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Co-authored-by: Nathan Bower <[email protected]> Signed-off-by: kolchfa-aws <[email protected]>
opensearch-project · Jun 21, 2024 · 468050c · 468050c
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diff --git a/_ml-commons-plugin/api/model-apis/register-model.md b/_ml-commons-plugin/api/model-apis/register-model.md
@@ -263,10 +263,10 @@ Field | Data type | Description
 `type` | String | The guardrail type. Valid values are [`local_regex`](#example-request-regex-and-stopword-validation) and [`model`](#example-request-guardrail-model-validation). Using `local_regex`, you can specify a regular expression or stop words. Using `model`, you can specify a guardrail model. For more information, see [Guardrails]({{site.url}}{{site.baseurl}}/ml-commons-plugin/remote-models/guardrails/). 
 `input_guardrail`| Object |  The guardrail for the model input. 
 `output_guardrail`| Object |  The guardrail for the model output. 
-`stop_words`| Object | The list of indexes containing stopwords used for the model input/output validation. If the model prompt/response contains a stopword contained in any of the indexes, the predict request on this model is rejected. 
+`stop_words`| Object | The list of indexes containing stopwords used for model input/output validation. If the model prompt/response contains a stopword contained in any of the indexes, then the predict request on the model is rejected. 
 `index_name`| Object | The name of the index storing the stopwords. 
 `source_fields`| Object | The name of the field storing the stopwords. 
-`regex`| Object |  A regular expression used for input/output validation. If the model prompt/response matches the regular expression, the predict request on this model is rejected. 
+`regex`| Object |  A regular expression used for input/output validation. If the model prompt/response matches the regular expression, then the predict request on the model is rejected. 
 `model_id`| String  | The guardrail model used to validate user input and LLM output. 
 `response_filter`| String | The dot path of the field containing the guardrail model response. 
 `response_validation_regex`| String | The regular expression used to validate the guardrail model response.     

diff --git a/_ml-commons-plugin/remote-models/guardrails.md b/_ml-commons-plugin/remote-models/guardrails.md
@@ -306,12 +306,12 @@ Guardrails are also triggered when a prompt matches the supplied regular express
 {: .label .label-purple }
 
 For more advanced validation, you can configure a guardrail model---a separate LLM whose purpose is to validate the user input and the LLM output. In this example, you'll configure two models:
-- Chat model: An Anthropic Claude model hosted on Amazon Bedrock, whose purpose is to hold a conversation with a user
-- Guardrail model: An OpenAI model, whose purpose is to validate the user input and Anthropic Claude output.
+- Chat model: An Anthropic Claude model hosted on Amazon Bedrock whose purpose is to hold a conversation with a user.
+- Guardrail model: An OpenAI model whose purpose is to validate the user input and Anthropic Claude output.
 
 ### Step 1: Create a connector for the guardrail model
 
-First, create a connector to the OpenAI guardrail model. Note that the OpenAI prompt instructs the model to respond only with the words `accept` or `reject`, depending on whether the input/output is acceptable. Additionally, the request contains the `response_filter` parameter that specifies the field in which the guardrail model will provide the validation result: 
+First, create a connector to the OpenAI guardrail model. Note that the OpenAI prompt instructs the model to respond only with the words `accept` or `reject`, depending on whether the input/output is acceptable. Additionally, the request contains the `response_filter` parameter, which specifies the field in which the guardrail model will provide the validation result: 
 
 ```json
 POST /_plugins/_ml/connectors/_create
@@ -346,7 +346,7 @@ POST /_plugins/_ml/connectors/_create
 ```
 {% include copy-curl.html %}
 
-The response contains the connector ID that you'll use in the next steps:
+The response contains the connector ID used in the next steps:
 
 ```json
 {
@@ -367,7 +367,7 @@ POST /_plugins/_ml/model_groups/_register
 ```
 {% include copy-curl.html %}
 
-The response contains the model group ID that you'll use to register a model to this model group:
+The response contains the model group ID used to register a model to this model group:
 
 ```json
 {
@@ -430,7 +430,7 @@ When the operation is complete, the state changes to `COMPLETED`:
 
 ### Step 4 (Optional): Test the guardrail model
 
-You can test the guardrail model user input validation by sending requests that do an do not contain offensive words. 
+You can test the guardrail model user input validation by sending requests that do and do not contain offensive words. 
 
 First, send a request that does not contain offensive words:
 
@@ -538,7 +538,7 @@ POST /_plugins/_ml/connectors/_create
 ```
 {% include copy-curl.html %}
 
-The response contains the connector ID that you'll use in the next steps:
+The response contains the connector ID used in the next steps:
 
 ```json
 {
@@ -548,7 +548,7 @@ The response contains the connector ID that you'll use in the next steps:
 
 ### Step 6: Register and deploy the chat model with guardrails
 
-To register and deploy the Anthropic Claude chat model, send the following request. Note that the `guardrails` object contains a `response_validation_regex` parameter, which specifies to only treat the input/output as valid if the guardrail model responds with a variant of the word `accept`:
+To register and deploy the Anthropic Claude chat model, send the following request. Note that the `guardrails` object contains a `response_validation_regex` parameter that specifies to only treat the input/output as valid if the guardrail model responds with a variant of the word `accept`:
 
 ```json
 POST /_plugins/_ml/models/_register?deploy=true
@@ -585,7 +585,7 @@ OpenSearch returns the task ID of the register operation and the model ID of the
 
 ### Step 7 (Optional): Test the chat model with guardrails
 
-You can test the Anthropic Claude chat model with guardrails by sending predict requests that do an do not contain offensive words. 
+You can test the Anthropic Claude chat model with guardrails by sending predict requests that do and do not contain offensive words. 
 
 First, send a request that does not contain offensive words: