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types.go
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// This file was auto-generated by Fern from our API Definition.
package api
import (
json "encoding/json"
fmt "fmt"
internal "github.com/cohere-ai/cohere-go/v2/internal"
)
type ChatRequest struct {
// Pass text/event-stream to receive the streamed response as server-sent events. The default is `\n` delimited events.
Accepts *string `json:"-" url:"-"`
// Text input for the model to respond to.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
Message string `json:"message" url:"-"`
// Defaults to `command-r-plus-08-2024`.
//
// The name of a compatible [Cohere model](https://docs.cohere.com/docs/models) or the ID of a [fine-tuned](https://docs.cohere.com/docs/chat-fine-tuning) model.
//
// Compatible Deployments: Cohere Platform, Private Deployments
Model *string `json:"model,omitempty" url:"-"`
// Defaults to `false`.
//
// When `true`, the response will be a JSON stream of events. The final event will contain the complete response, and will have an `event_type` of `"stream-end"`.
//
// Streaming is beneficial for user interfaces that render the contents of the response piece by piece, as it gets generated.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
//
// When specified, the default Cohere preamble will be replaced with the provided one. Preambles are a part of the prompt used to adjust the model's overall behavior and conversation style, and use the `SYSTEM` role.
//
// The `SYSTEM` role is also used for the contents of the optional `chat_history=` parameter. When used with the `chat_history=` parameter it adds content throughout a conversation. Conversely, when used with the `preamble=` parameter it adds content at the start of the conversation only.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
Preamble *string `json:"preamble,omitempty" url:"-"`
// A list of previous messages between the user and the model, giving the model conversational context for responding to the user's `message`.
//
// Each item represents a single message in the chat history, excluding the current user turn. It has two properties: `role` and `message`. The `role` identifies the sender (`CHATBOT`, `SYSTEM`, or `USER`), while the `message` contains the text content.
//
// The chat_history parameter should not be used for `SYSTEM` messages in most cases. Instead, to add a `SYSTEM` role message at the beginning of a conversation, the `preamble` parameter should be used.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
ChatHistory []*Message `json:"chat_history,omitempty" url:"-"`
// An alternative to `chat_history`.
//
// Providing a `conversation_id` creates or resumes a persisted conversation with the specified ID. The ID can be any non empty string.
//
// Compatible Deployments: Cohere Platform
ConversationId *string `json:"conversation_id,omitempty" url:"-"`
// Defaults to `AUTO` when `connectors` are specified and `OFF` in all other cases.
//
// Dictates how the prompt will be constructed.
//
// With `prompt_truncation` set to "AUTO", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be changed and ranked by relevance.
//
// With `prompt_truncation` set to "AUTO_PRESERVE_ORDER", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be preserved as they are inputted into the API.
//
// With `prompt_truncation` set to "OFF", no elements will be dropped. If the sum of the inputs exceeds the model's context length limit, a `TooManyTokens` error will be returned.
//
// Compatible Deployments:
// - AUTO: Cohere Platform Only
// - AUTO_PRESERVE_ORDER: Azure, AWS Sagemaker/Bedrock, Private Deployments
PromptTruncation *ChatRequestPromptTruncation `json:"prompt_truncation,omitempty" url:"-"`
// Accepts `{"id": "web-search"}`, and/or the `"id"` for a custom [connector](https://docs.cohere.com/docs/connectors), if you've [created](https://docs.cohere.com/v1/docs/creating-and-deploying-a-connector) one.
//
// When specified, the model's reply will be enriched with information found by querying each of the connectors (RAG).
//
// Compatible Deployments: Cohere Platform
Connectors []*ChatConnector `json:"connectors,omitempty" url:"-"`
// Defaults to `false`.
//
// When `true`, the response will only contain a list of generated search queries, but no search will take place, and no reply from the model to the user's `message` will be generated.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
SearchQueriesOnly *bool `json:"search_queries_only,omitempty" url:"-"`
// A list of relevant documents that the model can cite to generate a more accurate reply. Each document is a string-string dictionary.
//
// Example:
// ```
// [
//
// { "title": "Tall penguins", "text": "Emperor penguins are the tallest." },
// { "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica." },
//
// ]
// ```
//
// Keys and values from each document will be serialized to a string and passed to the model. The resulting generation will include citations that reference some of these documents.
//
// Some suggested keys are "text", "author", and "date". For better generation quality, it is recommended to keep the total word count of the strings in the dictionary to under 300 words.
//
// An `id` field (string) can be optionally supplied to identify the document in the citations. This field will not be passed to the model.
//
// An `_excludes` field (array of strings) can be optionally supplied to omit some key-value pairs from being shown to the model. The omitted fields will still show up in the citation object. The "_excludes" field will not be passed to the model.
//
// See ['Document Mode'](https://docs.cohere.com/docs/retrieval-augmented-generation-rag#document-mode) in the guide for more information.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
Documents []ChatDocument `json:"documents,omitempty" url:"-"`
// Defaults to `"accurate"`.
//
// Dictates the approach taken to generating citations as part of the RAG flow by allowing the user to specify whether they want `"accurate"` results, `"fast"` results or no results.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
CitationQuality *ChatRequestCitationQuality `json:"citation_quality,omitempty" url:"-"`
// Defaults to `0.3`.
//
// A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations, and higher temperatures mean more random generations.
//
// Randomness can be further maximized by increasing the value of the `p` parameter.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
Temperature *float64 `json:"temperature,omitempty" url:"-"`
// The maximum number of tokens the model will generate as part of the response. Note: Setting a low value may result in incomplete generations.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
MaxTokens *int `json:"max_tokens,omitempty" url:"-"`
// The maximum number of input tokens to send to the model. If not specified, `max_input_tokens` is the model's context length limit minus a small buffer.
//
// Input will be truncated according to the `prompt_truncation` parameter.
