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base.ts
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import type { Tiktoken, TiktokenModel } from "js-tiktoken/lite";
import { z } from "zod";
import { type BaseCache, InMemoryCache } from "../caches/base.js";
import {
type BasePromptValueInterface,
StringPromptValue,
ChatPromptValue,
} from "../prompt_values.js";
import {
type BaseMessage,
type BaseMessageLike,
type MessageContent,
} from "../messages/base.js";
import { coerceMessageLikeToMessage } from "../messages/utils.js";
import { type LLMResult } from "../outputs.js";
import { CallbackManager, Callbacks } from "../callbacks/manager.js";
import { AsyncCaller, AsyncCallerParams } from "../utils/async_caller.js";
import { encodingForModel } from "../utils/tiktoken.js";
import { Runnable, type RunnableInterface } from "../runnables/base.js";
import { RunnableConfig } from "../runnables/config.js";
// https://www.npmjs.com/package/js-tiktoken
export const getModelNameForTiktoken = (modelName: string): TiktokenModel => {
if (modelName.startsWith("gpt-3.5-turbo-16k")) {
return "gpt-3.5-turbo-16k";
}
if (modelName.startsWith("gpt-3.5-turbo-")) {
return "gpt-3.5-turbo";
}
if (modelName.startsWith("gpt-4-32k")) {
return "gpt-4-32k";
}
if (modelName.startsWith("gpt-4-")) {
return "gpt-4";
}
if (modelName.startsWith("gpt-4o")) {
return "gpt-4o";
}
return modelName as TiktokenModel;
};
export const getEmbeddingContextSize = (modelName?: string): number => {
switch (modelName) {
case "text-embedding-ada-002":
return 8191;
default:
return 2046;
}
};
export const getModelContextSize = (modelName: string): number => {
switch (getModelNameForTiktoken(modelName)) {
case "gpt-3.5-turbo-16k":
return 16384;
case "gpt-3.5-turbo":
return 4096;
case "gpt-4-32k":
return 32768;
case "gpt-4":
return 8192;
case "text-davinci-003":
return 4097;
case "text-curie-001":
return 2048;
case "text-babbage-001":
return 2048;
case "text-ada-001":
return 2048;
case "code-davinci-002":
return 8000;
case "code-cushman-001":
return 2048;
default:
return 4097;
}
};
/**
* Whether or not the input matches the OpenAI tool definition.
* @param {unknown} tool The input to check.
* @returns {boolean} Whether the input is an OpenAI tool definition.
*/
export function isOpenAITool(tool: unknown): tool is ToolDefinition {
if (typeof tool !== "object" || !tool) return false;
if (
"type" in tool &&
tool.type === "function" &&
"function" in tool &&
typeof tool.function === "object" &&
tool.function &&
"name" in tool.function &&
"parameters" in tool.function
) {
return true;
}
return false;
}
interface CalculateMaxTokenProps {
prompt: string;
modelName: TiktokenModel;
}
export const calculateMaxTokens = async ({
prompt,
modelName,
}: CalculateMaxTokenProps) => {
let numTokens;
try {
numTokens = (
await encodingForModel(getModelNameForTiktoken(modelName))
).encode(prompt).length;
} catch (error) {
console.warn(
"Failed to calculate number of tokens, falling back to approximate count"
);
// fallback to approximate calculation if tiktoken is not available
// each token is ~4 characters: https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them#
numTokens = Math.ceil(prompt.length / 4);
}
const maxTokens = getModelContextSize(modelName);
return maxTokens - numTokens;
};
const getVerbosity = () => false;
export type SerializedLLM = {
_model: string;
_type: string;
// eslint-disable-next-line @typescript-eslint/no-explicit-any
} & Record<string, any>;
export interface BaseLangChainParams {
verbose?: boolean;
callbacks?: Callbacks;
tags?: string[];
metadata?: Record<string, unknown>;
}
/**
* Base class for language models, chains, tools.
