@langchain/core/dist/language_models/llms.d.cts.map

Core LangChain.js abstractions and schemas

{"version":3,"file":"llms.d.cts","names":["BasePromptValueInterface","LLMResult","Generation","GenerationChunk","BaseCallbackConfig","CallbackManagerForLLMRun","Callbacks","BaseLanguageModel","BaseLanguageModelCallOptions","BaseLanguageModelInput","BaseLanguageModelParams","RunnableConfig","BaseCache","SerializedLLM","Record","BaseLLMParams","BaseLLMCallOptions","BaseLLM","CallOptions","Exclude","Omit","Promise","AsyncGenerator","Partial","prompts","cache","llmStringKey","parsedOptions","handledOptions","runId","LLM"],"sources":["../../src/language_models/llms.d.ts"],"sourcesContent":["import type { BasePromptValueInterface } from \"../prompt_values.js\";\nimport { type LLMResult, type Generation, GenerationChunk } from \"../outputs.js\";\nimport { type BaseCallbackConfig, type CallbackManagerForLLMRun, type Callbacks } from \"../callbacks/manager.js\";\nimport { BaseLanguageModel, type BaseLanguageModelCallOptions, type BaseLanguageModelInput, type BaseLanguageModelParams } from \"./base.js\";\nimport type { RunnableConfig } from \"../runnables/config.js\";\nimport type { BaseCache } from \"../caches/index.js\";\nexport type SerializedLLM = {\n    _model: string;\n    _type: string;\n} & Record<string, any>;\nexport interface BaseLLMParams extends BaseLanguageModelParams {\n}\nexport interface BaseLLMCallOptions extends BaseLanguageModelCallOptions {\n}\n/**\n * LLM Wrapper. Takes in a prompt (or prompts) and returns a string.\n */\nexport declare abstract class BaseLLM<CallOptions extends BaseLLMCallOptions = BaseLLMCallOptions> extends BaseLanguageModel<string, CallOptions> {\n    ParsedCallOptions: Omit<CallOptions, Exclude<keyof RunnableConfig, \"signal\" | \"timeout\" | \"maxConcurrency\">>;\n    lc_namespace: string[];\n    /**\n     * This method takes an input and options, and returns a string. It\n     * converts the input to a prompt value and generates a result based on\n     * the prompt.\n     * @param input Input for the LLM.\n     * @param options Options for the LLM call.\n     * @returns A string result based on the prompt.\n     */\n    invoke(input: BaseLanguageModelInput, options?: CallOptions): Promise<string>;\n    _streamResponseChunks(_input: string, _options: this[\"ParsedCallOptions\"], _runManager?: CallbackManagerForLLMRun): AsyncGenerator<GenerationChunk>;\n    protected _separateRunnableConfigFromCallOptionsCompat(options?: Partial<CallOptions>): [RunnableConfig, this[\"ParsedCallOptions\"]];\n    _streamIterator(input: BaseLanguageModelInput, options?: CallOptions): AsyncGenerator<string>;\n    /**\n     * This method takes prompt values, options, and callbacks, and generates\n     * a result based on the prompts.\n     * @param promptValues Prompt values for the LLM.\n     * @param options Options for the LLM call.\n     * @param callbacks Callbacks for the LLM call.\n     * @returns An LLMResult based on the prompts.\n     */\n    generatePrompt(promptValues: BasePromptValueInterface[], options?: string[] | CallOptions, callbacks?: Callbacks): Promise<LLMResult>;\n    /**\n     * Run the LLM on the given prompts and input.\n     */\n    abstract _generate(prompts: string[], options: this[\"ParsedCallOptions\"], runManager?: CallbackManagerForLLMRun): Promise<LLMResult>;\n    /**\n     * Get the parameters used to invoke the model\n     */\n    invocationParams(_options?: this[\"ParsedCallOptions\"]): any;\n    _flattenLLMResult(llmResult: LLMResult): LLMResult[];\n    /** @ignore */\n    _generateUncached(prompts: string[], parsedOptions: this[\"ParsedCallOptions\"], handledOptions: BaseCallbackConfig, startedRunManagers?: CallbackManagerForLLMRun[]): Promise<LLMResult>;\n    _generateCached({ prompts, cache, llmStringKey, parsedOptions, handledOptions, runId }: {\n        prompts: string[];\n        cache: BaseCache<Generation[]>;\n        llmStringKey: string;\n        parsedOptions: any;\n        handledOptions: RunnableConfig;\n        runId?: string;\n    }): Promise<LLMResult & {\n        missingPromptIndices: number[];\n        startedRunManagers?: CallbackManagerForLLMRun[];\n    }>;\n    /**\n     * Run the LLM on the given prompts and input, handling caching.\n     */\n    generate(prompts: string[], options?: string[] | CallOptions, callbacks?: Callbacks): Promise<LLMResult>;\n    /**\n     * Get the identifying parameters of the LLM.\n     */\n    _identifyingParams(): Record<string, any>;\n    /**\n     * Return the string type key uniquely identifying this class of LLM.\n     */\n    abstract _llmType(): string;\n    _modelType(): string;\n}\n/**\n * LLM class that provides a simpler interface to subclass than {@link BaseLLM}.\n *\n * Requires only implementing a simpler {@link _call} method instead of {@link _generate}.\n *\n * @augments BaseLLM\n */\nexport declare abstract class LLM<CallOptions extends BaseLLMCallOptions = BaseLLMCallOptions> extends BaseLLM<CallOptions> {\n    /**\n     * Run the LLM on the given prompt and input.\n     */\n    abstract _call(prompt: string, options: this[\"ParsedCallOptions\"], runManager?: CallbackManagerForLLMRun): Promise<string>;\n    _generate(prompts: string[], options: this[\"ParsedCallOptions\"], runManager?: CallbackManagerForLLMRun): Promise<LLMResult>;\n}\n//# 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