//
// Compatible Deployments: Cohere Platform
MaxInputTokens *int `json:"max_input_tokens,omitempty" url:"-"`
// Ensures only the top `k` most likely tokens are considered for generation at each step.
// Defaults to `0`, min value of `0`, max value of `500`.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
K *int `json:"k,omitempty" url:"-"`
// Ensures that only the most likely tokens, with total probability mass of `p`, are considered for generation at each step. If both `k` and `p` are enabled, `p` acts after `k`.
// Defaults to `0.75`. min value of `0.01`, max value of `0.99`.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
P *float64 `json:"p,omitempty" url:"-"`
// If specified, the backend will make a best effort to sample tokens
// deterministically, such that repeated requests with the same
// seed and parameters should return the same result. However,
// determinism cannot be totally guaranteed.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
Seed *int `json:"seed,omitempty" url:"-"`
// A list of up to 5 strings that the model will use to stop generation. If the model generates a string that matches any of the strings in the list, it will stop generating tokens and return the generated text up to that point not including the stop sequence.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
StopSequences []string `json:"stop_sequences,omitempty" url:"-"`
// Defaults to `0.0`, min value of `0.0`, max value of `1.0`.
//
// Used to reduce repetitiveness of generated tokens. The higher the value, the stronger a penalty is applied to previously present tokens, proportional to how many times they have already appeared in the prompt or prior generation.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
FrequencyPenalty *float64 `json:"frequency_penalty,omitempty" url:"-"`
// Defaults to `0.0`, min value of `0.0`, max value of `1.0`.
//
// Used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty`, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
PresencePenalty *float64 `json:"presence_penalty,omitempty" url:"-"`
// When enabled, the user's prompt will be sent to the model without
// any pre-processing.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
RawPrompting *bool `json:"raw_prompting,omitempty" url:"-"`
// The prompt is returned in the `prompt` response field when this is enabled.
ReturnPrompt *bool `json:"return_prompt,omitempty" url:"-"`
// A list of available tools (functions) that the model may suggest invoking before producing a text response.
//
// When `tools` is passed (without `tool_results`), the `text` field in the response will be `""` and the `tool_calls` field in the response will be populated with a list of tool calls that need to be made. If no calls need to be made, the `tool_calls` array will be empty.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
Tools []*Tool `json:"tools,omitempty" url:"-"`
// A list of results from invoking tools recommended by the model in the previous chat turn. Results are used to produce a text response and will be referenced in citations. When using `tool_results`, `tools` must be passed as well.
// Each tool_result contains information about how it was invoked, as well as a list of outputs in the form of dictionaries.
//
// **Note**: `outputs` must be a list of objects. If your tool returns a single object (eg `{"status": 200}`), make sure to wrap it in a list.
// ```
// tool_results = [
//
// {
// "call": {
// "name": <tool name>,
// "parameters": {
// <param name>: <param value>
// }
// },
// "outputs": [{
// <key>: <value>
// }]
// },
// ...
//
// ]
// ```
// **Note**: Chat calls with `tool_results` should not be included in the Chat history to avoid duplication of the message text.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
ToolResults []*ToolResult `json:"tool_results,omitempty" url:"-"`
// Forces the chat to be single step. Defaults to `false`.
ForceSingleStep *bool `json:"force_single_step,omitempty" url:"-"`
ResponseFormat *ResponseFormat `json:"response_format,omitempty" url:"-"`
// Used to select the [safety instruction](https://docs.cohere.com/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`.
// When `NONE` is specified, the safety instruction will be omitted.
//
// Safety modes are not yet configurable in combination with `tools`, `tool_results` and `documents` parameters.
//
// **Note**: This parameter is only compatible with models [Command R 08-2024](https://docs.cohere.com/docs/command-r#august-2024-release), [Command R+ 08-2024](https://docs.cohere.com/docs/command-r-plus#august-2024-release) and newer.
//
// **Note**: `command-r7b-12-2024` only supports `"CONTEXTUAL"` and `"STRICT"` modes.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
SafetyMode *ChatRequestSafetyMode `json:"safety_mode,omitempty" url:"-"`
stream bool
}
func (c *ChatRequest) Stream() bool {
return c.stream
}
func (c *ChatRequest) UnmarshalJSON(data []byte) error {
type unmarshaler ChatRequest
var body unmarshaler
if err := json.Unmarshal(data, &body); err != nil {
return err
}
*c = ChatRequest(body)
c.stream = false
return nil
}
func (c *ChatRequest) MarshalJSON() ([]byte, error) {
type embed ChatRequest
var marshaler = struct {
embed
Stream bool `json:"stream"`
}{
embed: embed(*c),
Stream: false,
}
return json.Marshal(marshaler)
}
type ChatStreamRequest struct {
// Pass text/event-stream to receive the streamed response as server-sent events. The default is `\n` delimited events.
Accepts *string `json:"-" url:"-"`
// Text input for the model to respond to.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
Message string `json:"message" url:"-"`
// Defaults to `command-r-plus-08-2024`.
//
// The name of a compatible [Cohere model](https://docs.cohere.com/docs/models) or the ID of a [fine-tuned](https://docs.cohere.com/docs/chat-fine-tuning) model.
//
// Compatible Deployments: Cohere Platform, Private Deployments
Model *string `json:"model,omitempty" url:"-"`
// Defaults to `false`.
//
// When `true`, the response will be a JSON stream of events. The final event will contain the complete response, and will have an `event_type` of `"stream-end"`.
//
// Streaming is beneficial for user interfaces that render the contents of the response piece by piece, as it gets generated.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
//
// When specified, the default Cohere preamble will be replaced with the provided one. Preambles are a part of the prompt used to adjust the model's overall behavior and conversation style, and use the `SYSTEM` role.
//
// The `SYSTEM` role is also used for the contents of the optional `chat_history=` parameter. When used with the `chat_history=` parameter it adds content throughout a conversation. Conversely, when used with the `preamble=` parameter it adds content at the start of the conversation only.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
Preamble *string `json:"preamble,omitempty" url:"-"`
// A list of previous messages between the user and the model, giving the model conversational context for responding to the user's `message`.
//
// Each item represents a single message in the chat history, excluding the current user turn. It has two properties: `role` and `message`. The `role` identifies the sender (`CHATBOT`, `SYSTEM`, or `USER`), while the `message` contains the text content.
//
// The chat_history parameter should not be used for `SYSTEM` messages in most cases. Instead, to add a `SYSTEM` role message at the beginning of a conversation, the `preamble` parameter should be used.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
ChatHistory []*Message `json:"chat_history,omitempty" url:"-"`
// An alternative to `chat_history`.
//
// Providing a `conversation_id` creates or resumes a persisted conversation with the specified ID. The ID can be any non empty string.
//
// Compatible Deployments: Cohere Platform
ConversationId *string `json:"conversation_id,omitempty" url:"-"`
// Defaults to `AUTO` when `connectors` are specified and `OFF` in all other cases.
//
// Dictates how the prompt will be constructed.
//
// With `prompt_truncation` set to "AUTO", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be changed and ranked by relevance.
//
// With `prompt_truncation` set to "AUTO_PRESERVE_ORDER", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be preserved as they are inputted into the API.
//
// With `prompt_truncation` set to "OFF", no elements will be dropped. If the sum of the inputs exceeds the model's context length limit, a `TooManyTokens` error will be returned.
//
// Compatible Deployments:
// - AUTO: Cohere Platform Only
// - AUTO_PRESERVE_ORDER: Azure, AWS Sagemaker/Bedrock, Private Deployments
PromptTruncation *ChatStreamRequestPromptTruncation `json:"prompt_truncation,omitempty" url:"-"`
// Accepts `{"id": "web-search"}`, and/or the `"id"` for a custom [connector](https://docs.cohere.com/docs/connectors), if you've [created](https://docs.cohere.com/v1/docs/creating-and-deploying-a-connector) one.
//
// When specified, the model's reply will be enriched with information found by querying each of the connectors (RAG).
//
// Compatible Deployments: Cohere Platform
Connectors []*ChatConnector `json:"connectors,omitempty" url:"-"`
// Defaults to `false`.
//
// When `true`, the response will only contain a list of generated search queries, but no search will take place, and no reply from the model to the user's `message` will be generated.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
SearchQueriesOnly *bool `json:"search_queries_only,omitempty" url:"-"`
// A list of relevant documents that the model can cite to generate a more accurate reply. Each document is a string-string dictionary.
//
// Example:
// ```
// [
//
// { "title": "Tall penguins", "text": "Emperor penguins are the tallest." },
// { "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica." },
//
// ]
// ```
//
// Keys and values from each document will be serialized to a string and passed to the model. The resulting generation will include citations that reference some of these documents.
//
// Some suggested keys are "text", "author", and "date". For better generation quality, it is recommended to keep the total word count of the strings in the dictionary to under 300 words.
//
// An `id` field (string) can be optionally supplied to identify the document in the citations. This field will not be passed to the model.
//
// An `_excludes` field (array of strings) can be optionally supplied to omit some key-value pairs from being shown to the model. The omitted fields will still show up in the citation object. The "_excludes" field will not be passed to the model.
//
// See ['Document Mode'](https://docs.cohere.com/docs/retrieval-augmented-generation-rag#document-mode) in the guide for more information.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
Documents []ChatDocument `json:"documents,omitempty" url:"-"`
// Defaults to `"accurate"`.
//
// Dictates the approach taken to generating citations as part of the RAG flow by allowing the user to specify whether they want `"accurate"` results, `"fast"` results or no results.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
CitationQuality *ChatStreamRequestCitationQuality `json:"citation_quality,omitempty" url:"-"`
// Defaults to `0.3`.
//
// A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations, and higher temperatures mean more random generations.
//
// Randomness can be further maximized by increasing the value of the `p` parameter.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
Temperature *float64 `json:"temperature,omitempty" url:"-"`
// The maximum number of tokens the model will generate as part of the response. Note: Setting a low value may result in incomplete generations.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
MaxTokens *int `json:"max_tokens,omitempty" url:"-"`
// The maximum number of input tokens to send to the model. If not specified, `max_input_tokens` is the model's context length limit minus a small buffer.
//
// Input will be truncated according to the `prompt_truncation` parameter.
//
// Compatible Deployments: Cohere Platform
MaxInputTokens *int `json:"max_input_tokens,omitempty" url:"-"`
// Ensures only the top `k` most likely tokens are considered for generation at each step.
// Defaults to `0`, min value of `0`, max value of `500`.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
K *int `json:"k,omitempty" url:"-"`
// Ensures that only the most likely tokens, with total probability mass of `p`, are considered for generation at each step. If both `k` and `p` are enabled, `p` acts after `k`.
// Defaults to `0.75`. min value of `0.01`, max value of `0.99`.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
P *float64 `json:"p,omitempty" url:"-"`
// If specified, the backend will make a best effort to sample tokens
// deterministically, such that repeated requests with the same
// seed and parameters should return the same result. However,
// determinism cannot be totally guaranteed.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
Seed *int `json:"seed,omitempty" url:"-"`
// A list of up to 5 strings that the model will use to stop generation. If the model generates a string that matches any of the strings in the list, it will stop generating tokens and return the generated text up to that point not including the stop sequence.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
StopSequences []string `json:"stop_sequences,omitempty" url:"-"`
// Defaults to `0.0`, min value of `0.0`, max value of `1.0`.
