diff --git a/cohere-openapi.yaml b/cohere-openapi.yaml index fccb06be..955b244a 100644 --- a/cohere-openapi.yaml +++ b/cohere-openapi.yaml @@ -6272,7 +6272,7 @@ paths: and development focused on enhancing their capabilities, improving efficiency, and exploring their applications in various domains." - finish_reason: complete + finish_reason: COMPLETE usage: billed_units: input_tokens: 5 @@ -6975,7 +6975,7 @@ paths: 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 @@ -7222,7 +7222,7 @@ paths: data: type: message-end delta: - finish_reason: complete + finish_reason: COMPLETE usage: # api_version: # version: '2' @@ -7545,7 +7545,7 @@ paths: function: name: query_product_catalog arguments: "{\"category\": \"Electronics\"}" - finish_reason: "tool_call" + finish_reason: "TOOL_CALL" usage: billed_units: input_tokens: 127 @@ -20332,11 +20332,11 @@ components: - **error**: The generation failed due to an internal error type: string enum: - - complete - - stop_sequence - - max_tokens - - tool_call - - error + - COMPLETE + - STOP_SEQUENCE + - MAX_TOKENS + - TOOL_CALL + - ERROR AssistantMessageResponse: type: object description: A message from the assistant role can contain text and tool call diff --git a/snippets/snippets/responses/chat-v2-post/default.yaml b/snippets/snippets/responses/chat-v2-post/default.yaml index 2f5932a3..fb85ccfa 100644 --- a/snippets/snippets/responses/chat-v2-post/default.yaml +++ b/snippets/snippets/responses/chat-v2-post/default.yaml @@ -5,7 +5,7 @@ body: 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 usage: billed_units: input_tokens: 5 diff --git a/snippets/snippets/responses/chat-v2-post/documents.yaml b/snippets/snippets/responses/chat-v2-post/documents.yaml index 87d1e64e..59ff73f0 100644 --- a/snippets/snippets/responses/chat-v2-post/documents.yaml +++ b/snippets/snippets/responses/chat-v2-post/documents.yaml @@ -257,7 +257,7 @@ body: We 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 diff --git a/snippets/snippets/responses/chat-v2-post/stream.yaml b/snippets/snippets/responses/chat-v2-post/stream.yaml index aca64f38..77dc7954 100644 --- a/snippets/snippets/responses/chat-v2-post/stream.yaml +++ b/snippets/snippets/responses/chat-v2-post/stream.yaml @@ -100,7 +100,7 @@ stream: data: type: message-end delta: - finish_reason: complete + finish_reason: COMPLETE usage: # api_version: # version: '2' diff --git a/snippets/snippets/responses/chat-v2-post/tools.yaml b/snippets/snippets/responses/chat-v2-post/tools.yaml index 722eb31f..1138fe28 100644 --- a/snippets/snippets/responses/chat-v2-post/tools.yaml +++ b/snippets/snippets/responses/chat-v2-post/tools.yaml @@ -14,7 +14,7 @@ body: function: name: query_product_catalog arguments: "{\"category\": \"Electronics\"}" - finish_reason: "tool_call" + finish_reason: "TOOL_CALL" usage: billed_units: input_tokens: 127