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Fix conversational search formatting (#5345)

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Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>
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kolchfa-aws 2023-10-19 16:13:35 -04:00 committed by GitHub
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_ml-commons-plugin/conversational-search.md
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@ -40,7 +40,6 @@ In the `conversation-meta` index, you can customize the `name` field to make it
}
}
```
{% include copy-curl.html %}
### `conversation-interactions` index
@ -73,7 +72,6 @@ The `conversation-interactions` index creates a clean interaction abstraction an
}
}
```
{% include copy-curl.html %}
## Working with conversations and interactions
@ -169,7 +167,7 @@ The Memory API responds with the most recent conversation, as indicated in the `
If there are fewer conversations than the number set in `max_results`, the response only returns the number of conversations that exist. Lastly, `next_token` provides an ordered position of the sorted list of conversations. When a conversation is added between subsequent GET conversation calls, one of the listed conversations will be duplicated in the results, for example:
```json
```plaintext
GetConversations -> [BCD]EFGH
CreateConversation -> ABCDEFGH
GetConversations(next_token=3) -> ABC[DEF]GH
@ -249,73 +247,74 @@ Use the following steps to set up an HTTP connector using the OpenAI GPT 3.5 mod
1. Use the Connector API to create the HTTP connector:
```json
POST /_plugins/_ml/connectors/_create
{
"name": "OpenAI Chat Connector",
"description": "The connector to public OpenAI model service for GPT 3.5",
"version": 2,
"protocol": "http",
"parameters": {
"endpoint": "[api.openai.com](http://api.openai.com/)",
"model": "gpt-3.5-turbo",
"temperature": 0
```json
POST /_plugins/_ml/connectors/_create
{
"name": "OpenAI Chat Connector",
"description": "The connector to public OpenAI model service for GPT 3.5",
"version": 2,
"protocol": "http",
"parameters": {
"endpoint": "[api.openai.com](http://api.openai.com/)",
"model": "gpt-3.5-turbo",
"temperature": 0
},
"credential": {
"openAI_key": "<your OpenAI key>"
},
"credential": {
"openAI_key": "<your OpenAI key>"
},
"actions": [
{
"action_type": "predict",
"method": "POST",
"url": "[https://$](https://%24/){parameters.endpoint}/v1/chat/completions",
"headers": {
"Authorization": "Bearer ${credential.openAI_key}"
},
"request_body": "{ \"model\": \"${parameters.model}\", \"messages\": ${parameters.messages}, \"temperature\": $ {parameters.temperature} }"
}
]
}
```
"actions": [
{
"action_type": "predict",
"method": "POST",
"url": "[https://$](https://%24/){parameters.endpoint}/v1/chat/completions",
"headers": {
"Authorization": "Bearer ${credential.openAI_key}"
},
"request_body": "{ \"model\": \"${parameters.model}\", \"messages\": ${parameters.messages}, \"temperature\": $ {parameters.temperature} }"
}
]
}
```
{% include copy-curl.html %}
2. Create a new model group for the connected model. You'll use the `model_group_id` returned by the Register API to register the model:
1. Create a new model group for the connected model. You'll use the `model_group_id` returned by the Register API to register the model:
```json
POST /_plugins/_ml/model_group/_register
{
"name": "public_model_group",
"description": "This is a public model group"
}
```
{% include copy-curl.html %}
```json
POST /_plugins/_ml/model_group/_register
{
"name": "public_model_group",
"description": "This is a public model group"
}
```
{% include copy-curl.html %}
3. Register and deploy the model using the `connector_id` from the Connector API response in Step 1 and the `model_group_id` returned in Step 2:
1. Register and deploy the model using the `connector_id` from the Connector API response in Step 1 and the `model_group_id` returned in Step 2:
```json
POST /_plugins/_ml/models/_register
{
"name": "openAI-gpt-3.5-turbo",
"function_name": "remote",
"model_group_id": "fp-hSYoBu0R6vVqGMnM1",
"description": "test model",
"connector_id": "f5-iSYoBu0R6vVqGI3PA"
}
```
{% include copy-curl.html %}
```json
POST /_plugins/_ml/models/_register
{
"name": "openAI-gpt-3.5-turbo",
"function_name": "remote",
"model_group_id": "fp-hSYoBu0R6vVqGMnM1",
"description": "test model",
"connector_id": "f5-iSYoBu0R6vVqGI3PA"
}
```
{% include copy-curl.html %}
4. With the model registered, use the `task_id` returned in the registration response to get the `model_id`. You'll use the `model_id` to deploy the model to OpenSearch:
1. With the model registered, use the `task_id` returned in the registration response to get the `model_id`. You'll use the `model_id` to deploy the model to OpenSearch:
```json
GET /_plugins/_ml/tasks/<task_id>
```
{% include copy-curl.html %}
```json
GET /_plugins/_ml/tasks/<task_id>
```
{% include copy-curl.html %}
5. Using the `model_id` from step 4, deploy the model:
1. Using the `model_id` from step 4, deploy the model:
```json
POST /_plugins/_ml/models/<model_id>/_deploy
```
{% include copy-curl.html %}
```json
POST /_plugins/_ml/models/<model_id>/_deploy
```
{% include copy-curl.html %}
### Setting up the pipeline