//
// Used to reduce repetitiveness of generated tokens. The higher the value, the stronger a penalty is applied to previously present tokens, proportional to how many times they have already appeared in the prompt or prior generation.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
FrequencyPenalty *float64 `json:"frequency_penalty,omitempty" url:"-"`
// Defaults to `0.0`, min value of `0.0`, max value of `1.0`.
//
// Used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty`, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
PresencePenalty *float64 `json:"presence_penalty,omitempty" url:"-"`
// When enabled, the user's prompt will be sent to the model without
// any pre-processing.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
RawPrompting *bool `json:"raw_prompting,omitempty" url:"-"`
// The prompt is returned in the `prompt` response field when this is enabled.
ReturnPrompt *bool `json:"return_prompt,omitempty" url:"-"`
// A list of available tools (functions) that the model may suggest invoking before producing a text response.
//
// When `tools` is passed (without `tool_results`), the `text` field in the response will be `""` and the `tool_calls` field in the response will be populated with a list of tool calls that need to be made. If no calls need to be made, the `tool_calls` array will be empty.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
Tools []*Tool `json:"tools,omitempty" url:"-"`
// A list of results from invoking tools recommended by the model in the previous chat turn. Results are used to produce a text response and will be referenced in citations. When using `tool_results`, `tools` must be passed as well.
// Each tool_result contains information about how it was invoked, as well as a list of outputs in the form of dictionaries.
//
// **Note**: `outputs` must be a list of objects. If your tool returns a single object (eg `{"status": 200}`), make sure to wrap it in a list.
// ```
// tool_results = [
//
// {
// "call": {
// "name": <tool name>,
// "parameters": {
// <param name>: <param value>
// }
// },
// "outputs": [{
// <key>: <value>
// }]
// },
// ...
//
// ]
// ```
// **Note**: Chat calls with `tool_results` should not be included in the Chat history to avoid duplication of the message text.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
ToolResults []*ToolResult `json:"tool_results,omitempty" url:"-"`
// Forces the chat to be single step. Defaults to `false`.
ForceSingleStep *bool `json:"force_single_step,omitempty" url:"-"`
ResponseFormat *ResponseFormat `json:"response_format,omitempty" url:"-"`
// Used to select the [safety instruction](https://docs.cohere.com/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`.
// When `NONE` is specified, the safety instruction will be omitted.
//
// Safety modes are not yet configurable in combination with `tools`, `tool_results` and `documents` parameters.
//
// **Note**: This parameter is only compatible with models [Command R 08-2024](https://docs.cohere.com/docs/command-r#august-2024-release), [Command R+ 08-2024](https://docs.cohere.com/docs/command-r-plus#august-2024-release) and newer.
//
// **Note**: `command-r7b-12-2024` only supports `"CONTEXTUAL"` and `"STRICT"` modes.
//
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
SafetyMode *ChatStreamRequestSafetyMode `json:"safety_mode,omitempty" url:"-"`
stream bool
}
func (c *ChatStreamRequest) Stream() bool {
return c.stream
}
func (c *ChatStreamRequest) UnmarshalJSON(data []byte) error {
type unmarshaler ChatStreamRequest
var body unmarshaler
if err := json.Unmarshal(data, &body); err != nil {
return err
}
*c = ChatStreamRequest(body)
c.stream = true
return nil
}
func (c *ChatStreamRequest) MarshalJSON() ([]byte, error) {
type embed ChatStreamRequest
var marshaler = struct {
embed
Stream bool `json:"stream"`
}{
embed: embed(*c),
Stream: true,
}
return json.Marshal(marshaler)
}
type ClassifyRequest struct {
// A list of up to 96 texts to be classified. Each one must be a non-empty string.
// There is, however, no consistent, universal limit to the length a particular input can be. We perform classification on the first `x` tokens of each input, and `x` varies depending on which underlying model is powering classification. The maximum token length for each model is listed in the "max tokens" column [here](https://docs.cohere.com/docs/models).
// Note: by default the `truncate` parameter is set to `END`, so tokens exceeding the limit will be automatically dropped. This behavior can be disabled by setting `truncate` to `NONE`, which will result in validation errors for longer texts.
Inputs []string `json:"inputs,omitempty" url:"-"`
// An array of examples to provide context to the model. Each example is a text string and its associated label/class. Each unique label requires at least 2 examples associated with it; the maximum number of examples is 2500, and each example has a maximum length of 512 tokens. The values should be structured as `{text: "...",label: "..."}`.
// Note: [Fine-tuned Models](https://docs.cohere.com/docs/classify-fine-tuning) trained on classification examples don't require the `examples` parameter to be passed in explicitly.
Examples []*ClassifyExample `json:"examples,omitempty" url:"-"`
// The identifier of the model. Currently available models are `embed-multilingual-v2.0`, `embed-english-light-v2.0`, and `embed-english-v2.0` (default). Smaller "light" models are faster, while larger models will perform better. [Fine-tuned models](https://docs.cohere.com/docs/fine-tuning) can also be supplied with their full ID.
Model *string `json:"model,omitempty" url:"-"`
// The ID of a custom playground preset. You can create presets in the [playground](https://dashboard.cohere.com/playground/classify?model=large). If you use a preset, all other parameters become optional, and any included parameters will override the preset's parameters.
Preset *string `json:"preset,omitempty" url:"-"`
// One of `NONE|START|END` to specify how the API will handle inputs longer than the maximum token length.
// Passing `START` will discard the start of the input. `END` will discard the end of the input. In both cases, input is discarded until the remaining input is exactly the maximum input token length for the model.
// If `NONE` is selected, when the input exceeds the maximum input token length an error will be returned.
Truncate *ClassifyRequestTruncate `json:"truncate,omitempty" url:"-"`
}
type DetokenizeRequest struct {
// The list of tokens to be detokenized.