*/
export abstract class BaseLangChain<
RunInput,
RunOutput,
CallOptions extends RunnableConfig = RunnableConfig
>
extends Runnable<RunInput, RunOutput, CallOptions>
implements BaseLangChainParams
{
/**
* Whether to print out response text.
*/
verbose: boolean;
callbacks?: Callbacks;
tags?: string[];
metadata?: Record<string, unknown>;
get lc_attributes(): { [key: string]: undefined } | undefined {
return {
callbacks: undefined,
verbose: undefined,
};
}
constructor(params: BaseLangChainParams) {
super(params);
this.verbose = params.verbose ?? getVerbosity();
this.callbacks = params.callbacks;
this.tags = params.tags ?? [];
this.metadata = params.metadata ?? {};
}
}
/**
* Base interface for language model parameters.
* A subclass of {@link BaseLanguageModel} should have a constructor that
* takes in a parameter that extends this interface.
*/
export interface BaseLanguageModelParams
extends AsyncCallerParams,
BaseLangChainParams {
/**
* @deprecated Use `callbacks` instead
*/
callbackManager?: CallbackManager;
cache?: BaseCache | boolean;
}
export interface BaseLanguageModelCallOptions extends RunnableConfig {
/**
* Stop tokens to use for this call.
* If not provided, the default stop tokens for the model will be used.
*/
stop?: string[];
}
export interface FunctionDefinition {
/**
* The name of the function to be called. Must be a-z, A-Z, 0-9, or contain
* underscores and dashes, with a maximum length of 64.
*/
name: string;
/**
* The parameters the functions accepts, described as a JSON Schema object. See the
* [guide](https://platform.openai.com/docs/guides/gpt/function-calling) for
* examples, and the
* [JSON Schema reference](https://json-schema.org/understanding-json-schema/) for
* documentation about the format.
*
* To describe a function that accepts no parameters, provide the value
* `{"type": "object", "properties": {}}`.
*/
parameters: Record<string, unknown>;
/**
* A description of what the function does, used by the model to choose when and
* how to call the function.
*/
description?: string;
/**
* Whether to enable strict schema adherence when generating the function call. If
* set to true, the model will follow the exact schema defined in the `parameters`
* field. Only a subset of JSON Schema is supported when `strict` is `true`. Learn
* more about Structured Outputs in the
* [function calling guide](https://platform.openai.com/docs/guides/function-calling).
*/
strict?: boolean;
}
export interface ToolDefinition {
type: "function";
function: FunctionDefinition;
}
export type FunctionCallOption = {
name: string;
};
export interface BaseFunctionCallOptions extends BaseLanguageModelCallOptions {
function_call?: FunctionCallOption;
functions?: FunctionDefinition[];
}
export type BaseLanguageModelInput =
| BasePromptValueInterface
| string
| BaseMessageLike[];
// eslint-disable-next-line @typescript-eslint/no-explicit-any
export type StructuredOutputType = z.infer<z.ZodObject<any, any, any, any>>;
export type StructuredOutputMethodOptions<IncludeRaw extends boolean = false> =
{
name?: string;
method?: "functionCalling" | "jsonMode" | "jsonSchema" | string;
includeRaw?: IncludeRaw;
};
/** @deprecated Use StructuredOutputMethodOptions instead */
export type StructuredOutputMethodParams<
RunOutput,
IncludeRaw extends boolean = false
> = {
/** @deprecated Pass schema in as the first argument */
// eslint-disable-next-line @typescript-eslint/no-explicit-any
schema: z.ZodType<RunOutput> | Record<string, any>;
name?: string;
method?: "functionCalling" | "jsonMode";
includeRaw?: IncludeRaw;
};
export interface BaseLanguageModelInterface<
// eslint-disable-next-line @typescript-eslint/no-explicit-any
RunOutput = any,
CallOptions extends BaseLanguageModelCallOptions = BaseLanguageModelCallOptions
> extends RunnableInterface<BaseLanguageModelInput, RunOutput, CallOptions> {
get callKeys(): string[];
generatePrompt(
promptValues: BasePromptValueInterface[],
options?: string[] | CallOptions,
callbacks?: Callbacks
): Promise<LLMResult>;
/**
* @deprecated Use .invoke() instead. Will be removed in 0.2.0.
