@langchain/core
Version:
Core LangChain.js abstractions and schemas
285 lines (283 loc) • 9.23 kB
JavaScript
const require_rolldown_runtime = require('../_virtual/rolldown_runtime.cjs');
const require_utils = require('../messages/utils.cjs');
const require_utils_async_caller = require('../utils/async_caller.cjs');
const require_base = require('../runnables/base.cjs');
const require_prompt_values = require('../prompt_values.cjs');
const require_caches_index = require('../caches/index.cjs');
const require_utils_tiktoken = require('../utils/tiktoken.cjs');
//#region src/language_models/base.ts
var base_exports = {};
require_rolldown_runtime.__export(base_exports, {
BaseLangChain: () => BaseLangChain,
BaseLanguageModel: () => BaseLanguageModel,
calculateMaxTokens: () => calculateMaxTokens,
getEmbeddingContextSize: () => getEmbeddingContextSize,
getModelContextSize: () => getModelContextSize,
getModelNameForTiktoken: () => getModelNameForTiktoken,
isOpenAITool: () => isOpenAITool
});
const getModelNameForTiktoken = (modelName) => {
if (modelName.startsWith("gpt-5")) return "gpt-5";
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;
};
const getEmbeddingContextSize = (modelName) => {
switch (modelName) {
case "text-embedding-ada-002": return 8191;
default: return 2046;
}
};
/**
* Get the context window size (max input tokens) for a given model.
*
* Context window sizes are sourced from official model documentation:
* - OpenAI: https://platform.openai.com/docs/models
* - Anthropic: https://docs.anthropic.com/claude/docs/models-overview
* - Google: https://ai.google.dev/gemini/docs/models/gemini
*
* @param modelName - The name of the model
* @returns The context window size in tokens
*/
const getModelContextSize = (modelName) => {
const normalizedName = getModelNameForTiktoken(modelName);
switch (normalizedName) {
case "gpt-5":
case "gpt-5-turbo":
case "gpt-5-turbo-preview": return 4e5;
case "gpt-4o":
case "gpt-4o-mini":
case "gpt-4o-2024-05-13":
case "gpt-4o-2024-08-06": return 128e3;
case "gpt-4-turbo":
case "gpt-4-turbo-preview":
case "gpt-4-turbo-2024-04-09":
case "gpt-4-0125-preview":
case "gpt-4-1106-preview": return 128e3;
case "gpt-4-32k":
case "gpt-4-32k-0314":
case "gpt-4-32k-0613": return 32768;
case "gpt-4":
case "gpt-4-0314":
case "gpt-4-0613": return 8192;
case "gpt-3.5-turbo-16k":
case "gpt-3.5-turbo-16k-0613": return 16384;
case "gpt-3.5-turbo":
case "gpt-3.5-turbo-0301":
case "gpt-3.5-turbo-0613":
case "gpt-3.5-turbo-1106":
case "gpt-3.5-turbo-0125": return 4096;
case "text-davinci-003":
case "text-davinci-002": return 4097;
case "text-davinci-001": return 2049;
case "text-curie-001":
case "text-babbage-001":
case "text-ada-001": return 2048;
case "code-davinci-002":
case "code-davinci-001": return 8e3;
case "code-cushman-001": return 2048;
case "claude-3-5-sonnet-20241022":
case "claude-3-5-sonnet-20240620":
case "claude-3-opus-20240229":
case "claude-3-sonnet-20240229":
case "claude-3-haiku-20240307":
case "claude-2.1": return 2e5;
case "claude-2.0":
case "claude-instant-1.2": return 1e5;
case "gemini-1.5-pro":
case "gemini-1.5-pro-latest":
case "gemini-1.5-flash":
case "gemini-1.5-flash-latest": return 1e6;
case "gemini-pro":
case "gemini-pro-vision": return 32768;
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.
*/
function isOpenAITool(tool) {
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;
}
const calculateMaxTokens = async ({ prompt, modelName }) => {
let numTokens;
try {
numTokens = (await require_utils_tiktoken.encodingForModel(getModelNameForTiktoken(modelName))).encode(prompt).length;
} catch {
console.warn("Failed to calculate number of tokens, falling back to approximate count");
numTokens = Math.ceil(prompt.length / 4);
}
const maxTokens = getModelContextSize(modelName);
return maxTokens - numTokens;
};
const getVerbosity = () => false;
/**
* Base class for language models, chains, tools.
