feat(langchain): Add hub entrypoint with automatic dynamic entrypoint of models

langchain/.gitignore

@@ -294,6 +294,10 @@ hub.cjs
 hub.js
 hub.d.ts
 hub.d.cts
+hub/node.cjs
+hub/node.js
+hub/node.d.ts
+hub/node.d.cts
 util/document.cjs
 util/document.js
 util/document.d.ts

langchain/langchain.config.js

@@ -118,7 +118,8 @@ export const config = {
     "storage/in_memory": "storage/in_memory",
     "storage/file_system": "storage/file_system",
     // hub
-    hub: "hub",
+    hub: "hub/index",
+    "hub/node": "hub/node",
     // utilities
     "util/document": "util/document",
     "util/math": "util/math",
@@ -217,6 +218,7 @@ export const config = {
     "storage/file_system",
     // Prevent export due to circular dependency with "load" entrypoint
     "hub",
+    "hub/node",
     "experimental/prompts/handlebars",
   ],
   extraImportMapEntries: [

langchain/package.json

@@ -306,6 +306,10 @@
     "hub.js",
     "hub.d.ts",
     "hub.d.cts",
+    "hub/node.cjs",
+    "hub/node.js",
+    "hub/node.d.ts",
+    "hub/node.d.cts",
     "util/document.cjs",
     "util/document.js",
     "util/document.d.ts",
@@ -1222,6 +1226,15 @@
       "import": "./hub.js",
       "require": "./hub.cjs"
     },
+    "./hub/node": {
+      "types": {
+        "import": "./hub/node.d.ts",
+        "require": "./hub/node.d.cts",
+        "default": "./hub/node.d.ts"
+      },
+      "import": "./hub/node.js",
+      "require": "./hub/node.cjs"
+    },
     "./util/document": {
       "types": {
         "import": "./util/document.d.ts",

langchain/src/agents/tests/create_openai_functions_agent.int.test.ts

@@ -4,7 +4,7 @@ import { test, expect } from "@jest/globals";
 import { ChatOpenAI } from "@langchain/openai";
 import type { ChatPromptTemplate } from "@langchain/core/prompts";
 import { TavilySearchResults } from "../../util/testing/tools/tavily_search.js";
-import { pull } from "../../hub.js";
+import { pull } from "../../hub/index.js";
 import { AgentExecutor, createOpenAIFunctionsAgent } from "../index.js";
 
 const tools = [new TavilySearchResults({ maxResults: 1 })];

langchain/src/agents/tests/create_openai_tools_agent.int.test.ts

@@ -8,7 +8,7 @@ import { tool } from "@langchain/core/tools";
 import { z } from "zod";
 import { AsyncLocalStorage } from "async_hooks";
 import { TavilySearchResults } from "../../util/testing/tools/tavily_search.js";
-import { pull } from "../../hub.js";
+import { pull } from "../../hub/index.js";
 import { AgentExecutor, createOpenAIToolsAgent } from "../index.js";
 
 const tools = [new TavilySearchResults({ maxResults: 1 })];

langchain/src/agents/tests/create_react_agent.int.test.ts

@@ -2,7 +2,7 @@ import { test, expect } from "@jest/globals";
 import { OpenAI } from "@langchain/openai";
 import type { PromptTemplate } from "@langchain/core/prompts";
 import { TavilySearchResults } from "../../util/testing/tools/tavily_search.js";
-import { pull } from "../../hub.js";
+import { pull } from "../../hub/index.js";
 import { AgentExecutor, createReactAgent } from "../index.js";
 
 const tools = [new TavilySearchResults({ maxResults: 1 })];

langchain/src/agents/tests/create_structured_chat_agent.int.test.ts

@@ -2,7 +2,7 @@ import { test, expect } from "@jest/globals";
 import { ChatOpenAI } from "@langchain/openai";
 import type { ChatPromptTemplate } from "@langchain/core/prompts";
 import { TavilySearchResults } from "../../util/testing/tools/tavily_search.js";
-import { pull } from "../../hub.js";
+import { pull } from "../../hub/index.js";
 import { AgentExecutor, createStructuredChatAgent } from "../index.js";
 
 const tools = [new TavilySearchResults({ maxResults: 1 })];

langchain/src/agents/tests/create_xml_agent.int.test.ts

@@ -2,7 +2,7 @@ import { test, expect } from "@jest/globals";
 import type { PromptTemplate } from "@langchain/core/prompts";
 import { ChatOpenAI } from "@langchain/openai";
 import { TavilySearchResults } from "../../util/testing/tools/tavily_search.js";
-import { pull } from "../../hub.js";
+import { pull } from "../../hub/index.js";
 import { AgentExecutor, createXmlAgent } from "../index.js";
 
