import { OpenAIApi, CreateEmbeddingRequest, Configuration } from 'openai';
import GPT3Tokenizer from 'gpt3-tokenizer';

interface Parser<T extends any> {
    parse(text: string): T;
}
declare class NoopParser implements Parser<string> {
    parse(text: string): string;
}
declare class JSONParser implements Parser<any> {
    /**
     * Parses JSON text into an object
     *
     * @example
     * const parser = new JSONParser();
     * parser.parse('{"a": 1, "b": 2, "c": 3}'); // outputs {a: 1, b: 2, c: 3}
     *
     * @param text a string of JSON text
     * @returns an object
     */
    parse(text: string): any;
}
/**
 * Parser that parses CSV text into an array of objects
 */
declare class CSVParser implements Parser<any> {
    /**
     * Parses CSV text into an array of objects
     *
     * @example
     * const parser = new CSVParser();
     * parser.parse("a,b,c\n1,2,3"); // outputs [{a: 1, b: 2, c: 3}]
     *
     * @param text a string of CSV text
     * @returns an array of objects
     */
    parse(text: string): any;
}
declare class ListParser implements Parser<Array<any>> {
    /**
     * Parses a list of items separated by a character
     *
     * @example
     * const parser = new ListParser();
     * parser.parse("a, b, c"); // outputs ["a", "b", "c"]
     *
     * @param text a string of items separated by a character
     * @param char a character to split the text by
     * @returns an array of items
     */
    parse(text: string, char?: string): string[];
}

declare class Prompt<T extends string = string, P extends Parser<any> = NoopParser> implements Parser<any> {
    text: string;
    variableNames: T[];
    private parser;
    constructor(text: string, variableNames: T[], parser?: P);
    parse(completion: string): any;
    format(variables: Record<T, string>): string;
    toJson(): {
        text: string;
        variableNames: T[];
    };
}

declare enum ModelProviderType {
    OpenAI = 0
}
declare abstract class ModelProvider {
    type: ModelProviderType;
    constructor(type: ModelProviderType);
}
interface CompletionsModelProvider extends ModelProvider {
    generate<T extends string>(promptText: string, ...args: any[]): Promise<string>;
}
interface EmbeddingsModelProvider extends ModelProvider {
    embed(texts: string[], ...args: any[]): Promise<number[][]>;
    embed(text: string, ...args: any[]): Promise<number[]>;
}
interface Tokenizer {
    encode(text: string): {
        tokens: number[];
        texts: string[];
    };
    decode(tokens: number[]): string;
    truncate(text: string, maxTokens: number): string;
    countTokens(text: string): number;
    countDocumentTokens(doc: Document): number;
}

declare class LLMChain<T extends string = string, P extends Parser<any> = NoopParser> {
    prompt: Prompt<T, P>;
    provider: CompletionsModelProvider;
    constructor(prompt: Prompt<T, P>, provider: CompletionsModelProvider);
    protected _run(variables: Record<T, string>): Promise<any>;
    run(variables: Record<T, string>): Promise<any>;
}

declare class BufferedChatMemory implements Memory {
    protected botName: string;
    protected userName: string;
    protected startingSpeaker: "user" | "bot";
    protected maxInteractionTurns: number;
    botMessages: string[];
    userMessages: string[];
    clear(): void;
    constructor(botName?: string, userName?: string, startingSpeaker?: "user" | "bot", maxInteractionTurns?: number);
    /**
     * get the interaction history
     *
     * @example
     *
     * const memory = new BufferedChatMemory();
     *
     * memory.addBotMessage("Hello");
     * memory.addUserMessage("Hi");
     * memory.addBotMessage("How are you?");
     * memory.addUserMessage("I'm fine, thanks");
     *
     * memory.get();
     * // Assistant: Hello
     * // User: Hi
     * // Assistant: How are you?
     * // User: I'm fine, thanks
     *
     *
     *
     * @returns a string containing the interaction history
     */
    get(): string;
    addBotMessage(botMessage: string): void;
    addUserMessage(userMessage: string): void;
}

interface Memory {
    get: () => string;
    clear: () => void;
}

declare class MemoryLLMChain<T extends "memory" | "userInput", P extends Parser<any> = NoopParser> extends LLMChain<T, P> {
    prompt: Prompt<T, P>;
    provider: CompletionsModelProvider;
    memory: Memory;
    constructor(prompt: Prompt<T, P>, provider: CompletionsModelProvider, memory: Memory);
    protected _run(variables: Record<T, string>): Promise<any>;
    run(variables: Omit<Record<T, string>, "memory">): Promise<any>;
}

interface EmbeddingsOptions {
    cacheDir?: string;
}
interface QueryResult {
    query: string | number[];
    document: Document;
    similarity: number;
}
declare class Embeddings {
    key: string;
    cacheDir: string;
    provider: EmbeddingsModelProvider;
    documents: Document[];
    embeddings: number[][];
    constructor(key: string, provider: EmbeddingsModelProvider, documents: Document[], options?: EmbeddingsOptions);
    isCached(): boolean;
    clearCache(): void;
    load(): void;
    index(embeddings?: number[][]): Promise<void>;
    isInitialized(): boolean;
    query(query: number[], k: number): Promise<QueryResult[]>;
    query(query: string, k: number): Promise<QueryResult[]>;
    update(data: string[]): void;
    delete(data: string[]): void;
    save(): void;
}

/**
 *
Given the following document, answer the question if you can.
If you don\'t have enough information, don't return anything.

Document:
{{document}}

Question:
{{question}}

Answer:`
 */
declare const QA: () => Prompt<"document" | "question", NoopParser>;
/**
 *Use the following portion of a long document to see if any of the text is relevant to answer the question.

Return any relevant text verbatim.

{{document}}

Question: {{question}}

Relevant text, if any:
 */
declare const extractText: () => Prompt<"document" | "question", NoopParser>;
/**
 * Write a concise summary of the document below:

{{document}}

Summary:
 */
declare const summarize: () => Prompt<"document", NoopParser>;
/**
You are Assistant. Help the user as much as possible.

{{memory}}
User: {{userInput}}
Assistant:
*/
declare const chatbot: () => Prompt<"memory" | "userInput", NoopParser>;
/**
  Given this typescript type return a valid, stringified JSON Object representing an instance of the type.
  Make sure the response is JUST the object. Not a variable or anything else.
  Examples:

  Correct:
  {
    "name": "John",
  }

  Wrong:
  const data = {
    "name": "John",
  }

  Note:
  - undefined and null is not a valid json fields, instead, just leave out the field
  - use a random stringified iso date format for dates
  - optional, undefined or nullable fields in the response mean that you can skip them OR you can add them. Choose randomly.
   
  
  Okay, here's the type:
  {{type}}

  const VALID_STRINGIFIED_JSON=
 */
declare const extractJSON: () => Prompt<"data" | "type", JSONParser>;
/**
Given this data and a list of headers, return a CSV file containing the column data.
    Example:
  
    Data: Hey my name is Colin and I'm a 29 year old software engineer.
    Headers: Name,
    CSV:
    Name,Age,Occupation
    Colin,29,Software Engineer
  
    Notes:
    - undefined and null are not valid csv fields, instead, just leave out the field.
    - convert date information to stringified iso date format for dates.
    - optional, undefined or nullable fields in the response mean that you can skip them OR you can add them depending on the data.
    - the first row of the csv should be the headers
  
    ---
     
    Okay, here is the Data and the Headers:
    Data:
    {{data}}
  
    Headers:
    {{headers}}
  
    CSV:
 */
declare const extractCSV: () => Prompt<"data" | "headers", CSVParser>;

declare const prompts_QA: typeof QA;
declare const prompts_chatbot: typeof chatbot;
declare const prompts_extractCSV: typeof extractCSV;
declare const prompts_extractJSON: typeof extractJSON;
declare const prompts_extractText: typeof extractText;
declare const prompts_summarize: typeof summarize;
declare namespace prompts {
  export {
    prompts_QA as QA,
    prompts_chatbot as chatbot,
    prompts_extractCSV as extractCSV,
    prompts_extractJSON as extractJSON,
    prompts_extractText as extractText,
    prompts_summarize as summarize,
  };
}

declare class OpenAIConfiguration extends Configuration {
}
type GenerateCompletionOptions = {
    /**
     * ID of the model to use. You can use the [List models](/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](/docs/models/overview) for descriptions of them.
     * @type {string}
     * @memberof CreateCompletionRequest
     */
    model?: OPENAI_MODEL;
    /**
     * The suffix that comes after a completion of inserted text.
     * @type {string}
     * @memberof CreateCompletionRequest
     */
    suffix?: string | null;
    /**
     * The maximum number of [tokens](/tokenizer) to generate in the completion.  The token count of your prompt plus `max_tokens` cannot exceed the model\'s context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096).
     * @type {number}
     * @memberof CreateCompletionRequest
     */
    max_tokens?: number | null;
    /**
     * What [sampling temperature](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277) to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.  We generally recommend altering this or `top_p` but not both.
     * @type {number}
     * @memberof CreateCompletionRequest
     */
    temperature?: number | null;
    /**
     * 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.
     * @type {number}
     * @memberof CreateCompletionRequest
     */
    top_p?: number | null;
    /**
     * How many completions to generate for each prompt.  **Note:** Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for `max_tokens` and `stop`.
     * @type {number}
     * @memberof CreateCompletionRequest
     */
    n?: number | null;
    /**
     * Whether to stream back partial progress. If set, tokens will be sent as data-only [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format) as they become available, with the stream terminated by a `data: [DONE]` message.
     * @type {boolean}
     * @memberof CreateCompletionRequest
     */
    stream?: boolean | null;
    /**
     * Include the log probabilities on the `logprobs` most likely tokens, as well the chosen tokens. For example, if `logprobs` is 5, the API will return a list of the 5 most likely tokens. The API will always return the `logprob` of the sampled token, so there may be up to `logprobs+1` elements in the response.  The maximum value for `logprobs` is 5. If you need more than this, please contact us through our [Help center](https://help.openai.com) and describe your use case.
     * @type {number}
     * @memberof CreateCompletionRequest
     */
    logprobs?: number | null;
    /**
     * Echo back the prompt in addition to the completion
     * @type {boolean}
     * @memberof CreateCompletionRequest
     */
    echo?: boolean | null;
    /**
     *
     * @type {CreateCompletionRequestStop}
     * @memberof CreateCompletionRequest
     */
    stop?: string | string[] | null;
    /**
     * 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)
     * @type {number}
     * @memberof CreateCompletionRequest
     */
    presence_penalty?: number | null;
    /**
     * 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)
     * @type {number}
     * @memberof CreateCompletionRequest
     */
    frequency_penalty?: number | null;
    /**
     * Generates `best_of` completions server-side and returns the \"best\" (the one with the highest log probability per token). Results cannot be streamed.  When used with `n`, `best_of` controls the number of candidate completions and `n` specifies how many to return – `best_of` must be greater than `n`.  **Note:** Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for `max_tokens` and `stop`.
     * @type {number}
     * @memberof CreateCompletionRequest
     */
    best_of?: number | null;
    /**
     * Modify the likelihood of specified tokens appearing in the completion.  Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this [tokenizer tool](/tokenizer?view=bpe) (which works for both GPT-2 and GPT-3) to convert text to token IDs. 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.  As an example, you can pass `{\"50256\": -100}` to prevent the <|endoftext|> token from being generated.
     * @type {object}
     * @memberof CreateCompletionRequest
     */
    logit_bias?: object | null;
    /**
     * A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse. [Learn more](/docs/usage-policies/end-user-ids).
     * @type {string}
     * @memberof CreateCompletionRequest
     */
    user?: string;
};
declare class OpenAI extends ModelProvider implements CompletionsModelProvider, EmbeddingsModelProvider {
    apiKey: string;
    config: OpenAIConfiguration;
    api: OpenAIApi;
    completionsConfig: {
        model: OPENAI_MODEL;
        max_tokens: number;
        temperature: number;
        stop: null;
    };
    embeddingsConfig: OpenAIEmbeddingsConfig;
    tokenizer: OpenAITokenizer;
    constructor(apiKey: string);
    countTokens(text: string): number;
    generate(promptText: string, options?: GenerateCompletionOptions): Promise<string>;
    /**
     * NOTE: DISABLED until we can figure out how to stream the response.
     *
     * Use this on your server to stream completions from the OpenAI API.
     *
     * @param promptText
     * @param options
     * @returns
     */
    stream(promptText: string, options?: Omit<GenerateCompletionOptions, "stream">): Promise<void>;
    embed(text: string, options?: Omit<CreateEmbeddingRequest, "input">): Promise<number[]>;
    embed(texts: string[], options?: Omit<CreateEmbeddingRequest, "input">): Promise<number[][]>;
    private embedOne;
    private embedMany;
}
type OPENAI_MODEL = "text-davinci-003" | "text-davinci-002" | "code-davinci-002" | "text-curie-002";
interface OpenAIEmbeddingsConfig {
    model: string;
}
declare class OpenAITokenizer implements Tokenizer {
    private tokenizer;
    constructor(type?: "gpt3" | "codex");
    encode(text: string): {
        tokens: number[];
        texts: string[];
    };
    decode(tokens: number[]): string;
    truncate(text: string, maxTokens: number): string;
    countTokens(text: string): number;
    countDocumentTokens(doc: Document): number;
}

