 200
+    }
+  ]
+}
+```
+
+#### (Optional) 2. Setup k-NN index and ingestion pipeline
+
+##### 2a. Create your pipeline
+
+It is important that the `field_map` parameter contains all the document fields you'd like to embed as a vector. The key value is the document field name, and the value will be the field containing the embedding.
+
+```json
+PUT /_ingest/pipeline/cohere-ingest-pipeline
+{
+  "description": "Test Cohere Embedding pipeline",
+  "processors": [
+    {
+      "text_embedding": {
+        "model_id": "<MODEL_ID>",
+        "field_map": {
+          "passage_text": "passage_embedding"
+        }
+      }
+    }
+  ]
+}
+```
+
+Sample response:
+
+```json
+{
+  "acknowledged": true
+}
+```
+
+##### 2b. Create a k-NN index
+
+Here `cohere-nlp-index` is the name of your index, you can change it as needed.
+
+````json
+PUT /cohere-nlp-index
+
+{
+  "settings": {
+    "index.knn": true,
+    "default_pipeline": "cohere-ingest-pipeline"
+  },
+  "mappings": {
+    "properties": {
+      "id": {
+      "type": "text"
+      },
+      "passage_embedding": {
+        "type": "knn_vector",
+        "dimension": 1024,
+        "method": {
+          "engine": "lucene",
+          "space_type": "l2",
+          "name": "hnsw",
+          "parameters": {}
+        }
+      },
+      "passage_text": {
+        "type": "text"
+      }
+    }
+  }
+}
+
+Sample response:
+
+```json
+{
+  "acknowledged": true,
+  "shards_acknowledged": true,
+  "index": "cohere-nlp-index"
+}
+````
+
+##### 2c. Testing the index and pipeline
+
+First, you can insert a record:
+
+```json
+PUT /cohere-nlp-index/_doc/1
+{
+  "passage_text": "Hi - Cohere Embeddings are cool!",
+  "id": "c1"
+}
+```
+
+Sample response:
+
+```json
+{
+  "_index": "cohere-nlp-index",
+  "_id": "1",
+  "_version": 1,
+  "result": "created",
+  "_shards": {
+    "total": 2,
+    "successful": 1,
+    "failed": 0
+  },
+  "_seq_no": 0,
+  "_primary_term": 1
+}
+```
+
+The last step is to check that the embeddings were properly created. Notice that the embedding field created corresponds to the `field_map` mapping you defined in step 3a.
+
+```json
+GET /cohere-nlp-index/\_search
+
+{
+  "query": {
+    "match_all": {}
+  }
+}
+```
+
+Sample response:
+
+```json
+{
+  "took": 2,
+  "timed_out": false,
+  "_shards": {
+    "total": 1,
+    "successful": 1,
+    "skipped": 0,
+    "failed": 0
+  },
+  "hits": {
+    "total": {
+      "value": 1,
+      "relation": "eq"
+    },
+    "max_score": 1,
+    "hits": [
+      {
+        "_index": "cohere-nlp-index",
+        "_id": "1",
+        "_score": 1,
+        "_source": {
+          "passage_text": "Hi - Cohere Embeddings are cool!",
+          "passage_embedding": [
+            0.02494812,
+            -0.009391785,
+            -0.015716553,
+            -0.051849365,
+            -0.015930176,
+            -0.024734497,
+            -0.028518677,
+            -0.008323669,
+            -0.008323669,
+            .............
+
+          ],
+          "id": "c1"
+        }
+      }
+    ]
+  }
+}
+```
+
+Congratulations! You've successfully created your ingestion pipeline.