openai

Documentation

Index

• Variables
• func NewClient() func(NewClientParams) (Client, error)
• type AnySummarizationInputs
• type ChatHistorySummarizationOutputs
• type ChatHistorySummarizationOutputsDiscussion
• type ChatHistorySummarizationPromptInputs
  • func NewChatHistorySummarizationPromptInputs(chatHistory string, language string) *ChatHistorySummarizationPromptInputs
• type Client
• type NewClientParams
• type OpenAIClient
  • func (c *OpenAIClient) SplitContentBasedByTokenLimitations(textContent string, limits int) []string
  • func (c *OpenAIClient) SummarizeAny(ctx context.Context, content string) (*openai.ChatCompletionResponse, error)
  • func (c *OpenAIClient) SummarizeChatHistories(ctx context.Context, llmFriendlyChatHistories string) (*openai.ChatCompletionResponse, error)
  • func (c *OpenAIClient) SummarizeOneChatHistory(ctx context.Context, llmFriendlyChatHistory string) (*openai.ChatCompletionResponse, error)
  • func (c *OpenAIClient) SummarizeWithQuestionsAsSimplifiedChinese(ctx context.Context, title, by, content string) (*openai.ChatCompletionResponse, error)
  • func (c *OpenAIClient) TruncateContentBasedOnTokens(textContent string, limits int) string

Variables

var AnySummarizationPrompt = lo.Must(template.New("anything summarization prompt").Parse("" +
	`内容：{{ .Content }}
你是我的总结助手。我将为你提供一段话，我需要你在不丢失原文主旨和情感、不做更多的解释和说明的情况下帮我用不超过100字总结一下这段话说了什么。`))

var ChatHistorySummarizationPrompt = lo.Must(template.New("chat histories summarization prompt").Parse("" +
	`Chat histories:"""
{{ .ChatHistory }}
"""

You are a expert in summarizing the refined outlines from documents and dialogues. Please read through the provided chat history and identify the 1-10 distinct discussion topics that discussed and talked about. Summarize only those key topics by extracting the most relevant points and message IDs.

Output topics in the following JSON format in language {{ .Language }}:"""
[{"topicName":"Most Important Topic 1","sinceId":123456789,"participantsNamesWithoutUsername":["John","Mary"],"discussion":[{"point":"Most relevant key point","keyIds":[123456789,987654321]}],"conclusion":"Optional brief conclusion"},{"topicName":"Most Important Topic 2","sinceId":987654321,"participantsNamesWithoutUsername":["Bob","Alice"],"discussion":[{"point":"Most relevant key point","keyIds":[987654321]}],"conclusion":"Optional brief conclusion"}]
"""

Only summarize the 1-10 distinct topics from the chat history. For each topic, extract just the most relevant point and key message IDs. Be very concise and focused on the key essence of each topic.`))

Functions

func NewClient

func NewClient() func(NewClientParams) (Client, error)

Types

type AnySummarizationInputs

type AnySummarizationInputs struct {
	Content string
}

type ChatHistorySummarizationOutputs

type ChatHistorySummarizationOutputs struct {
	TopicName                        string                                       `json:"topicName"`
	SinceID                          int64                                        `json:"sinceId"`
	ParticipantsNamesWithoutUsername []string                                     `json:"participantsNamesWithoutUsername"`
	Discussion                       []*ChatHistorySummarizationOutputsDiscussion `json:"discussion"`
	Conclusion                       string                                       `json:"conclusion"`
}

type ChatHistorySummarizationOutputsDiscussion

type ChatHistorySummarizationOutputsDiscussion struct {
	Point  string  `json:"point"`
	KeyIDs []int64 `json:"keyIds"`
}

type ChatHistorySummarizationPromptInputs

type ChatHistorySummarizationPromptInputs struct {
	ChatHistory string
	Language    string
}

func NewChatHistorySummarizationPromptInputs

func NewChatHistorySummarizationPromptInputs(chatHistory string, language string) *ChatHistorySummarizationPromptInputs

type Client

type Client interface {
	SplitContentBasedByTokenLimitations(textContent string, limits int) []string
	SummarizeAny(ctx context.Context, content string) (*openai.ChatCompletionResponse, error)
	SummarizeChatHistories(ctx context.Context, llmFriendlyChatHistories string) (*openai.ChatCompletionResponse, error)
	SummarizeOneChatHistory(ctx context.Context, llmFriendlyChatHistory string) (*openai.ChatCompletionResponse, error)
	SummarizeWithQuestionsAsSimplifiedChinese(ctx context.Context, title string, by string, content string) (*openai.ChatCompletionResponse, error)
	TruncateContentBasedOnTokens(textContent string, limits int) string
}

type NewClientParams

type NewClientParams struct {
	fx.In

	Config *configs.Config
	Logger *logger.Logger
	Ent    *datastore.Ent
}

type OpenAIClient

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

func (*OpenAIClient) SplitContentBasedByTokenLimitations

func (c *OpenAIClient) SplitContentBasedByTokenLimitations(textContent string, limits int) []string

SplitContentBasedByTokenLimitations 基于 token 计算的方式分割文本。

func (*OpenAIClient) SummarizeAny

func (c *OpenAIClient) SummarizeAny(ctx context.Context, content string) (*openai.ChatCompletionResponse, error)

SummarizeAny 通过 OpenAI 的 Chat API 来为任意内容生成摘要。

func (*OpenAIClient) SummarizeChatHistories

func (c *OpenAIClient) SummarizeChatHistories(ctx context.Context, llmFriendlyChatHistories string) (*openai.ChatCompletionResponse, error)

func (*OpenAIClient) SummarizeOneChatHistory

func (c *OpenAIClient) SummarizeOneChatHistory(ctx context.Context, llmFriendlyChatHistory string) (*openai.ChatCompletionResponse, error)

func (*OpenAIClient) SummarizeWithQuestionsAsSimplifiedChinese

func (c *OpenAIClient) SummarizeWithQuestionsAsSimplifiedChinese(ctx context.Context, title, by, content string) (*openai.ChatCompletionResponse, error)

SummarizeWithQuestionsAsSimplifiedChinese 通过 OpenAI 的 Chat API 来为文章生成摘要和联想问题。

func (*OpenAIClient) TruncateContentBasedOnTokens

func (c *OpenAIClient) TruncateContentBasedOnTokens(textContent string, limits int) string

truncateContentBasedOnTokens 基于 token 计算的方式截断文本。