 const tools = [new TavilySearchResults({ maxResults: 1 })];

langchain/src/chat_models/tests/universal.int.test.ts

@@ -1,4 +1,5 @@
 /* eslint-disable no-process-env */
+/* eslint-disable @typescript-eslint/no-explicit-any */
 import { tool } from "@langchain/core/tools";
 import { z } from "zod";
 import { it } from "@jest/globals";
@@ -8,7 +9,7 @@ import { AIMessageChunk } from "@langchain/core/messages";
 import { concat } from "@langchain/core/utils/stream";
 import { awaitAllCallbacks } from "@langchain/core/callbacks/promises";
 import { AgentExecutor, createReactAgent } from "../../agents/index.js";
-import { pull } from "../../hub.js";
+import { pull } from "../../hub/index.js";
 import { initChatModel } from "../universal.js";
 
 // Make copies of API keys and remove them from the environment to avoid conflicts.

langchain/src/chat_models/universal.ts

@@ -75,6 +75,7 @@ async function _initChatModelHelper(
       `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 {

langchain/src/hub.ts → langchain/src/hub/base.ts

@@ -1,7 +1,6 @@
-import { Client } from "langsmith";
-import { Runnable } from "@langchain/core/runnables";
 import type { BaseLanguageModel } from "@langchain/core/language_models/base";
-import { load } from "./load/index.js";
+import type { Runnable } from "@langchain/core/runnables";
+import { Client } from "langsmith";
 
 /**
  * Push a prompt to the hub.
@@ -11,7 +10,7 @@ import { load } from "./load/index.js";
  * @param options
  * @returns The URL of the newly pushed prompt in the hub.
  */
-export async function push(
+export async function basePush(
   repoFullName: string,
   runnable: Runnable,
   options?: {
@@ -40,21 +39,9 @@ export async function push(
   return client.pushPrompt(repoFullName, payloadOptions);
 }
 
-/**
- * Pull a prompt from the hub.
- * @param ownerRepoCommit The name of the repo containing the prompt, as well as an optional commit hash separated by a slash.
- * @param options
- * @returns
- */
-export async function pull<T extends Runnable>(
+export async function basePull(
   ownerRepoCommit: string,
-  options?: {
-    apiKey?: string;
-    apiUrl?: string;
-    includeModel?: boolean;
-    // eslint-disable-next-line @typescript-eslint/no-explicit-any
-    modelClass?: new (...args: any[]) => BaseLanguageModel;
-  }
+  options?: { apiKey?: string; apiUrl?: string; includeModel?: boolean }
 ) {
   const client = new Client(options);
 
@@ -76,13 +63,46 @@ export async function pull<T extends Runnable>(
     lc_hub_commit_hash: promptObject.commit_hash,
   };
 
