langchain
Version:
Typescript bindings for langchain
710 lines (709 loc) • 29.4 kB
JavaScript
;
var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {
if (k2 === undefined) k2 = k;
var desc = Object.getOwnPropertyDescriptor(m, k);
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desc = { enumerable: true, get: function() { return m[k]; } };
}
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if (k2 === undefined) k2 = k;
o[k2] = m[k];
}));
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Object.defineProperty(o, "default", { enumerable: true, value: v });
}) : function(o, v) {
o["default"] = v;
});
var __importStar = (this && this.__importStar) || (function () {
var ownKeys = function(o) {
ownKeys = Object.getOwnPropertyNames || function (o) {
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return result;
};
})();
Object.defineProperty(exports, "__esModule", { value: true });
exports.ConfigurableModel = void 0;
exports._inferModelProvider = _inferModelProvider;
exports.initChatModel = initChatModel;
const chat_models_1 = require("@langchain/core/language_models/chat_models");
const runnables_1 = require("@langchain/core/runnables");
const stream_1 = require("@langchain/core/utils/stream");
const _SUPPORTED_PROVIDERS = [
"openai",
"anthropic",
"azure_openai",
"cohere",
"google-vertexai",
"google-vertexai-web",
"google-genai",
"ollama",
"together",
"fireworks",
"mistralai",
"groq",
"bedrock",
"cerebras",
"deepseek",
"xai",
"perplexity",
];
async function _initChatModelHelper(model, modelProvider,
// eslint-disable-next-line @typescript-eslint/no-explicit-any
params = {}) {
const modelProviderCopy = modelProvider || _inferModelProvider(model);
if (!modelProviderCopy) {
throw new Error(`Unable to infer model provider for { model: ${model} }, please specify modelProvider directly.`);
}
// eslint-disable-next-line @typescript-eslint/no-unused-vars
const { modelProvider: _unused, ...passedParams } = params;
try {
switch (modelProviderCopy) {
case "openai": {
const { ChatOpenAI } = await Promise.resolve().then(() => __importStar(require("@langchain/openai")));
return new ChatOpenAI({ model, ...passedParams });
}
case "anthropic": {
const { ChatAnthropic } = await Promise.resolve().then(() => __importStar(require("@langchain/anthropic")));
return new ChatAnthropic({ model, ...passedParams });
}
case "azure_openai": {
const { AzureChatOpenAI } = await Promise.resolve().then(() => __importStar(require("@langchain/openai")));
return new AzureChatOpenAI({ model, ...passedParams });
}
case "cohere": {
const { ChatCohere } = await Promise.resolve().then(() => __importStar(require("@langchain/cohere")));
return new ChatCohere({ model, ...passedParams });
}
case "google-vertexai": {
const { ChatVertexAI } = await Promise.resolve().then(() => __importStar(require("@langchain/google-vertexai")));
return new ChatVertexAI({ model, ...passedParams });
}
case "google-vertexai-web": {
const { ChatVertexAI } = await Promise.resolve().then(() => __importStar(require("@langchain/google-vertexai-web")));
return new ChatVertexAI({ model, ...passedParams });
}
case "google-genai": {
const { ChatGoogleGenerativeAI } = await Promise.resolve().then(() => __importStar(require("@langchain/google-genai")));
return new ChatGoogleGenerativeAI({ model, ...passedParams });
}
case "ollama": {
const { ChatOllama } = await Promise.resolve().then(() => __importStar(require("@langchain/ollama")));
return new ChatOllama({ model, ...passedParams });
}
case "mistralai": {
const { ChatMistralAI } = await Promise.resolve().then(() => __importStar(require("@langchain/mistralai")));
return new ChatMistralAI({ model, ...passedParams });
}
case "groq": {
const { ChatGroq } = await Promise.resolve().then(() => __importStar(require("@langchain/groq")));
return new ChatGroq({ model, ...passedParams });
}
case "cerebras": {
const { ChatCerebras } = await Promise.resolve().then(() => __importStar(require("@langchain/cerebras")));
return new ChatCerebras({ model, ...passedParams });
}
case "bedrock": {
const { ChatBedrockConverse } = await Promise.resolve().then(() => __importStar(require("@langchain/aws")));
return new ChatBedrockConverse({ model, ...passedParams });
}
case "deepseek": {
const { ChatDeepSeek } = await Promise.resolve().then(() => __importStar(require("@langchain/deepseek")));
return new ChatDeepSeek({ model, ...passedParams });
}
case "xai": {
const { ChatXAI } = await Promise.resolve().then(() => __importStar(require("@langchain/xai")));
return new ChatXAI({ model, ...passedParams });
}
case "fireworks": {
// eslint-disable-next-line @typescript-eslint/ban-ts-comment
// @ts-ignore - Can not install as a proper dependency due to circular dependency
const { ChatFireworks } = await Promise.resolve().then(() => __importStar(require(
// We can not 'expect-error' because if you explicitly build `@langchain/community`
// this import will be able to be resolved, thus there will be no error. However
// this will never be the case in CI.