*/
predict(
text: string,
options?: string[] | CallOptions,
callbacks?: Callbacks
): Promise<string>;
/**
* @deprecated Use .invoke() instead. Will be removed in 0.2.0.
*/
predictMessages(
messages: BaseMessage[],
options?: string[] | CallOptions,
callbacks?: Callbacks
): Promise<BaseMessage>;
_modelType(): string;
_llmType(): string;
getNumTokens(content: MessageContent): Promise<number>;
/**
* Get the identifying parameters of the LLM.
*/
// eslint-disable-next-line @typescript-eslint/no-explicit-any
_identifyingParams(): Record<string, any>;
serialize(): SerializedLLM;
}
export type LanguageModelOutput = BaseMessage | string;
export type LanguageModelLike = Runnable<
BaseLanguageModelInput,
LanguageModelOutput
>;
/**
* Base class for language models.
*/
export abstract class BaseLanguageModel<
// eslint-disable-next-line @typescript-eslint/no-explicit-any
RunOutput = any,
CallOptions extends BaseLanguageModelCallOptions = BaseLanguageModelCallOptions
>
extends BaseLangChain<BaseLanguageModelInput, RunOutput, CallOptions>
implements
BaseLanguageModelParams,
BaseLanguageModelInterface<RunOutput, CallOptions>
{
/**
* Keys that the language model accepts as call options.
*/
get callKeys(): string[] {
return ["stop", "timeout", "signal", "tags", "metadata", "callbacks"];
}
/**
* The async caller should be used by subclasses to make any async calls,
* which will thus benefit from the concurrency and retry logic.
*/
caller: AsyncCaller;
cache?: BaseCache;
constructor({
callbacks,
callbackManager,
...params
}: BaseLanguageModelParams) {
super({
callbacks: callbacks ?? callbackManager,
...params,
});
if (typeof params.cache === "object") {
this.cache = params.cache;
} else if (params.cache) {
this.cache = InMemoryCache.global();
} else {
this.cache = undefined;
}
this.caller = new AsyncCaller(params ?? {});
}
abstract generatePrompt(
promptValues: BasePromptValueInterface[],
options?: string[] | CallOptions,
callbacks?: Callbacks
): Promise<LLMResult>;
/**
* @deprecated Use .invoke() instead. Will be removed in 0.2.0.
*/
abstract predict(
text: string,
options?: string[] | CallOptions,
callbacks?: Callbacks
): Promise<string>;
/**
* @deprecated Use .invoke() instead. Will be removed in 0.2.0.
*/
abstract predictMessages(
messages: BaseMessage[],
options?: string[] | CallOptions,
callbacks?: Callbacks
): Promise<BaseMessage>;
abstract _modelType(): string;
abstract _llmType(): string;
private _encoding?: Tiktoken;
async getNumTokens(content: MessageContent) {
// TODO: Figure out correct value.
if (typeof content !== "string") {
return 0;
}
// fallback to approximate calculation if tiktoken is not available
let numTokens = Math.ceil(content.length / 4);
if (!this._encoding) {
try {
this._encoding = await encodingForModel(
"modelName" in this
? getModelNameForTiktoken(this.modelName as string)
: "gpt2"
);
} catch (error) {
console.warn(
"Failed to calculate number of tokens, falling back to approximate count",
error
);
}
}
if (this._encoding) {
try {
numTokens = this._encoding.encode(content).length;
} catch (error) {
console.warn(
"Failed to calculate number of tokens, falling back to approximate count",
error
);
}
}
return numTokens;
}
protected static _convertInputToPromptValue(
input: BaseLanguageModelInput
): BasePromptValueInterface {
if (typeof input === "string") {
return new StringPromptValue(input);
} else if (Array.isArray(input)) {
return new ChatPromptValue(input.map(coerceMessageLikeToMessage));
} else {
return input;
}
}
/**
* Get the identifying parameters of the LLM.
