chat

package
v0.0.3 Latest Latest
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Published: Oct 1, 2025 License: MPL-2.0 Imports: 4 Imported by: 0

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Constants

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Variables

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Functions

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Types

type Content added in v0.0.3

type Content struct {
	Text  string `json:"text,omitempty"`
	Image *Image `json:"image,omitempty"`
}

func NewImageContent added in v0.0.3

func NewImageContent(imageContent []byte) Content

func NewImageUrlContent added in v0.0.3

func NewImageUrlContent(url string, detail ...ImageDetailLevel) Content

func NewTextContent added in v0.0.3

func NewTextContent(text string) Content

func (Content) AsSlice added in v0.0.3

func (c Content) AsSlice() []Content

type ContentType added in v0.0.3

type ContentType string

type Image added in v0.0.3

type Image struct {
	Content []byte `json:"buffer,omitempty"`
	Url     string `json:"url,omitempty"`
	// OpenAI: The level of detail for the image description. Options are "auto", "low", "medium", and "high". Default is "auto".
	Detail ImageDetailLevel `json:"detail,omitempty"`
}

type ImageDetailLevel added in v0.0.3

type ImageDetailLevel string
const (
	ImageDetailAuto   ImageDetailLevel = "auto"
	ImageDetailLow    ImageDetailLevel = "low"
	ImageDetailMedium ImageDetailLevel = "medium"
	ImageDetailHigh   ImageDetailLevel = "high"
)

type Message

type Message struct {
	Role      Role    `json:"role"`
	Content   Content `json:"content"`
	Reasoning string  `json:"reasoning"`
	// Assistant tool calls
	ToolCalls []ToolCall `json:"tool_calls"`
	// OpenAI: An optional name for the participant. Provides the model information to differentiate between participants of the same role.
	Name string `json:"name"`
	// OpenAI: Assistant refusal message
	Refusal string `json:"refusal"`
	// Anthropic: User boolean indicating whether function call resulted in an error.
	IsErr bool `json:"is_err"`
}

type Options

type Options struct {
	// OpenAI: Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
	FrequencyPenalty *float64
	// OpenAI: Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`.
	Logprobs bool
	// OpenAI: An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and [reasoning tokens](https://platform.openai.com/docs/guides/reasoning).
	MaxCompletionTokens uint
	// The maximum number of tokens to generate before stopping.
	MaxTokens uint
	// OpenAI: How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep `n` as `1` to minimize costs.
	// N *uint
	// OpenAI: Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
	PresencePenalty float64
	// OpenAI: This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same `seed` and parameters should return the same result. Determinism is not guaranteed, and you should refer to the `system_fingerprint` response parameter to monitor changes in the backend.
	Seed *int64
	// OpenAI: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or `top_p` but not both.
	// Anthropic: Amount of randomness injected into the response. Defaults to `1.0`. Ranges from `0.0` to `1.0`. Use `temperature` closer to `0.0` for analytical / multiple choice, and closer to `1.0` for creative and generative tasks. Note that even with `temperature` of `0.0`, the results will not be fully deterministic.
	Temperature *float64
	// OpenAI: An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to `true` if this parameter is used.
	TopLogprobs *int32
	TopP        *float64
	TopK        *float32
	// OpenAI: Whether to enable [parallel function calling](https://platform.openai.com/docs/guides/function-calling#configuring-parallel-function-calling) during tool use.
	ParallelToolCalls *bool
	// OpenAI: Cache responses for similar requests to optimize your cache hit rates. Replaces the `user` field.
	// Google:  Resource name of a context cache that can be used in subsequent requests.
	PromptCacheKey string
	// OpenAI: A stable identifier used to help detect users of your application that may be violating OpenAI's usage policies. The IDs should be a string that uniquely identifies each user. We recommend hashing their username or email address, in order to avoid sending us any identifying information. [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#safety-identifiers).
	SafetyIdentifier string
	// OpenAI: This field is being replaced by `safety_identifier` and `prompt_cache_key`. Use `prompt_cache_key` instead to maintain caching optimizations. A stable identifier for your end-users. Used to boost cache hit rates by better bucketing similar requests and to help OpenAI detect and prevent abuse. [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#safety-identifiers).
	User string
	// OpenAI: Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
	LogitBias map[string]int64
	// OpenAI: **o-series models only** Constrains effort on reasoning for [reasoning models](https://platform.openai.com/docs/guides/reasoning). (Accepts [ReasoningEffortUnion.ofString])
	// Ollama: Think controls whether thinking/reasoning models will think before responding. (Accepts both [ReasoningEffortUnion.ofString] and [ReasoningEffortUnion.ofBool])
	ReasoningEffort *ReasoningEffortUnion
	// OpenAI: Specifies the processing type used for serving the request. Any of "auto", "default", "flex", "scale", "priority".
	// Anthropic: Any of "auto", "standard_only"
	ServiceTier string
	// OpenAI: Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
	// Anthropic: Custom text sequences that will cause the model to stop generating.
	Stop []string
	// Schema specifying the format that the model must output.
	//
	// *Supported providers*: OpenAI, Ollama
	ResponseFormat *jsonschema.Schema
	// Anthropic: Enable extended thinking. Must be ≥1024 and less than `max_tokens`.
	Thinking uint64
	// Ollama: How long the model will stay loaded into memory following the request.
	KeepAlive *time.Duration
	// Google:
	//     - `text/plain` (default)
	//     - `application/json`
	ResponseMIMEType string
	// OpenAI: Include usage statistics in streaming mode.
	IncludeStreamMetrics bool
	// l337: Controls channel buffering for streaming responses. Defaults to `0` (unbuffered).
	StreamingBufferSize int
}