// eslint-disable-next-line @typescript-eslint/ban-ts-comment
// @ts-ignore - Can not install as a proper dependency due to circular dependency
"@langchain/community/chat_models/fireworks")));
return new ChatFireworks({ model, ...passedParams });
}
case "together": {
// eslint-disable-next-line @typescript-eslint/ban-ts-comment
// @ts-ignore - Can not install as a proper dependency due to circular dependency
const { ChatTogetherAI } = await Promise.resolve().then(() => __importStar(require(
// We can not 'expect-error' because if you explicitly build `@langchain/community`
// this import will be able to be resolved, thus there will be no error. However
// this will never be the case in CI.
// eslint-disable-next-line @typescript-eslint/ban-ts-comment
// @ts-ignore - Can not install as a proper dependency due to circular dependency
"@langchain/community/chat_models/togetherai")));
return new ChatTogetherAI({ model, ...passedParams });
}
case "perplexity": {
// eslint-disable-next-line @typescript-eslint/ban-ts-comment
// @ts-ignore - Can not install as a proper dependency due to circular dependency
const { ChatPerplexity } = await Promise.resolve().then(() => __importStar(require(
// We can not 'expect-error' because if you explicitly build `@langchain/community`
// this import will be able to be resolved, thus there will be no error. However
// this will never be the case in CI.
// eslint-disable-next-line @typescript-eslint/ban-ts-comment
// @ts-ignore - Can not install as a proper dependency due to circular dependency
"@langchain/community/chat_models/perplexity")));
return new ChatPerplexity({ model, ...passedParams });
}
default: {
const supported = _SUPPORTED_PROVIDERS.join(", ");
throw new Error(`Unsupported { modelProvider: ${modelProviderCopy} }.\n\nSupported model providers are: ${supported}`);
}
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
}
catch (e) {
if ("code" in e && e.code.includes("ERR_MODULE_NOT_FOUND")) {
const attemptedPackage = new Error(e).message
.split("Error: Cannot find package '")[1]
.split("'")[0];
throw new Error(`Unable to import ${attemptedPackage}. Please install with ` +
`\`npm install ${attemptedPackage}\` or \`yarn add ${attemptedPackage}\``);
}
throw e;
}
}
/**
* Attempts to infer the model provider based on the given model name.
*
* @param {string} modelName - The name of the model to infer the provider for.
* @returns {string | undefined} The inferred model provider name, or undefined if unable to infer.
*
* @example
* _inferModelProvider("gpt-4"); // returns "openai"
* _inferModelProvider("claude-2"); // returns "anthropic"
* _inferModelProvider("unknown-model"); // returns undefined
*/
function _inferModelProvider(modelName) {
if (modelName.startsWith("gpt-3") ||
modelName.startsWith("gpt-4") ||
modelName.startsWith("o1") ||
modelName.startsWith("o3") ||
modelName.startsWith("o4")) {
return "openai";
}
else if (modelName.startsWith("claude")) {
return "anthropic";
}
else if (modelName.startsWith("command")) {
return "cohere";
}
else if (modelName.startsWith("accounts/fireworks")) {
return "fireworks";
}
else if (modelName.startsWith("gemini")) {
return "google-vertexai";
}
else if (modelName.startsWith("amazon.")) {
return "bedrock";
}
else if (modelName.startsWith("mistral")) {
return "mistralai";
}
else if (modelName.startsWith("sonar") || modelName.startsWith("pplx")) {
return "perplexity";
}
else {
return undefined;
}
}
/**
* Internal class used to create chat models.
