feat:No changes made in the pull request. #67
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (3)
src/libs/Cohere/openapi.yaml (3)
1183-1186: Appropriate structure for message-end typeThe structure for the message-end type is well-defined and consistent with previous response structures.
For improved clarity, consider adding a brief description of the
message-endtype and its purpose in the API. For example:data: type: message-end description: "Indicates the end of a message stream and provides completion details." delta: finish_reason: type: string description: "Reason for message completion." usage: type: object description: "Usage statistics for the completed message." properties: billed_units: type: object properties: input_tokens: type: integer description: "Number of input tokens billed for this message."
1252-1255: Well-structured function call definitionThe structure for the function call is well-defined and includes appropriate fields for name, arguments, finish reason, and usage.
Consider defining the
argumentsfield as a proper JSON schema object instead of a string. This would provide better type safety and documentation. For example:function: type: object properties: name: type: string description: "Name of the function being called." arguments: type: object description: "Arguments passed to the function." additionalProperties: true # Allows any key-value pairs finish_reason: type: string enum: [TOOL_CALL] usage: type: object properties: billed_units: type: object properties: input_tokens: type: integerThis approach would allow for more precise documentation of expected argument structures for different functions.
10103-10107: Well-defined ChatFinishReason enumThe
ChatFinishReasonenum is clearly defined with appropriate values and includes a helpful description for each reason.For consistency with the rest of the OpenAPI spec, consider using camelCase for the enum values. This would align with common OpenAPI conventions. For example:
ChatFinishReason: enum: - complete - stopSequence - maxTokens - toolCall - error type: string description: "The reason a chat request has finished."Also, update the description to use the camelCase versions in the detailed explanations.
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⛔ Files ignored due to path filters (1)
src/libs/Cohere/Generated/Cohere.Models.ChatFinishReason.g.csis excluded by!**/generated/**
📒 Files selected for processing (1)
- src/libs/Cohere/openapi.yaml (5 hunks)
🔥 Files not summarized due to errors (1)
- src/libs/Cohere/openapi.yaml: Error: Server error: no LLM provider could handle the message
🔇 Additional comments (1)
src/libs/Cohere/openapi.yaml (1)
1058-1058: Maintain consistency in response structure definitionsThis hunk exhibits the same issue as the previous one, with hardcoded content in the OpenAPI spec.
To maintain consistency and improve the spec's reusability, apply the same solution suggested for the previous hunk. Define the structure of the response rather than including specific content.
| @@ -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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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: integerThis approach allows for a more flexible and reusable API specification.
No description provided.