Tokens []int `json:"tokens,omitempty" url:"-"`
// An optional parameter to provide the model name. This will ensure that the detokenization is done by the tokenizer used by that model.
Model string `json:"model" url:"-"`
}
type EmbedRequest struct {
// An array of strings for the model to embed. Maximum number of texts per call is `96`. We recommend reducing the length of each text to be under `512` tokens for optimal quality.
Texts []string `json:"texts,omitempty" url:"-"`
// An array of image data URIs for the model to embed. Maximum number of images per call is `1`.
//
// The image must be a valid [data URI](https://developer.mozilla.org/en-US/docs/Web/URI/Schemes/data). The image must be in either `image/jpeg` or `image/png` format and has a maximum size of 5MB.
Images []string `json:"images,omitempty" url:"-"`
// Defaults to embed-english-v2.0
//
// The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
//
// Available models and corresponding embedding dimensions:
//
// * `embed-english-v3.0` 1024
// * `embed-multilingual-v3.0` 1024
// * `embed-english-light-v3.0` 384
// * `embed-multilingual-light-v3.0` 384
//
// * `embed-english-v2.0` 4096
// * `embed-english-light-v2.0` 1024
// * `embed-multilingual-v2.0` 768
Model *string `json:"model,omitempty" url:"-"`
InputType *EmbedInputType `json:"input_type,omitempty" url:"-"`
// Specifies the types of embeddings you want to get back. Not required and default is None, which returns the Embed Floats response type. Can be one or more of the following types.
//
// * `"float"`: Use this when you want to get back the default float embeddings. Valid for all models.
// * `"int8"`: Use this when you want to get back signed int8 embeddings. Valid for only v3 models.
// * `"uint8"`: Use this when you want to get back unsigned int8 embeddings. Valid for only v3 models.
// * `"binary"`: Use this when you want to get back signed binary embeddings. Valid for only v3 models.
// * `"ubinary"`: Use this when you want to get back unsigned binary embeddings. Valid for only v3 models.
EmbeddingTypes []EmbeddingType `json:"embedding_types,omitempty" url:"-"`
// One of `NONE|START|END` to specify how the API will handle inputs longer than the maximum token length.
//
// Passing `START` will discard the start of the input. `END` will discard the end of the input. In both cases, input is discarded until the remaining input is exactly the maximum input token length for the model.
//
// If `NONE` is selected, when the input exceeds the maximum input token length an error will be returned.
Truncate *EmbedRequestTruncate `json:"truncate,omitempty" url:"-"`
}
type GenerateRequest struct {
// The input text that serves as the starting point for generating the response.
// Note: The prompt will be pre-processed and modified before reaching the model.
Prompt string `json:"prompt" url:"-"`
// The identifier of the model to generate with. Currently available models are `command` (default), `command-nightly` (experimental), `command-light`, and `command-light-nightly` (experimental).
// Smaller, "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
Model *string `json:"model,omitempty" url:"-"`
// The maximum number of generations that will be returned. Defaults to `1`, min value of `1`, max value of `5`.
NumGenerations *int `json:"num_generations,omitempty" url:"-"`
// When `true`, the response will be a JSON stream of events. Streaming is beneficial for user interfaces that render the contents of the response piece by piece, as it gets generated.
//
// The final event will contain the complete response, and will contain an `is_finished` field set to `true`. The event will also contain a `finish_reason`, which can be one of the following:
// - `COMPLETE` - the model sent back a finished reply
// - `MAX_TOKENS` - the reply was cut off because the model reached the maximum number of tokens for its context length
// - `ERROR` - something went wrong when generating the reply
// - `ERROR_TOXIC` - the model generated a reply that was deemed toxic
//
// The maximum number of tokens the model will generate as part of the response. Note: Setting a low value may result in incomplete generations.
//
// This parameter is off by default, and if it's not specified, the model will continue generating until it emits an EOS completion token. See [BPE Tokens](/bpe-tokens-wiki) for more details.
//
// Can only be set to `0` if `return_likelihoods` is set to `ALL` to get the likelihood of the prompt.
MaxTokens *int `json:"max_tokens,omitempty" url:"-"`
// One of `NONE|START|END` to specify how the API will handle inputs longer than the maximum token length.
//
// Passing `START` will discard the start of the input. `END` will discard the end of the input. In both cases, input is discarded until the remaining input is exactly the maximum input token length for the model.
//
// If `NONE` is selected, when the input exceeds the maximum input token length an error will be returned.
Truncate *GenerateRequestTruncate `json:"truncate,omitempty" url:"-"`
// A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations. See [Temperature](/temperature-wiki) for more details.
// Defaults to `0.75`, min value of `0.0`, max value of `5.0`.
Temperature *float64 `json:"temperature,omitempty" url:"-"`
// If specified, the backend will make a best effort to sample tokens
// deterministically, such that repeated requests with the same
// seed and parameters should return the same result. However,
// determinism cannot be totally guaranteed.
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
Seed *int `json:"seed,omitempty" url:"-"`
// Identifier of a custom preset. A preset is a combination of parameters, such as prompt, temperature etc. You can create presets in the [playground](https://dashboard.cohere.com/playground/generate).
// When a preset is specified, the `prompt` parameter becomes optional, and any included parameters will override the preset's parameters.
Preset *string `json:"preset,omitempty" url:"-"`
// The generated text will be cut at the beginning of the earliest occurrence of an end sequence. The sequence will be excluded from the text.
EndSequences []string `json:"end_sequences,omitempty" url:"-"`
// The generated text will be cut at the end of the earliest occurrence of a stop sequence. The sequence will be included the text.
StopSequences []string `json:"stop_sequences,omitempty" url:"-"`
// Ensures only the top `k` most likely tokens are considered for generation at each step.
// Defaults to `0`, min value of `0`, max value of `500`.