*
* @internal
*/
class ConfigurableModel extends chat_models_1.BaseChatModel {
_llmType() {
return "chat_model";
}
constructor(fields) {
super(fields);
Object.defineProperty(this, "lc_namespace", {
enumerable: true,
configurable: true,
writable: true,
value: ["langchain", "chat_models"]
});
// eslint-disable-next-line @typescript-eslint/no-explicit-any
Object.defineProperty(this, "_defaultConfig", {
enumerable: true,
configurable: true,
writable: true,
value: {}
});
/**
* @default "any"
*/
Object.defineProperty(this, "_configurableFields", {
enumerable: true,
configurable: true,
writable: true,
value: "any"
});
/**
* @default ""
*/
Object.defineProperty(this, "_configPrefix", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
/**
* Methods which should be called after the model is initialized.
* The key will be the method name, and the value will be the arguments.
*/
// eslint-disable-next-line @typescript-eslint/no-explicit-any
Object.defineProperty(this, "_queuedMethodOperations", {
enumerable: true,
configurable: true,
writable: true,
value: {}
});
// Extract the input types from the `BaseModel` class.
Object.defineProperty(this, "withStructuredOutput", {
enumerable: true,
configurable: true,
writable: true,
value: (schema, ...args) => {
const newQueuedOperations = { ...this._queuedMethodOperations };
newQueuedOperations.withStructuredOutput = [schema, ...args];
return new ConfigurableModel({
defaultConfig: this._defaultConfig,
configurableFields: this._configurableFields,
configPrefix: this._configPrefix,
queuedMethodOperations: newQueuedOperations,
});
}
});
this._defaultConfig = fields.defaultConfig ?? {};
if (fields.configurableFields === "any") {
this._configurableFields = "any";
}
else {
this._configurableFields = fields.configurableFields ?? [
"model",
"modelProvider",
];
}
if (fields.configPrefix) {
this._configPrefix = fields.configPrefix.endsWith("_")
? fields.configPrefix
: `${fields.configPrefix}_`;
}
else {
this._configPrefix = "";
}
this._queuedMethodOperations =
fields.queuedMethodOperations ?? this._queuedMethodOperations;
}
async _model(config) {
const params = { ...this._defaultConfig, ...this._modelParams(config) };
let initializedModel = await _initChatModelHelper(params.model, params.modelProvider, params);
// Apply queued method operations
const queuedMethodOperationsEntries = Object.entries(this._queuedMethodOperations);
if (queuedMethodOperationsEntries.length > 0) {
for (const [method, args] of queuedMethodOperationsEntries) {
if (method in initializedModel &&
// eslint-disable-next-line @typescript-eslint/no-explicit-any
typeof initializedModel[method] === "function") {
// eslint-disable-next-line @typescript-eslint/no-explicit-any
initializedModel = await initializedModel[method](...args);
}
}
}
return initializedModel;
}
async _generate(messages, options, runManager) {
const model = await this._model(options);
return model._generate(messages, options ?? {}, runManager);
}
bindTools(tools,
// eslint-disable-next-line @typescript-eslint/no-explicit-any
params) {
const newQueuedOperations = { ...this._queuedMethodOperations };
newQueuedOperations.bindTools = [tools, params];
return new ConfigurableModel({
defaultConfig: this._defaultConfig,
configurableFields: this._configurableFields,
configPrefix: this._configPrefix,
queuedMethodOperations: newQueuedOperations,
});
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
_modelParams(config) {
const configurable = config?.configurable ?? {};
// eslint-disable-next-line @typescript-eslint/no-explicit-any
let modelParams = {};
for (const [key, value] of Object.entries(configurable)) {
if (key.startsWith(this._configPrefix)) {
const strippedKey = this._removePrefix(key, this._configPrefix);
modelParams[strippedKey] = value;
}
}
if (this._configurableFields !== "any") {
modelParams = Object.fromEntries(Object.entries(modelParams).filter(([key]) => this._configurableFields.includes(key)));
}
return modelParams;
}
_removePrefix(str, prefix) {
return str.startsWith(prefix) ? str.slice(prefix.length) : str;
}
/**
* Bind config to a Runnable, returning a new Runnable.
