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Merged
merged 1 commit into from
Oct 2, 2024
Merged

feat:No changes made in the pull request. #67

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40 changes: 20 additions & 20 deletions src/libs/Cohere/Generated/Cohere.Models.ChatFinishReason.g.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -14,25 +14,25 @@ namespace Cohere
public enum ChatFinishReason
{
/// <summary>
/// The model finished sending a complete message.
///
/// </summary>
Complete,
COMPLETE,
/// <summary>
/// One of the provided `stop_sequence` entries was reached in the model's generation.
///
/// </summary>
StopSequence,
STOPSEQUENCE,
/// <summary>
/// The number of generated tokens exceeded the model's context length or the value specified via the `max_tokens` parameter.
///
/// </summary>
MaxTokens,
MAXTOKENS,
/// <summary>
/// The model generated a Tool Call and is expecting a Tool Message in return
///
/// </summary>
ToolCall,
TOOLCALL,
/// <summary>
/// The generation failed due to an internal error
///
/// </summary>
Error,
ERROR,
}

/// <summary>
Expand All @@ -47,11 +47,11 @@ public static string ToValueString(this ChatFinishReason value)
{
return value switch
{
ChatFinishReason.Complete => "complete",
ChatFinishReason.StopSequence => "stop_sequence",
ChatFinishReason.MaxTokens => "max_tokens",
ChatFinishReason.ToolCall => "tool_call",
ChatFinishReason.Error => "error",
ChatFinishReason.COMPLETE => "COMPLETE",
ChatFinishReason.STOPSEQUENCE => "STOP_SEQUENCE",
ChatFinishReason.MAXTOKENS => "MAX_TOKENS",
ChatFinishReason.TOOLCALL => "TOOL_CALL",
ChatFinishReason.ERROR => "ERROR",
_ => throw new global::System.ArgumentOutOfRangeException(nameof(value), value, null),
};
}
Expand All @@ -62,11 +62,11 @@ public static string ToValueString(this ChatFinishReason value)
{
return value switch
{
"complete" => ChatFinishReason.Complete,
"stop_sequence" => ChatFinishReason.StopSequence,
"max_tokens" => ChatFinishReason.MaxTokens,
"tool_call" => ChatFinishReason.ToolCall,
"error" => ChatFinishReason.Error,
"COMPLETE" => ChatFinishReason.COMPLETE,
"STOP_SEQUENCE" => ChatFinishReason.STOPSEQUENCE,
"MAX_TOKENS" => ChatFinishReason.MAXTOKENS,
"TOOL_CALL" => ChatFinishReason.TOOLCALL,
"ERROR" => ChatFinishReason.ERROR,
_ => null,
};
}
Expand Down
18 changes: 9 additions & 9 deletions src/libs/Cohere/openapi.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -916,7 +916,7 @@ paths:
content:
- type: text
text: "LLMs stand for Large Language Models, which are a type of neural network model specialized in processing and generating human language. They are designed to understand and respond to natural language input and have become increasingly popular and valuable in recent years.\n\nLLMs are trained on vast amounts of text data, enabling them to learn patterns, grammar, and semantic meanings present in the language. These models can then be used for various natural language processing tasks, such as text generation, summarization, question answering, machine translation, sentiment analysis, and even some aspects of natural language understanding.\n\nSome well-known examples of LLMs include:\n\n1. GPT-3 (Generative Pre-trained Transformer 3) — An open-source LLM developed by OpenAI, capable of generating human-like text and performing various language tasks.\n\n2. BERT (Bidirectional Encoder Representations from Transformers) — A Google-developed LLM that is particularly good at understanding contextual relationships in text, and is widely used for natural language understanding tasks like sentiment analysis and named entity recognition.\n\n3. T5 (Text-to-Text Transfer Transformer) — Also from Google, T5 is a flexible LLM that frames all language tasks as text-to-text problems, where the model learns to generate output text based on input text prompts.\n\n4. RoBERTa (Robustly Optimized BERT Approach) — A variant of BERT that uses additional training techniques to improve performance.\n\n5. DeBERTa (Decoding-enhanced BERT with disentangled attention) — Another variant of BERT that introduces a new attention mechanism.\n\nLLMs have become increasingly powerful and larger in scale, improving the accuracy and sophistication of language tasks. They are also being used as a foundation for developing various applications, including chatbots, content recommendation systems, language translation services, and more. \n\nThe future of LLMs holds the potential for even more sophisticated language technologies, with ongoing research and development focused on enhancing their capabilities, improving efficiency, and exploring their applications in various domains."
finish_reason: complete
finish_reason: COMPLETE
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⚠️ Potential issue

Consider removing hardcoded response content from OpenAPI spec

The OpenAPI specification typically defines the structure of requests and responses, not their content. The large block of text about LLMs should not be part of the spec itself.

Instead of including the actual content, consider defining the response structure. For example:

content:
  application/json:
    schema:
      type: object
      properties:
        content:
          type: array
          items:
            type: object
            properties:
              type:
                type: string
              text:
                type: string
        finish_reason:
          type: string
        usage:
          type: object
          properties:
            billed_units:
              type: object
              properties:
                input_tokens:
                  type: integer

This approach allows for a more flexible and reusable API specification.

usage:
billed_units:
input_tokens: 5
Expand Down Expand Up @@ -1055,7 +1055,7 @@ paths:
document:
snippet: "1997, 1998, 2000 and 2001 also rank amongst some of the very best years.\nYet the way many music consumers – especially teenagers and young women’s – embraced their output deserves its own chapter. If Jonas Brothers and more recently One Direction reached a great level of popularity during the past decade, the type of success achieved by Backstreet Boys is in a completely different level as they really dominated the business for a few years all over the world, including in some countries that were traditionally hard to penetrate for Western artists.\n\nWe will try to analyze the extent of that hegemony with this new article with final results which will more than surprise many readers."
title: 'CSPC: Backstreet Boys Popularity Analysis - ChartMasters'
finish_reason: complete
finish_reason: COMPLETE
usage:
billed_units:
input_tokens: 682
Expand Down Expand Up @@ -1180,7 +1180,7 @@ paths:
data:
type: message-end
delta:
finish_reason: complete
finish_reason: COMPLETE
usage:
billed_units:
input_tokens: 3
Expand Down Expand Up @@ -1249,7 +1249,7 @@ paths:
function:
name: query_product_catalog
arguments: '{"category": "Electronics"}'
finish_reason: tool_call
finish_reason: TOOL_CALL
usage:
billed_units:
input_tokens: 127
Expand Down Expand Up @@ -10100,11 +10100,11 @@ components:
json_object: '#/components/schemas/JsonResponseFormatV2'
ChatFinishReason:
enum:
- complete
- stop_sequence
- max_tokens
- tool_call
- error
- COMPLETE
- STOP_SEQUENCE
- MAX_TOKENS
- TOOL_CALL
- ERROR
type: string
description: "The reason a chat request has finished.\n\n- **complete**: The model finished sending a complete message.\n- **max_tokens**: The number of generated tokens exceeded the model's context length or the value specified via the `max_tokens` parameter.\n- **stop_sequence**: One of the provided `stop_sequence` entries was reached in the model's generation.\n- **tool_call**: The model generated a Tool Call and is expecting a Tool Message in return\n- **error**: The generation failed due to an internal error\n"
AssistantMessageResponse:
Expand Down
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