llm chat complete doc

docs/reference-docs/ai-tasks/llm-chat-complete.md

@@ -0,0 +1,106 @@
+---
+sidebar_position: 10
+---
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# LLM Chat Complete
+
+A system task to complete the chat query that is designed to direct the model's behavior accurately, preventing any deviation from the objective.
+
+## Definitions
+
+```json
+   {
+     "name": "llm_chat_complete",
+     "taskReferenceName": "llm_chat_complete_ref",
+     "inputParameters": {
+       "llmProvider": "openai",
+       "model": "gpt-4",
+       "instructions": "your-prompt-template",
+       "messages": [
+         {
+           "role": "user",
+           "message": "${workflow.input.text}"
+         }
+       ],
+       "temperature": 0.1,
+       "topP": 0.2,
+       "maxTokens": 4,
+       "stopWords": "and"
+     },
+     "type": "LLM_CHAT_COMPLETE"
+   }
+```
+
+## Input Parameters
+
+| Parameter | Description |
+| --------- | ----------- |
+| llmProvider | Choose the required LLM provider. You can only choose providers to which you have access for at least one model from that provider.<br/><br/>**Note:**If you haven’t configured your AI / LLM provider on your Orkes console, navigate to the **Integrations** tab and configure your required provider. Refer to this doc on [how to integrate the LLM providers with Orkes console and provide access to required groups](https://orkes.io/content/category/integrations/ai-llm). | 
+| model | Choose from the available language model for the chosen LLM provider. You can only choose models for which you have access.<br/><br/>For example, If your LLM provider is Azure Open AI & you’ve configured *text-davinci-003* as the language model, you can choose it under this field. |
+| instructions | Set the ground rule/instructions for the chat so the model responds to only specific queries and will not deviate from the objective.<br/><br/>Under this field, choose the AI prompt created. You can only use the prompts for which you have access.<br/><br/>**Note:**If you haven’t created an AI prompt for your language model, refer to this documentation on [how to create AI Prompts in Orkes Conductor and provide access to required groups](https://orkes.io/content/reference-docs/ai-tasks/prompt-template). |
+| messages | Choose the role and messages to complete the chat query.<p align="center"><img src="/content/img/llm-chat-complete-messages.png" alt="Role and messages in LLM Chat complete task" width="50%" height="auto"></img></p><ul><li>Under ‘Role,’ choose the required role for the chat completion. It can take values such as *user*, *assistant*, *system*, or *human*.<ul><li>The roles “user” and “human” represent the user asking questions or initiating the conversation.</li><li>The roles “assistant” and “system” refer to the model responding to the user queries.</li></ul></li><li>Under “Message”, choose the corresponding input to be provided. It can also be [passed as variables](https://orkes.io/content/developer-guides/passing-inputs-to-task-in-conductor). </li></ul> | 
+| temperature | A parameter to control the randomness of the model’s output. Higher temperatures, such as 1.0, make the output more random and creative. Whereas a lower value makes the output more deterministic and focused.<br/><br/>Example: If you're using a text blurb as input and want to categorize it based on its content type, opt for a lower temperature setting. Conversely, if you're providing text inputs and intend to generate content like emails or blogs, it's advisable to use a higher temperature setting. |
+| stopWords | Provide the stop words to be omitted during the text generation process.<br/><br/>In LLM, stop words may be filtered out or given less importance during the text generation process to ensure that the generated text is coherent and contextually relevant. |
+| topP | Another parameter to control the randomness of the model’s output. This parameter defines a probability threshold and then chooses tokens whose cumulative probability exceeds this threshold.<br/><br/>For example: Imagine you want to complete the sentence: “She walked into the room and saw a ______.” Now, the top 4 words the LLM model would consider based on the highest probabilities would be:<ul><li>Cat - 35%</li><li>Dog - 25% </li><li>Book - 15% </li><li>Chair - 10%</li></ul>If you set the top-p parameter to 0.70, the AI will consider tokens until their cumulative probability reaches or exceeds 70%. Here's how it works:<ul><li>Adding "Cat" (35%) to the cumulative probability.</li><li>Adding "Dog" (25%) to the cumulative probability, totaling 60%.</li><li>Adding "Book" (15%) to the cumulative probability, now at 75%.</li></ul>At this point, the cumulative probability is 75%, exceeding the set top-p value of 70%. Therefore, the AI will randomly select one of the tokens from the list of "Cat," "Dog," and "Book" to complete the sentence because these tokens collectively account for approximately 75% of the likelihood. |
+| maxTokens | The maximum number of tokens to be generated by the LLM and returned as part of the result. A token should be approximately 4 characters. |
+
+## Output Parameters
+
+The task output displays the completed chat by the LLM.
+
+## Examples
+
+<Tabs>
+<TabItem value="UI" label="UI" className="paddedContent">
+
+<div className="row">
+<div className="col col--4">
+
+<br/>
+<br/>
+
+1. Add task type **LLM Chat Complete**.
+2. Choose the LLM provider, model & prompt template.
+3. Provide the input parameters.
+
+</div>
+<div className="col">
+<div className="embed-loom-video">
+
+<p><img src="/content/img/llm-chat-complete-ui-method.png" alt="LLM Chat Complete Task" width="500" height="auto"/></p>
+
+</div>
+</div>
+</div>
+
+
+
+</TabItem>
+ <TabItem value="JSON" label="JSON Example">
+
+```json
+{
+     "name": "llm_chat_complete",
+     "taskReferenceName": "llm_chat_complete_ref",
+     "inputParameters": {
+       "llmProvider": "openai",
+       "model": "gpt-4",
+       "instructions": "your-prompt-template",
+       "messages": [
+         {
+           "role": "user",
+           "message": "${workflow.input.text}"
+         }
+       ],
+       "temperature": 0.1,
+       "topP": 0.2,
+       "maxTokens": 4,
+       "stopWords": "and"
+     },
+     "type": "LLM_CHAT_COMPLETE"
+   }
+```
+</TabItem>
+</Tabs>