* @param {RunnableConfig | undefined} [config] - The config to bind.
* @returns {RunnableBinding<RunInput, RunOutput, CallOptions>} A new RunnableBinding with the bound config.
*/
withConfig(config) {
const mergedConfig = { ...(config || {}) };
const modelParams = this._modelParams(mergedConfig);
const remainingConfig = Object.fromEntries(Object.entries(mergedConfig).filter(([k]) => k !== "configurable"));
remainingConfig.configurable = Object.fromEntries(Object.entries(mergedConfig.configurable || {}).filter(([k]) => this._configPrefix &&
!Object.keys(modelParams).includes(this._removePrefix(k, this._configPrefix))));
const newConfigurableModel = new ConfigurableModel({
defaultConfig: { ...this._defaultConfig, ...modelParams },
configurableFields: Array.isArray(this._configurableFields)
? [...this._configurableFields]
: this._configurableFields,
configPrefix: this._configPrefix,
queuedMethodOperations: this._queuedMethodOperations,
});
return new runnables_1.RunnableBinding({
config: mergedConfig,
bound: newConfigurableModel,
});
}
async invoke(input, options) {
const model = await this._model(options);
const config = (0, runnables_1.ensureConfig)(options);
return model.invoke(input, config);
}
async stream(input, options) {
const model = await this._model(options);
const wrappedGenerator = new stream_1.AsyncGeneratorWithSetup({
generator: await model.stream(input, options),
config: options,
});
await wrappedGenerator.setup;
return stream_1.IterableReadableStream.fromAsyncGenerator(wrappedGenerator);
}
async batch(inputs, options, batchOptions) {
// We can super this since the base runnable implementation of
// `.batch` will call `.invoke` on each input.
return super.batch(inputs, options, batchOptions);
}
async *transform(generator, options) {
const model = await this._model(options);
const config = (0, runnables_1.ensureConfig)(options);
yield* model.transform(generator, config);
}
async *streamLog(input, options, streamOptions) {
const model = await this._model(options);
const config = (0, runnables_1.ensureConfig)(options);
yield* model.streamLog(input, config, {
...streamOptions,
_schemaFormat: "original",
includeNames: streamOptions?.includeNames,
includeTypes: streamOptions?.includeTypes,
includeTags: streamOptions?.includeTags,
excludeNames: streamOptions?.excludeNames,
excludeTypes: streamOptions?.excludeTypes,
excludeTags: streamOptions?.excludeTags,
});
}
streamEvents(input, options, streamOptions) {
// eslint-disable-next-line @typescript-eslint/no-this-alias
const outerThis = this;
async function* wrappedGenerator() {
const model = await outerThis._model(options);
const config = (0, runnables_1.ensureConfig)(options);
const eventStream = model.streamEvents(input, config, streamOptions);
for await (const chunk of eventStream) {
yield chunk;
}
}
return stream_1.IterableReadableStream.fromAsyncGenerator(wrappedGenerator());
}
}
exports.ConfigurableModel = ConfigurableModel;
// ################################# FOR CONTRIBUTORS #################################
//
// If adding support for a new provider, please append the provider
// name to the supported list in the docstring below.
//
// ####################################################################################
/**
* Initialize a ChatModel from the model name and provider.
* Must have the integration package corresponding to the model provider installed.
*
* @template {extends BaseLanguageModelInput = BaseLanguageModelInput} RunInput - The input type for the model.
* @template {extends ConfigurableChatModelCallOptions = ConfigurableChatModelCallOptions} CallOptions - Call options for the model.