K *int `json:"k,omitempty" url:"-"`
// Ensures that only the most likely tokens, with total probability mass of `p`, are considered for generation at each step. If both `k` and `p` are enabled, `p` acts after `k`.
// Defaults to `0.75`. min value of `0.01`, max value of `0.99`.
P *float64 `json:"p,omitempty" url:"-"`
// Used to reduce repetitiveness of generated tokens. The higher the value, the stronger a penalty is applied to previously present tokens, proportional to how many times they have already appeared in the prompt or prior generation.
//
// Using `frequency_penalty` in combination with `presence_penalty` is not supported on newer models.
FrequencyPenalty *float64 `json:"frequency_penalty,omitempty" url:"-"`
// Defaults to `0.0`, min value of `0.0`, max value of `1.0`.
//
// Can be used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty`, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies.
//
// Using `frequency_penalty` in combination with `presence_penalty` is not supported on newer models.
PresencePenalty *float64 `json:"presence_penalty,omitempty" url:"-"`
// One of `GENERATION|NONE` to specify how and if the token likelihoods are returned with the response. Defaults to `NONE`.
//
// If `GENERATION` is selected, the token likelihoods will only be provided for generated text.
//
// WARNING: `ALL` is deprecated, and will be removed in a future release.
ReturnLikelihoods *GenerateRequestReturnLikelihoods `json:"return_likelihoods,omitempty" url:"-"`
// When enabled, the user's prompt will be sent to the model without any pre-processing.
RawPrompting *bool `json:"raw_prompting,omitempty" url:"-"`
stream bool
}
func (g *GenerateRequest) Stream() bool {
return g.stream
}
func (g *GenerateRequest) UnmarshalJSON(data []byte) error {
type unmarshaler GenerateRequest
var body unmarshaler
if err := json.Unmarshal(data, &body); err != nil {
return err
}
*g = GenerateRequest(body)
g.stream = false
return nil
}
func (g *GenerateRequest) MarshalJSON() ([]byte, error) {
type embed GenerateRequest
var marshaler = struct {
embed
Stream bool `json:"stream"`
}{
embed: embed(*g),
Stream: false,
}
return json.Marshal(marshaler)
}
type GenerateStreamRequest struct {
// The input text that serves as the starting point for generating the response.
// Note: The prompt will be pre-processed and modified before reaching the model.
Prompt string `json:"prompt" url:"-"`
// The identifier of the model to generate with. Currently available models are `command` (default), `command-nightly` (experimental), `command-light`, and `command-light-nightly` (experimental).
// Smaller, "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
Model *string `json:"model,omitempty" url:"-"`
// The maximum number of generations that will be returned. Defaults to `1`, min value of `1`, max value of `5`.
NumGenerations *int `json:"num_generations,omitempty" url:"-"`
// When `true`, the response will be a JSON stream of events. Streaming is beneficial for user interfaces that render the contents of the response piece by piece, as it gets generated.
//
// The final event will contain the complete response, and will contain an `is_finished` field set to `true`. The event will also contain a `finish_reason`, which can be one of the following:
// - `COMPLETE` - the model sent back a finished reply
// - `MAX_TOKENS` - the reply was cut off because the model reached the maximum number of tokens for its context length
// - `ERROR` - something went wrong when generating the reply
// - `ERROR_TOXIC` - the model generated a reply that was deemed toxic
//
// The maximum number of tokens the model will generate as part of the response. Note: Setting a low value may result in incomplete generations.
//
// This parameter is off by default, and if it's not specified, the model will continue generating until it emits an EOS completion token. See [BPE Tokens](/bpe-tokens-wiki) for more details.
//
// Can only be set to `0` if `return_likelihoods` is set to `ALL` to get the likelihood of the prompt.
MaxTokens *int `json:"max_tokens,omitempty" url:"-"`
// One of `NONE|START|END` to specify how the API will handle inputs longer than the maximum token length.
//
// Passing `START` will discard the start of the input. `END` will discard the end of the input. In both cases, input is discarded until the remaining input is exactly the maximum input token length for the model.
//
// If `NONE` is selected, when the input exceeds the maximum input token length an error will be returned.
Truncate *GenerateStreamRequestTruncate `json:"truncate,omitempty" url:"-"`
// A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations. See [Temperature](/temperature-wiki) for more details.
// Defaults to `0.75`, min value of `0.0`, max value of `5.0`.
Temperature *float64 `json:"temperature,omitempty" url:"-"`
// If specified, the backend will make a best effort to sample tokens
// deterministically, such that repeated requests with the same
// seed and parameters should return the same result. However,
// determinism cannot be totally guaranteed.
// Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
Seed *int `json:"seed,omitempty" url:"-"`
// Identifier of a custom preset. A preset is a combination of parameters, such as prompt, temperature etc. You can create presets in the [playground](https://dashboard.cohere.com/playground/generate).
// When a preset is specified, the `prompt` parameter becomes optional, and any included parameters will override the preset's parameters.
Preset *string `json:"preset,omitempty" url:"-"`
// The generated text will be cut at the beginning of the earliest occurrence of an end sequence. The sequence will be excluded from the text.
EndSequences []string `json:"end_sequences,omitempty" url:"-"`
// The generated text will be cut at the end of the earliest occurrence of a stop sequence. The sequence will be included the text.
StopSequences []string `json:"stop_sequences,omitempty" url:"-"`
// Ensures only the top `k` most likely tokens are considered for generation at each step.
// Defaults to `0`, min value of `0`, max value of `500`.
K *int `json:"k,omitempty" url:"-"`
// Ensures that only the most likely tokens, with total probability mass of `p`, are considered for generation at each step. If both `k` and `p` are enabled, `p` acts after `k`.
// Defaults to `0.75`. min value of `0.01`, max value of `0.99`.