type Parameter

type Parameter interface {
	Apply(*Parameters) error
}

func WithImageContentMessage added in v0.0.3

func WithImageContentMessage(role Role, imageContent []byte) Parameter

func WithImageUrlMessage added in v0.0.3

func WithImageUrlMessage(role Role, imageURL string) Parameter

func WithSessionID

func WithSessionID(sessionID uuid.UUID) Parameter

func WithTextMessage added in v0.0.3

func WithTextMessage(role Role, content string) Parameter

type ParameterFunc

type ParameterFunc func(*Parameters) error

func (ParameterFunc) Apply

func (s ParameterFunc) Apply(r *Parameters) error

type Parameters

type Parameters struct {
	Messages  []Message
	SessionID uuid.UUID
}

type ReasoningEffortLevel

type ReasoningEffortLevel string
const (
	ReasoningEffortLow    ReasoningEffortLevel = "low"
	ReasoningEffortMedium ReasoningEffortLevel = "medium"
	ReasoningEffortHigh   ReasoningEffortLevel = "high"
)

type ReasoningEffortUnion

type ReasoningEffortUnion struct {
	// contains filtered or unexported fields
}

Only one can be set

func NewReasoningEffortBool

func NewReasoningEffortBool(enabled bool) *ReasoningEffortUnion

func NewReasoningEffortLevel

func NewReasoningEffortLevel(level ReasoningEffortLevel) *ReasoningEffortUnion

func (*ReasoningEffortUnion) AsAny

func (r *ReasoningEffortUnion) AsAny() any

func (*ReasoningEffortUnion) AsBool

func (r *ReasoningEffortUnion) AsBool() (bool, bool)

func (*ReasoningEffortUnion) AsLevel

type Role

type Role string
const (
	RoleAssistant Role = "assistant"
	RoleDeveloper Role = "developer"
	RoleSystem    Role = "system"
	RoleTool      Role = "tool"
	RoleUser      Role = "user"
	RoleModel     Role = "model"
)

func (Role) String

func (r Role) String() string

type RunResponse

type RunResponse struct {
	SessionID uuid.UUID                       `json:"session_id"`
	Messages  []Message                       `json:"messages"`
	Metrics   map[uuid.UUID][]metrics.Metrics `json:"metrics"`
}

func (*RunResponse) Content

func (r *RunResponse) Content() Content

Returns the content of the last message in the response.

type ToolCall

type ToolCall struct {
	// Unique identifier for the tool call.
	ID string `json:"id"`
	// Raw LLM arguments.
	Arguments string `json:"arguments"`
	// Tool name
	Name string `json:"name"`
}

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