import { ProtocolError, TMessage, TNotification } from "../protocol";
import { PitcherErrorCode } from "../errors";
export type CommonError = ProtocolError | {
    code: PitcherErrorCode.AI_NOT_AVAILABLE;
    message: string;
};
export type SuggestCommitMessage = TMessage<"ai/suggestCommit", {
    /**
     * Ability to pass a model for a/b testing
     * will be removed in the future
     **/
    model?: "gpt-3.5-turbo" | "gpt-4";
    files: string[];
    temperature?: number;
}, {
    result: {
        commit: string;
    };
    error: CommonError;
}>;
export type RawMessage = TMessage<"ai/raw", {
    /**
     * Ability to pass a model for a/b testing
     * will be removed in the future
     **/
    model?: "gpt-3.5-turbo" | "gpt-4" | "gpt-4-1106-preview";
    messages: Array<{
        role: "system" | "user" | "assistant";
        content: string;
    }>;
    /**
     * The maximum number of tokens allowed for the generated answer. By default, the number of tokens the model can return will be (4096 - prompt tokens).
     */
    max_tokens?: number;
    /**
     * 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.
     */
    temperature?: number;
    /**
     * An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.  We generally recommend altering this or `temperature` but not both.
     */
    top_p?: number;
    /**
     * 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.  [See more information about frequency and presence penalties.](/docs/api-reference/parameter-details)
     */
    presence_penalty?: number;
    /**
     * 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.  [See more information about frequency and presence penalties.](/docs/api-reference/parameter-details)
     */
    frequency_penalty?: number;
    /**
     * a token to force the completion to end.
     */
    stop?: string | string[];
}, {
    result: {
        reply: string;
    };
    error: CommonError;
}>;
export type StreamMessage = TMessage<"ai/stream", {
    /**
     * Ability to pass a model for a/b testing
     * will be removed in the future
     **/
    model?: "gpt-3.5-turbo" | "gpt-4";
    messages: Array<{
        role: "system" | "user" | "assistant";
        content: string;
    }>;
    /**
     * The maximum number of tokens allowed for the generated answer. By default, the number of tokens the model can return will be (4096 - prompt tokens).
     */
    max_tokens?: number;
    /**
     * 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.
     */
    temperature?: number;
    /**
     * An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.  We generally recommend altering this or `temperature` but not both.
     */
    top_p?: number;
    /**
     * 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.  [See more information about frequency and presence penalties.](/docs/api-reference/parameter-details)
     */
    presence_penalty?: number;
    /**
     * 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.  [See more information about frequency and presence penalties.](/docs/api-reference/parameter-details)
     */
    frequency_penalty?: number;
    /**
     * a token to force the completion to end.
     */
    stop?: string | string[];
}, {
    result: {
        messageId: string;
    };
    error: CommonError;
}>;
export type IChatContextFragment = {
    type: "text" | "logs";
    content: string;
} | {
    type: "code";
    content: string;
    filepath: string;
    startLine: number;
    endLine: number;
};
export type AIChatMessageMessage = TMessage<"ai/chatMessage", {
    chatId: string;
    message: string;
    messageId: string;
    context: IChatContextFragment[];
}, {
    result: {
        chatId: string;
        title: string;
    };
    error: CommonError;
}>;
export type AIChatCreatedNotification = TNotification<"ai/chatCreated", {
    chatId: string;
    title: string;
    entries: IChatHistoryEntry[];
}>;
export type AIChatMessageNotification = TNotification<"ai/chatMessage", {
    chatId: string;
    /**
     * idx, indicating the order of the messages, ensures we can resync if a message goes missing
     **/
    idx: number;
    /**
     * User is manual input by a user
     * System is intermediate messages, status, errors
     * Assistant is the AI responding
     **/
    role: "user" | "system" | "assistant";
    username?: string;
    /**
     * messageId is a unique identifier for the message used to listen for streaming progress
     */
    messageId: string;
    message: string;
    context: IChatContextFragment[];
    isFinished: boolean;
}>;
export type AIMessageProgressNotification = TNotification<"ai/messageProgress", {
    messageId: string;
    chunk: string;
    isFinished: boolean;
}>;
export interface IChatHistoryEntry {
    /**
     * idx, indicating the order of the messages, ensures we can resync if a message goes missing
     **/
    idx: number;
    /**
     * User is manual input by a user
     * System is intermediate messages, status, errors
     * Assistant is the AI responding
     **/
    role: "user" | "system" | "assistant";
    username?: string;
    /**
     * messageId is a unique identifier for the message used to listen for streaming progress
     */
    messageId: string;
    message: string;
    context: IChatContextFragment[];
    isFinished: boolean;
}
export type AIChatsMessage = TMessage<"ai/chats", Record<never, never>, {
    result: {
        chats: Array<{
            chatId: string;
            title: string;
        }>;
    };
    error: CommonError;
}>;
export type AiChatHistoryMessage = TMessage<"ai/chatHistory", {
    chatId: string;
}, {
    result: {
        entries: IChatHistoryEntry[];
    };
    error: CommonError;
}>;
export type AiMessageStateMessage = TMessage<"ai/messageState", {
    messageId: string;
}, {
    result: {
        content: string;
        isFinished: boolean;
    };
    error: CommonError;
}>;
export interface IEmbeddingRecord {
    filepath: string;
    filetype: "code" | "manifest" | "doc";
    content: string;
    startLine: number;
    endLine: number;
    /** cosine distance between vector and question */
    distance: number;
}
export type AIEmbeddingsMessage = TMessage<"ai/embeddings", {
    query: string;
}, {
    result: {
        matches: IEmbeddingRecord[];
    };
    error: CommonError;
}>;
export type AiMessage = SuggestCommitMessage | RawMessage | StreamMessage | AIChatMessageMessage | AIChatsMessage | AiChatHistoryMessage | AIEmbeddingsMessage | AiMessageStateMessage;
export type AiRequest = AiMessage["request"];
export type AiResponse = AiMessage["response"];
export type AiNotification = AIChatCreatedNotification | AIChatMessageNotification | AIMessageProgressNotification;