docs/reference-docs/ai-tasks/llm-generate-embeddings.md

@@ -29,7 +29,7 @@ A system task to generate embeddings from the input data provided. Embeddings ar
 
 ## Input Parameters
 
-| Attribute | Description |
+| Parameter | Description |
 | --------- | ----------- |
 | llmProvider | Choose the required LLM provider. You can only choose providers to which you have access for at least one model from that provider.<br/><br/>**Note**:If you haven’t configured your AI / LLM provider on your Orkes console, navigate to the **Integrations** tab and configure your required provider. Refer to this doc on [how to integrate the LLM providers with Orkes console and provide access to required groups](/content/category/integrations/ai-llm).| 
 | model | Choose from the available language model for the chosen LLM provider. You can only choose models for which you have access.<br/><br/>For example, If your LLM provider is Azure Open AI & you’ve configured *text-davinci-003* as the language model, you can choose it under this field. |

docs/reference-docs/ai-tasks/llm-get-document.md

@@ -24,7 +24,7 @@ A system task to retrieve the content of the document provided and use it for fu
 
 ## Input Parameters
 
-| Attribute | Description |
+| Parameter | Description |
 | --------- | ----------- |
 | url | Provide the URL of the document to be retrieved.<br/><br/>Check out our documentation on [how to pass parameters to tasks](https://orkes.io/content/developer-guides/passing-inputs-to-task-in-conductor). |
 | mediaType | Select the media type of the file to be retrieved. Currently, supported media types include:<ul><li>application/pdf</li><li>text/html</li><li>text/plain</li><li>json</li></ul> | 

docs/reference-docs/ai-tasks/llm-get-embeddings.md

@@ -26,7 +26,7 @@ A system task to get the numerical vector representations of words, phrases, sen
 
 ## Input Parameters
 
-| Attribute | Description | 
+| Parameter | Description |
 | --------- | ----------- | 
 | vectorDB | Choose the required vector database.<br/><br/>**Note**:If you haven’t configured the vector database on your Orkes console, navigate to the Integrations tab and configure your required provider. Refer to this doc on [how to integrate Vector Databases with Orkes console](/content/category/integrations/vector-databases). |
 | namespace | Choose from the available namespace configured within the chosen vector database.<br/><br/>Namespaces are separate isolated environments within the database to manage and organize vector data effectively.<br/><br/>**Note**:Namespace field is applicable only for Pinecone integration and is not applicable to Weaviate integration.|

docs/reference-docs/ai-tasks/llm-index-document.md

@@ -31,7 +31,7 @@ A system task to index the provided document into a vector database that can be
 