P *float64 `json:"p,omitempty" url:"-"`
// Used to reduce repetitiveness of generated tokens. The higher the value, the stronger a penalty is applied to previously present tokens, proportional to how many times they have already appeared in the prompt or prior generation.
//
// Using `frequency_penalty` in combination with `presence_penalty` is not supported on newer models.
FrequencyPenalty *float64 `json:"frequency_penalty,omitempty" url:"-"`
// Defaults to `0.0`, min value of `0.0`, max value of `1.0`.
//
// Can be used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty`, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies.
//
// Using `frequency_penalty` in combination with `presence_penalty` is not supported on newer models.
PresencePenalty *float64 `json:"presence_penalty,omitempty" url:"-"`
// One of `GENERATION|NONE` to specify how and if the token likelihoods are returned with the response. Defaults to `NONE`.
//
// If `GENERATION` is selected, the token likelihoods will only be provided for generated text.
//
// WARNING: `ALL` is deprecated, and will be removed in a future release.
ReturnLikelihoods *GenerateStreamRequestReturnLikelihoods `json:"return_likelihoods,omitempty" url:"-"`
// When enabled, the user's prompt will be sent to the model without any pre-processing.
RawPrompting *bool `json:"raw_prompting,omitempty" url:"-"`
stream bool
}
func (g *GenerateStreamRequest) Stream() bool {
return g.stream
}
func (g *GenerateStreamRequest) UnmarshalJSON(data []byte) error {
type unmarshaler GenerateStreamRequest
var body unmarshaler
if err := json.Unmarshal(data, &body); err != nil {
return err
}
*g = GenerateStreamRequest(body)
g.stream = true
return nil
}
func (g *GenerateStreamRequest) MarshalJSON() ([]byte, error) {
type embed GenerateStreamRequest
var marshaler = struct {
embed
Stream bool `json:"stream"`
}{
embed: embed(*g),
Stream: true,
}
return json.Marshal(marshaler)
}
type RerankRequest struct {
// The identifier of the model to use, eg `rerank-v3.5`.
Model *string `json:"model,omitempty" url:"-"`
// The search query
Query string `json:"query" url:"-"`
// A list of document objects or strings to rerank.
// If a document is provided the text fields is required and all other fields will be preserved in the response.
//
// The total max chunks (length of documents * max_chunks_per_doc) must be less than 10000.
//
// We recommend a maximum of 1,000 documents for optimal endpoint performance.
Documents []*RerankRequestDocumentsItem `json:"documents,omitempty" url:"-"`
// The number of most relevant documents or indices to return, defaults to the length of the documents
TopN *int `json:"top_n,omitempty" url:"-"`
// If a JSON object is provided, you can specify which keys you would like to have considered for reranking. The model will rerank based on order of the fields passed in (i.e. rank_fields=['title','author','text'] will rerank using the values in title, author, text sequentially. If the length of title, author, and text exceeds the context length of the model, the chunking will not re-consider earlier fields). If not provided, the model will use the default text field for ranking.
RankFields []string `json:"rank_fields,omitempty" url:"-"`
// - If false, returns results without the doc text - the api will return a list of {index, relevance score} where index is inferred from the list passed into the request.
// - If true, returns results with the doc text passed in - the api will return an ordered list of {index, text, relevance score} where index + text refers to the list passed into the request.
ReturnDocuments *bool `json:"return_documents,omitempty" url:"-"`
// The maximum number of chunks to produce internally from a document
MaxChunksPerDoc *int `json:"max_chunks_per_doc,omitempty" url:"-"`
}
type SummarizeRequest struct {
// The text to generate a summary for. Can be up to 100,000 characters long. Currently the only supported language is English.
Text string `json:"text" url:"-"`
// One of `short`, `medium`, `long`, or `auto` defaults to `auto`. Indicates the approximate length of the summary. If `auto` is selected, the best option will be picked based on the input text.
Length *SummarizeRequestLength `json:"length,omitempty" url:"-"`
// One of `paragraph`, `bullets`, or `auto`, defaults to `auto`. Indicates the style in which the summary will be delivered - in a free form paragraph or in bullet points. If `auto` is selected, the best option will be picked based on the input text.
Format *SummarizeRequestFormat `json:"format,omitempty" url:"-"`
// The identifier of the model to generate the summary with. Currently available models are `command` (default), `command-nightly` (experimental), `command-light`, and `command-light-nightly` (experimental). Smaller, "light" models are faster, while larger models will perform better.
Model *string `json:"model,omitempty" url:"-"`
// One of `low`, `medium`, `high`, or `auto`, defaults to `auto`. Controls how close to the original text the summary is. `high` extractiveness summaries will lean towards reusing sentences verbatim, while `low` extractiveness summaries will tend to paraphrase more. If `auto` is selected, the best option will be picked based on the input text.
Extractiveness *SummarizeRequestExtractiveness `json:"extractiveness,omitempty" url:"-"`
// Ranges from 0 to 5. Controls the randomness of the output. Lower values tend to generate more “predictable” output, while higher values tend to generate more “creative” output. The sweet spot is typically between 0 and 1.
Temperature *float64 `json:"temperature,omitempty" url:"-"`
// A free-form instruction for modifying how the summaries get generated. Should complete the sentence "Generate a summary _". Eg. "focusing on the next steps" or "written by Yoda"
AdditionalCommand *string `json:"additional_command,omitempty" url:"-"`
}
type TokenizeRequest struct {
// The string to be tokenized, the minimum text length is 1 character, and the maximum text length is 65536 characters.
Text string `json:"text" url:"-"`
// An optional parameter to provide the model name. This will ensure that the tokenization uses the tokenizer used by that model.