interface Loader {
    load(): Promise<Document[]>;
}
declare class FileLoader implements Loader {
    path: string;
    meta?: Record<string, any>;
    constructor(path: string, meta?: Record<string, any>);
    /**
     * Load a file from the filesystem
     *
     * @returns {Promise<Document[]>} A promise that resolves to an array of documents
     */
    load(): Promise<Document[]>;
}

interface TextSplitterOptions {
    lengthFn?: (text: string) => number;
    chunk?: boolean;
    chunkSize?: number;
    overlap?: number;
}
declare abstract class TextSplitter {
    chunk: boolean;
    chunkSize: number;
    overlap: number;
    protected tokenizer: GPT3Tokenizer;
    constructor(opts?: TextSplitterOptions);
    abstract splitText(text: string, opts?: TextSplitterOptions): string[];
    mergeText(texts: string[], separator?: string): string;
    mergeDocuments(docs: Document[]): string;
    splitDocuments(docs: Document[], opts?: TextSplitterOptions): Document[];
    createDocuments(texts: string[], metas?: Record<string, any>[], opts?: TextSplitterOptions): {
        content: string;
        meta: Record<string, any>;
    }[];
    protected createChunks(texts: string[], separator: string): string[];
    getLength: (text: string) => number;
    private lengthFn;
}
declare class CharacterTextSplitter extends TextSplitter {
    character: string;
    constructor(character?: string, opts?: TextSplitterOptions);
    splitText: (text: string, opts?: TextSplitterOptions) => string[];
}
declare class SentenceTextSplitter extends TextSplitter {
    splitText(text: string, opts?: TextSplitterOptions): string[];
}
declare class TokenSplitter extends TextSplitter {
    chunk: boolean;
    splitText(text: string, opts?: Omit<TextSplitterOptions, "chunk">): string[];
}

declare function injectVariables(template: string, variables: {
    [key: string]: any;
}): string;

interface TraceConfig {
    serverUrl: string;
    send: (trace: Trace) => Promise<void> | void;
}
/**
 * Set the trace config for your application
 *
 * This is useful for setting the server URL or the send function.
 *
 * @param newConfig
 */
declare const setTraceConfig: (newConfig: Partial<TraceConfig>) => void;
declare function sendTraceToServer(trace: Trace): Promise<void>;
type Trace = {
    name: string;
    inputs: any[];
    outputs: any | null;
    tags: string[];
    id: string;
    parentId: string | undefined;
    children: Trace[];
    error?: any;
    timestamp: number;
};
declare const trace: <T extends any[], R extends unknown>(name: string, fn: (this: any, ...args: T) => R | Promise<R>, tags?: string[]) => (...args: T) => Promise<R>;
declare function graphTraces(traces: Trace[], indent?: number): void;

interface Document {
    content: string;
    meta: Record<string, any>;
}

declare const utils: {
    unescapeStopTokens: (stop_tokens: string | string[]) => any;
    injectVariables: typeof injectVariables;
    parseJsonSSE: <T>({ data, onParse, onFinish, }: {
        data: ReadableStream<any>;
        onParse: (object: T) => void;
        onFinish: () => void;
    }) => Promise<void>;
};

export { BufferedChatMemory, CSVParser, CharacterTextSplitter, Document, Embeddings, FileLoader, JSONParser, LLMChain, ListParser, Loader, MemoryLLMChain, ModelProvider, OpenAI, Parser, Prompt, SentenceTextSplitter, TextSplitter, TokenSplitter, Trace, graphTraces, prompts, sendTraceToServer, setTraceConfig, trace, utils };