*
* @param {string | ChatModelProvider} [model] - The name of the model, e.g. "gpt-4", "claude-3-opus-20240229".
* Can be prefixed with the model provider, e.g. "openai:gpt-4", "anthropic:claude-3-opus-20240229".
* @param {Object} [fields] - Additional configuration options.
* @param {string} [fields.modelProvider] - The model provider. Supported values include:
* - openai (@langchain/openai)
* - anthropic (@langchain/anthropic)
* - azure_openai (@langchain/openai)
* - google-vertexai (@langchain/google-vertexai)
* - google-vertexai-web (@langchain/google-vertexai-web)
* - google-genai (@langchain/google-genai)
* - bedrock (@langchain/aws)
* - cohere (@langchain/cohere)
* - fireworks (@langchain/community/chat_models/fireworks)
* - together (@langchain/community/chat_models/togetherai)
* - mistralai (@langchain/mistralai)
* - groq (@langchain/groq)
* - ollama (@langchain/ollama)
* - perplexity (@langchain/community/chat_models/perplexity)
* - cerebras (@langchain/cerebras)
* - deepseek (@langchain/deepseek)
* - xai (@langchain/xai)
* @param {string[] | "any"} [fields.configurableFields] - Which model parameters are configurable:
* - undefined: No configurable fields.
* - "any": All fields are configurable. (See Security Note in description)
* - string[]: Specified fields are configurable.
* @param {string} [fields.configPrefix] - Prefix for configurable fields at runtime.
* @param {Record<string, any>} [fields.params] - Additional keyword args to pass to the ChatModel constructor.
* @returns {Promise<ConfigurableModel<RunInput, CallOptions>>} A class which extends BaseChatModel.
* @throws {Error} If modelProvider cannot be inferred or isn't supported.
* @throws {Error} If the model provider integration package is not installed.
*
* @example Initialize non-configurable models
* ```typescript
* import { initChatModel } from "langchain/chat_models/universal";
*
* const gpt4 = await initChatModel("openai:gpt-4", {
* temperature: 0.25,
* });
* const gpt4Result = await gpt4.invoke("what's your name");
*
* const claude = await initChatModel("anthropic:claude-3-opus-20240229", {
* temperature: 0.25,
* });
* const claudeResult = await claude.invoke("what's your name");
*
* const gemini = await initChatModel("gemini-1.5-pro", {
* modelProvider: "google-vertexai",
* temperature: 0.25,
* });
* const geminiResult = await gemini.invoke("what's your name");
* ```
*
* @example Create a partially configurable model with no default model
* ```typescript
* import { initChatModel } from "langchain/chat_models/universal";
*
* const configurableModel = await initChatModel(undefined, {
* temperature: 0,
* configurableFields: ["model", "apiKey"],
* });
*
* const gpt4Result = await configurableModel.invoke("what's your name", {
* configurable: {
* model: "gpt-4",
* },
* });
*
* const claudeResult = await configurableModel.invoke("what's your name", {
* configurable: {
* model: "claude-3-5-sonnet-20240620",
* },
* });
* ```
*
* @example Create a fully configurable model with a default model and a config prefix
* ```typescript
* import { initChatModel } from "langchain/chat_models/universal";
*
* const configurableModelWithDefault = await initChatModel("gpt-4", {
* modelProvider: "openai",
* configurableFields: "any",
* configPrefix: "foo",
* temperature: 0,
* });
*
* const openaiResult = await configurableModelWithDefault.invoke(
* "what's your name",
* {
* configurable: {
* foo_apiKey: process.env.OPENAI_API_KEY,
* },
* }
* );
*
* const claudeResult = await configurableModelWithDefault.invoke(
* "what's your name",
* {
* configurable: {
* foo_model: "claude-3-5-sonnet-20240620",
* foo_modelProvider: "anthropic",
* foo_temperature: 0.6,
* foo_apiKey: process.env.ANTHROPIC_API_KEY,
* },
* }
* );
* ```
*
* @example Bind tools to a configurable model:
* ```typescript
* import { initChatModel } from "langchain/chat_models/universal";
* import { z } from "zod";