Model string `json:"model" url:"-"`
}
type ApiMeta struct {
ApiVersion *ApiMetaApiVersion `json:"api_version,omitempty" url:"api_version,omitempty"`
BilledUnits *ApiMetaBilledUnits `json:"billed_units,omitempty" url:"billed_units,omitempty"`
Tokens *ApiMetaTokens `json:"tokens,omitempty" url:"tokens,omitempty"`
Warnings []string `json:"warnings,omitempty" url:"warnings,omitempty"`
extraProperties map[string]interface{}
rawJSON json.RawMessage
}
func (a *ApiMeta) GetApiVersion() *ApiMetaApiVersion {
if a == nil {
return nil
}
return a.ApiVersion
}
func (a *ApiMeta) GetBilledUnits() *ApiMetaBilledUnits {
if a == nil {
return nil
}
return a.BilledUnits
}
func (a *ApiMeta) GetTokens() *ApiMetaTokens {
if a == nil {
return nil
}
return a.Tokens
}
func (a *ApiMeta) GetWarnings() []string {
if a == nil {
return nil
}
return a.Warnings
}
func (a *ApiMeta) GetExtraProperties() map[string]interface{} {
return a.extraProperties
}
func (a *ApiMeta) UnmarshalJSON(data []byte) error {
type unmarshaler ApiMeta
var value unmarshaler
if err := json.Unmarshal(data, &value); err != nil {
return err
}
*a = ApiMeta(value)
extraProperties, err := internal.ExtractExtraProperties(data, *a)
if err != nil {
return err
}
a.extraProperties = extraProperties
a.rawJSON = json.RawMessage(data)
return nil
}
func (a *ApiMeta) String() string {
if len(a.rawJSON) > 0 {
if value, err := internal.StringifyJSON(a.rawJSON); err == nil {
return value
}
}
if value, err := internal.StringifyJSON(a); err == nil {
return value
}
return fmt.Sprintf("%#v", a)
}
type ApiMetaApiVersion struct {
Version string `json:"version" url:"version"`
IsDeprecated *bool `json:"is_deprecated,omitempty" url:"is_deprecated,omitempty"`
IsExperimental *bool `json:"is_experimental,omitempty" url:"is_experimental,omitempty"`
extraProperties map[string]interface{}
rawJSON json.RawMessage
}
func (a *ApiMetaApiVersion) GetVersion() string {
if a == nil {
return ""
}
return a.Version
}
func (a *ApiMetaApiVersion) GetIsDeprecated() *bool {
if a == nil {
return nil
}
return a.IsDeprecated
}
func (a *ApiMetaApiVersion) GetIsExperimental() *bool {
if a == nil {
return nil
}
return a.IsExperimental
}
func (a *ApiMetaApiVersion) GetExtraProperties() map[string]interface{} {
return a.extraProperties
}
func (a *ApiMetaApiVersion) UnmarshalJSON(data []byte) error {
type unmarshaler ApiMetaApiVersion
var value unmarshaler
if err := json.Unmarshal(data, &value); err != nil {
return err
}
*a = ApiMetaApiVersion(value)
extraProperties, err := internal.ExtractExtraProperties(data, *a)
if err != nil {
return err
}
a.extraProperties = extraProperties
a.rawJSON = json.RawMessage(data)
return nil
}
func (a *ApiMetaApiVersion) String() string {
if len(a.rawJSON) > 0 {
if value, err := internal.StringifyJSON(a.rawJSON); err == nil {
return value
}
}
if value, err := internal.StringifyJSON(a); err == nil {
return value
}
return fmt.Sprintf("%#v", a)
}
type ApiMetaBilledUnits struct {
// The number of billed images.
Images *float64 `json:"images,omitempty" url:"images,omitempty"`
// The number of billed input tokens.
InputTokens *float64 `json:"input_tokens,omitempty" url:"input_tokens,omitempty"`
// The number of billed output tokens.
OutputTokens *float64 `json:"output_tokens,omitempty" url:"output_tokens,omitempty"`
// The number of billed search units.
SearchUnits *float64 `json:"search_units,omitempty" url:"search_units,omitempty"`
// The number of billed classifications units.
Classifications *float64 `json:"classifications,omitempty" url:"classifications,omitempty"`
extraProperties map[string]interface{}
rawJSON json.RawMessage
}
func (a *ApiMetaBilledUnits) GetImages() *float64 {
if a == nil {
return nil
}
return a.Images
}
func (a *ApiMetaBilledUnits) GetInputTokens() *float64 {
if a == nil {
return nil
}
return a.InputTokens
}
func (a *ApiMetaBilledUnits) GetOutputTokens() *float64 {
if a == nil {
return nil
}
return a.OutputTokens
}
func (a *ApiMetaBilledUnits) GetSearchUnits() *float64 {
if a == nil {
return nil
}
return a.SearchUnits
}
func (a *ApiMetaBilledUnits) GetClassifications() *float64 {
if a == nil {
return nil
}
return a.Classifications
}
func (a *ApiMetaBilledUnits) GetExtraProperties() map[string]interface{} {
return a.extraProperties
}
func (a *ApiMetaBilledUnits) UnmarshalJSON(data []byte) error {
type unmarshaler ApiMetaBilledUnits
var value unmarshaler
if err := json.Unmarshal(data, &value); err != nil {
return err
}
*a = ApiMetaBilledUnits(value)
extraProperties, err := internal.ExtractExtraProperties(data, *a)
if err != nil {
return err
}
a.extraProperties = extraProperties
a.rawJSON = json.RawMessage(data)
return nil
}
func (a *ApiMetaBilledUnits) String() string {
if len(a.rawJSON) > 0 {
if value, err := internal.StringifyJSON(a.rawJSON); err == nil {
return value
}
}
if value, err := internal.StringifyJSON(a); err == nil {
return value
}
return fmt.Sprintf("%#v", a)
}
type ApiMetaTokens struct {
// The number of tokens used as input to the model.
InputTokens *float64 `json:"input_tokens,omitempty" url:"input_tokens,omitempty"`
// The number of tokens produced by the model.