*/
// eslint-disable-next-line @typescript-eslint/no-explicit-any
_identifyingParams(): Record<string, any> {
return {};
}
/**
* Create a unique cache key for a specific call to a specific language model.
* @param callOptions Call options for the model
* @returns A unique cache key.
*/
_getSerializedCacheKeyParametersForCall(
// TODO: Fix when we remove the RunnableLambda backwards compatibility shim.
{ config, ...callOptions }: CallOptions & { config?: RunnableConfig }
): string {
// eslint-disable-next-line @typescript-eslint/no-explicit-any
const params: Record<string, any> = {
...this._identifyingParams(),
...callOptions,
_type: this._llmType(),
_model: this._modelType(),
};
const filteredEntries = Object.entries(params).filter(
([_, value]) => value !== undefined
);
const serializedEntries = filteredEntries
.map(([key, value]) => `${key}:${JSON.stringify(value)}`)
.sort()
.join(",");
return serializedEntries;
}
/**
* @deprecated
* Return a json-like object representing this LLM.
*/
serialize(): SerializedLLM {
return {
...this._identifyingParams(),
_type: this._llmType(),
_model: this._modelType(),
};
}
/**
* @deprecated
* Load an LLM from a json-like object describing it.
*/
static async deserialize(_data: SerializedLLM): Promise<BaseLanguageModel> {
throw new Error("Use .toJSON() instead");
}
withStructuredOutput?<
// eslint-disable-next-line @typescript-eslint/no-explicit-any
RunOutput extends Record<string, any> = Record<string, any>
// eslint-disable-next-line @typescript-eslint/no-explicit-any
>(
schema:
| z.ZodType<RunOutput>
// eslint-disable-next-line @typescript-eslint/no-explicit-any
| Record<string, any>,
config?: StructuredOutputMethodOptions<false>
): Runnable<BaseLanguageModelInput, RunOutput>;
withStructuredOutput?<
// eslint-disable-next-line @typescript-eslint/no-explicit-any
RunOutput extends Record<string, any> = Record<string, any>
>(
schema:
| z.ZodType<RunOutput>
// eslint-disable-next-line @typescript-eslint/no-explicit-any
| Record<string, any>,
config?: StructuredOutputMethodOptions<true>
): Runnable<BaseLanguageModelInput, { raw: BaseMessage; parsed: RunOutput }>;
/**
* Model wrapper that returns outputs formatted to match the given schema.
*
* @template {BaseLanguageModelInput} RunInput The input type for the Runnable, expected to be the same input for the LLM.
* @template {Record<string, any>} RunOutput The output type for the Runnable, expected to be a Zod schema object for structured output validation.
*
* @param {z.ZodEffects<RunOutput>} schema The schema for the structured output. Either as a Zod schema or a valid JSON schema object.
* If a Zod schema is passed, the returned attributes will be validated, whereas with JSON schema they will not be.
* @param {string} name The name of the function to call.
* @param {"functionCalling" | "jsonMode"} [method=functionCalling] The method to use for getting the structured output. Defaults to "functionCalling".
* @param {boolean | undefined} [includeRaw=false] Whether to include the raw output in the result. Defaults to false.
* @returns {Runnable<RunInput, RunOutput> | Runnable<RunInput, { raw: BaseMessage; parsed: RunOutput }>} A new runnable that calls the LLM with structured output.
*/
withStructuredOutput?<
// eslint-disable-next-line @typescript-eslint/no-explicit-any
RunOutput extends Record<string, any> = Record<string, any>
>(
schema:
| z.ZodType<RunOutput>
// eslint-disable-next-line @typescript-eslint/no-explicit-any
| Record<string, any>,
config?: StructuredOutputMethodOptions<boolean>
):
| Runnable<BaseLanguageModelInput, RunOutput>
| Runnable<
BaseLanguageModelInput,
{
raw: BaseMessage;
parsed: RunOutput;
}
>;
}
/**
* Shared interface for token usage
* return type from LLM calls.
*/
export interface TokenUsage {
completionTokens?: number;
promptTokens?: number;
totalTokens?: number;
}