*/
var BaseLangChain = class extends require_base.Runnable {
/**
* Whether to print out response text.
*/
verbose;
callbacks;
tags;
metadata;
get lc_attributes() {
return {
callbacks: void 0,
verbose: void 0
};
}
constructor(params) {
super(params);
this.verbose = params.verbose ?? getVerbosity();
this.callbacks = params.callbacks;
this.tags = params.tags ?? [];
this.metadata = params.metadata ?? {};
}
};
/**
* Base class for language models.
*/
var BaseLanguageModel = class extends BaseLangChain {
/**
* Keys that the language model accepts as call options.
*/
get callKeys() {
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;
cache;
constructor({ callbacks, callbackManager,...params }) {
const { cache,...rest } = params;
super({
callbacks: callbacks ?? callbackManager,
...rest
});
if (typeof cache === "object") this.cache = cache;
else if (cache) this.cache = require_caches_index.InMemoryCache.global();
else this.cache = void 0;
this.caller = new require_utils_async_caller.AsyncCaller(params ?? {});
}
_encoding;
/**
* Get the number of tokens in the content.
* @param content The content to get the number of tokens for.
* @returns The number of tokens in the content.
*/
async getNumTokens(content) {
let textContent;
if (typeof content === "string") textContent = content;
else
/**
* Content is an array of ContentBlock
*
* ToDo(@christian-bromann): This is a temporary fix to get the number of tokens for the content.
* We need to find a better way to do this.
* @see https://github.com/langchain-ai/langchainjs/pull/8341#pullrequestreview-2933713116
*/
textContent = content.map((item) => {
if (typeof item === "string") return item;
if (item.type === "text" && "text" in item) return item.text;
return "";
}).join("");
let numTokens = Math.ceil(textContent.length / 4);
if (!this._encoding) try {
this._encoding = await require_utils_tiktoken.encodingForModel("modelName" in this ? getModelNameForTiktoken(this.modelName) : "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(textContent).length;
} catch (error) {
console.warn("Failed to calculate number of tokens, falling back to approximate count", error);
}
return numTokens;
}
static _convertInputToPromptValue(input) {
if (typeof input === "string") return new require_prompt_values.StringPromptValue(input);
else if (Array.isArray(input)) return new require_prompt_values.ChatPromptValue(input.map(require_utils.coerceMessageLikeToMessage));
else return input;
}
/**
* Get the identifying parameters of the LLM.
*/
_identifyingParams() {
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({ config,...callOptions }) {
const params = {
...this._identifyingParams(),
...callOptions,
_type: this._llmType(),
_model: this._modelType()
};
const filteredEntries = Object.entries(params).filter(([_, value]) => value !== void 0);
const serializedEntries = filteredEntries.map(([key, value]) => `${key}:${JSON.stringify(value)}`).sort().join(",");
return serializedEntries;
}
/**
* @deprecated
* Return a json-like object representing this LLM.
*/
serialize() {
return {
...this._identifyingParams(),
_type: this._llmType(),
_model: this._modelType()
};
}
/**
* @deprecated
* Load an LLM from a json-like object describing it.
*/
static async deserialize(_data) {
throw new Error("Use .toJSON() instead");
}
/**
* Return profiling information for the model.
*
* @returns {ModelProfile} An object describing the model's capabilities and constraints
*/
get profile() {
return {};
}
};
//#endregion
exports.BaseLangChain = BaseLangChain;
exports.BaseLanguageModel = BaseLanguageModel;
Object.defineProperty(exports, 'base_exports', {
enumerable: true,
get: function () {
return base_exports;
}
});
exports.calculateMaxTokens = calculateMaxTokens;
exports.getEmbeddingContextSize = getEmbeddingContextSize;
exports.getModelContextSize = getModelContextSize;
exports.getModelNameForTiktoken = getModelNameForTiktoken;
exports.isOpenAITool = isOpenAITool;
//# sourceMappingURL=base.cjs.map