 ## Input Parameters
 
-| Attribute | Description |
+| Parameter | Description |
 | --------- | ----------- |
 | vectorDB | Choose the required vector database.<br/><br/>**Note**:If you haven’t configured the vector database on your Orkes console, navigate to the Integrations tab and configure your required provider. Refer to this doc on [how to integrate Vector Databases with Orkes console](/content/category/integrations/vector-databases). |
 | namespace | Choose from the available namespace configured within the chosen vector database.<br/><br/>Namespaces are separate isolated environments within the database to manage and organize vector data effectively.<br/><br/>**Note**:Namespace field is applicable only for Pinecone integration and is not applicable to Weaviate integration.|

docs/reference-docs/ai-tasks/llm-index-text.md

@@ -29,7 +29,7 @@ A system task to index the provided text into a vector space that can be efficie
 
 ## Input Parameters
 
-| Attribute | Description |
+| Parameter | Description |
 | --------- | ----------- |
 | vectorDB | Choose the required vector database.<br/><br/>**Note**:If you haven’t configured the vector database on your Orkes console, navigate to the Integrations tab and configure your required provider. Refer to this doc on [how to integrate Vector Databases with Orkes console](/content/category/integrations/vector-databases). |
 | namespace | Choose from the available namespace configured within the chosen vector database.<br/><br/>Namespaces are separate isolated environments within the database to manage and organize vector data effectively.<br/><br/>**Note**:Namespace field is applicable only for Pinecone integration and is not applicable to Weaviate integration.|

docs/reference-docs/ai-tasks/llm-search-index.md

@@ -30,7 +30,7 @@ For example, in a recommendation system, a user might issue a query to find prod
 
 ## Input Parameters
 
-| Attribute | Description |
+| Parameter | Description |
 | --------- | ----------- |
 | vectorDB | Choose the required vector database.<br/><br/>**Note**:If you haven’t configured the vector database on your Orkes console, navigate to the Integrations tab and configure your required provider. Refer to this doc on [how to integrate Vector Databases with Orkes console](/content/category/integrations/vector-databases). |
 | namespace | Choose from the available namespace configured within the chosen vector database.<br/><br/>Namespaces are separate isolated environments within the database to manage and organize vector data effectively.<br/><br/>**Note**:Namespace field is applicable only for Pinecone integration and is not applicable to Weaviate integration.|

docs/reference-docs/ai-tasks/llm-store-embeddings.md

@@ -28,7 +28,7 @@ A system task responsible for storing the generated embeddings produced by the [
 
 ## Input Parameters
 
-| Attribute | Decsription |
+| Parameter | Description |
 | ---------- | ----------- |
 | vectorDB | Choose the vector database to which the data is to be stored. <br/><br/>**Note**: If you haven’t configured the vector database on your Orkes console, navigate to the **_Integrations_** tab and configure your required provider. Refer to this doc on [how to integrate Vector Databases with Orkes console](https://orkes.io/content/category/integrations/vector-databases). |
 | index | Choose the index in your vector database where the text or data is to be stored.<br/><br/>**Note**: For Weaviate integration, this field refers to the class name, while in Pinecone integration, it denotes the index name itself. | 

docs/reference-docs/ai-tasks/llm-text-complete.md

@@ -37,7 +37,7 @@ A system task to predict or generate the next phrase or words in a given text ba
 
 ## Input Parameters
 
-| Attribute | Description |
+| Parameter | Description |
 | --------- | ----------- |
 | llmProvider | Choose the required LLM provider. You can only choose providers to which you have access for at least one model from that provider.<br/><br/>**Note**:If you haven’t configured your AI / LLM provider on your Orkes console, navigate to the **Integrations** tab and configure your required provider. Refer to this doc on [how to integrate the LLM providers with Orkes console and provide access to required groups](/content/category/integrations/ai-llm).| 
 | model | Choose from the available language model for the chosen LLM provider. You can only choose models for which you have access.<br/><br/>For example, If your LLM provider is Azure Open AI & you’ve configured *text-davinci-003* as the language model, you can choose it under this field. |

docs/reference-docs/ai-tasks/prompt-template.md

@@ -10,7 +10,7 @@ The AI prompts can be created in the Orkes Conductor cluster and can be used in
 
 ## Parameters
 
-| Attribute | Description |
+| Parameter | Description |
 | --------- | ----------- |
 | Prompt Name | A name for the prompt. |
 | Description | A description for the prompt. |