+  // Some nested mustache prompts have improperly parsed variables that include a dot.
+  if (promptObject.manifest.kwargs.template_format === "mustache") {
+    const stripDotNotation = (varName: string) => varName.split(".")[0];
+
+    const { input_variables } = promptObject.manifest.kwargs;
+    if (Array.isArray(input_variables)) {
+      promptObject.manifest.kwargs.input_variables =
+        input_variables.map(stripDotNotation);
+    }
+
+    const { messages } = promptObject.manifest.kwargs;
+    if (Array.isArray(messages)) {
+      // eslint-disable-next-line @typescript-eslint/no-explicit-any
+      promptObject.manifest.kwargs.messages = messages.map((message: any) => {
+        const nestedVars = message?.kwargs?.prompt?.kwargs?.input_variables;
+        if (Array.isArray(nestedVars)) {
+          // eslint-disable-next-line no-param-reassign
+          message.kwargs.prompt.kwargs.input_variables =
+            nestedVars.map(stripDotNotation);
+        }
+        return message;
+      });
+    }
+  }
+  return promptObject;
+}
+
+export function generateModelImportMap(
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  modelClass?: new (...args: any[]) => BaseLanguageModel
+) {
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
   const modelImportMap: Record<string, any> = {};
   // TODO: Fix in 0.4.0. We can't get lc_id without instantiating the class, so we
   // must put them inline here. In the future, make this less hacky
   // This should probably use dynamic imports and have a web-only entrypoint
   // in a future breaking release
-  if (options?.modelClass !== undefined) {
-    const modelLcName = (options.modelClass as any)?.lc_name();
+  if (modelClass !== undefined) {
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    const modelLcName = (modelClass as any)?.lc_name();
     let importMapKey;
     if (modelLcName === "ChatAnthropic") {
       importMapKey = "chat_models__anthropic";
@@ -105,62 +125,8 @@ export async function pull<T extends Runnable>(
     }
     modelImportMap[importMapKey] = {
       ...modelImportMap[importMapKey],
-      [modelLcName]: options.modelClass,
+      [modelLcName]: modelClass,
     };
   }
-
-  // Some nested mustache prompts have improperly parsed variables that include a dot.
-  if (promptObject.manifest.kwargs.template_format === "mustache") {
-    const stripDotNotation = (varName: string) => varName.split(".")[0];
-
-    const { input_variables } = promptObject.manifest.kwargs;
-    if (Array.isArray(input_variables)) {
-      promptObject.manifest.kwargs.input_variables =
-        input_variables.map(stripDotNotation);
-    }
-
-    const { messages } = promptObject.manifest.kwargs;
-    if (Array.isArray(messages)) {
-      promptObject.manifest.kwargs.messages = messages.map((message: any) => {
-        const nestedVars = message?.kwargs?.prompt?.kwargs?.input_variables;
-        if (Array.isArray(nestedVars)) {
-          // eslint-disable-next-line no-param-reassign
-          message.kwargs.prompt.kwargs.input_variables =
-            nestedVars.map(stripDotNotation);
-        }
-        return message;
-      });
-    }
-  }
-
-  try {
-    const loadedPrompt = await load<T>(
-      JSON.stringify(promptObject.manifest),
-      undefined,
-      undefined,
-      modelImportMap
-    );
-    return loadedPrompt;
-  } catch (e: any) {
-    if (options?.includeModel && options?.modelClass === undefined) {
-      throw new Error(
-        [
-          e.message,
-          "",
-          `To load prompts with an associated non-OpenAI model, you must pass a "modelClass" parameter like this:`,
-          "",
-          "```",
-          `import { ChatAnthropic } from "@langchain/anthropic";`,
-          "",
-          `const prompt = await pull("my-prompt", {`,
-          `  includeModel: true,`,
-          `  modelClass: ChatAnthropic,`,
-          `});`,
-          "```",
-        ].join("\n")
-      );
-    } else {
-      throw e;
-    }
-  }
+  return modelImportMap;
 }

langchain/src/hub/index.ts

@@ -0,0 +1,67 @@
+import { Runnable } from "@langchain/core/runnables";
+import type { BaseLanguageModel } from "@langchain/core/language_models/base";
+import { load } from "../load/index.js";
+import { basePush, basePull, generateModelImportMap } from "./base.js";
+
+export { basePush as push };
+
+/**
+ * Pull a prompt from the hub.
+ *
+ * @param ownerRepoCommit The name of the repo containing the prompt, as well as an optional commit hash separated by a slash.
+ * @param options.apiKey LangSmith API key to use when pulling the prompt
+ * @param options.apiUrl LangSmith API URL to use when pulling the prompt
+ * @param options.includeModel Whether to also instantiate and attach a model instance to the prompt,
+ *   if the prompt has associated model metadata. If set to true, invoking the resulting pulled prompt will
+ *   also invoke the instantiated model. For non-OpenAI models, you must also set "modelClass" to the
+ *   correct class of the model.
+ * @param options.modelClass If includeModel is true, the class of the model to instantiate. Required
+ *   for non-OpenAI models. If you are running in Node or another environment that supports dynamic imports,
+ *   you may instead import this function from "langchain/hub/node" and pass "includeModel: true" instead
+ *   of specifying this parameter.
+ * @returns
+ */
+export async function pull<T extends Runnable>(
+  ownerRepoCommit: string,
+  options?: {
+    apiKey?: string;
+    apiUrl?: string;
+    includeModel?: boolean;
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    modelClass?: new (...args: any[]) => BaseLanguageModel;
+  }
+) {
+  const promptObject = await basePull(ownerRepoCommit, options);
+  try {
+    const loadedPrompt = await load<T>(
+      JSON.stringify(promptObject.manifest),
+      undefined,
+      undefined,
+      generateModelImportMap(options?.modelClass)
+    );
+    return loadedPrompt;
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  } catch (e: any) {
+    if (options?.includeModel) {
+      throw new Error(
+        [
+          e.message,
+          "",
+          `To load prompts with an associated non-OpenAI model, you must use the "langchain/hub/node" entrypoint, or pass a "modelClass" parameter like this:`,
+          "",
+          "```",
+          `import { pull } from "langchain/hub";`,
+          `import { ChatAnthropic } from "@langchain/anthropic";`,
+          "",
+          `const prompt = await pull("my-prompt", {`,
+          `  includeModel: true,`,
+          `  modelClass: ChatAnthropic,`,
+          `});`,
+          "```",
+        ].join("\n")
+      );
+    } else {
+      throw e;
+    }
+  }
+}