* import { tool } from "@langchain/core/tools";
*
* const getWeatherTool = tool(
* (input) => {
* // Do something with the input
* return JSON.stringify(input);
* },
* {
* schema: z
* .object({
* location: z
* .string()
* .describe("The city and state, e.g. San Francisco, CA"),
* })
* .describe("Get the current weather in a given location"),
* name: "GetWeather",
* description: "Get the current weather in a given location",
* }
* );
*
* const getPopulationTool = tool(
* (input) => {
* // Do something with the input
* return JSON.stringify(input);
* },
* {
* schema: z
* .object({
* location: z
* .string()
* .describe("The city and state, e.g. San Francisco, CA"),
* })
* .describe("Get the current population in a given location"),
* name: "GetPopulation",
* description: "Get the current population in a given location",
* }
* );
*
* const configurableModel = await initChatModel("gpt-4", {
* configurableFields: ["model", "modelProvider", "apiKey"],
* temperature: 0,
* });
*
* const configurableModelWithTools = configurableModel.bindTools([
* getWeatherTool,
* getPopulationTool,
* ]);
*
* const configurableToolResult = await configurableModelWithTools.invoke(
* "Which city is hotter today and which is bigger: LA or NY?",
* {
* configurable: {
* apiKey: process.env.OPENAI_API_KEY,
* },
* }
* );
*
* const configurableToolResult2 = await configurableModelWithTools.invoke(
* "Which city is hotter today and which is bigger: LA or NY?",
* {
* configurable: {
* model: "claude-3-5-sonnet-20240620",
* apiKey: process.env.ANTHROPIC_API_KEY,
* },
* }
* );
* ```
*
* @description
* This function initializes a ChatModel based on the provided model name and provider.
* It supports various model providers and allows for runtime configuration of model parameters.
*
* Security Note: Setting `configurableFields` to "any" means fields like apiKey, baseUrl, etc.
* can be altered at runtime, potentially redirecting model requests to a different service/user.
* Make sure that if you're accepting untrusted configurations, you enumerate the
* `configurableFields` explicitly.
*
* The function will attempt to infer the model provider from the model name if not specified.
* Certain model name prefixes are associated with specific providers:
* - gpt-3... or gpt-4... -> openai
* - claude... -> anthropic
* - amazon.... -> bedrock
* - gemini... -> google-vertexai
* - command... -> cohere
* - accounts/fireworks... -> fireworks
*
* @since 0.2.11
* @version 0.2.11
*/
async function initChatModel(model,
// eslint-disable-next-line @typescript-eslint/no-explicit-any
fields) {
// eslint-disable-next-line prefer-const
let { configurableFields, configPrefix, modelProvider, ...params } = {
configPrefix: "",
...(fields ?? {}),
};
if (modelProvider === undefined && model?.includes(":")) {
const modelComponents = model.split(":", 2);
if (_SUPPORTED_PROVIDERS.includes(modelComponents[0])) {
// eslint-disable-next-line no-param-reassign
[modelProvider, model] = modelComponents;
}
}
let configurableFieldsCopy = Array.isArray(configurableFields)
? [...configurableFields]
: configurableFields;
if (!model && configurableFieldsCopy === undefined) {
configurableFieldsCopy = ["model", "modelProvider"];
}
if (configPrefix && configurableFieldsCopy === undefined) {
console.warn(`{ configPrefix: ${configPrefix} } has been set but no fields are configurable. Set ` +
`{ configurableFields: [...] } to specify the model params that are ` +
`configurable.`);
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
const paramsCopy = { ...params };
if (configurableFieldsCopy === undefined) {
return new ConfigurableModel({
defaultConfig: {
...paramsCopy,
model,
modelProvider,
},
configPrefix,
});
}
else {
if (model) {
paramsCopy.model = model;
}
if (modelProvider) {
paramsCopy.modelProvider = modelProvider;
}
return new ConfigurableModel({
defaultConfig: paramsCopy,
configPrefix,
configurableFields: configurableFieldsCopy,
});
}
}