langchain[minor]: Generic chat models (#6171)

* langchain[minor]: Generic chat models

* fix build

* chore: lint files

* cr

* added docs and entrypoints

* cr

* tetss

* update expect error to plain ts-ignore

* implemented bindTools and withStructuredOutput

* cleanup jsdoc examples

* issues w docs

* cr

* Update docs/core_docs/docs/how_to/index.mdx

* drop docs

* always return ConfigurableModel and code review fixes

* make all actual versions, not workspace deps

* cr

* cr

* docs

* code review

* cr

* cr

* cr

* yarn

docs/core_docs/docs/how_to/chat_models_universal_init.mdx

@@ -0,0 +1,75 @@
+# How to init any model in one line
+
+import CodeBlock from "@theme/CodeBlock";
+
+Many LLM applications let end users specify what model provider and model they want the application to be powered by.
+This requires writing some logic to initialize different ChatModels based on some user configuration.
+The `initChatModel()` helper method makes it easy to initialize a number of different model integrations without having to worry about import paths and class names.
+Keep in mind this feature is only for chat models.
+
+:::info Prerequisites
+
+This guide assumes familiarity with the following concepts:
+
+- [Chat models](/docs/concepts/#chat-models)
+
+- [LangChain Expression Language (LCEL)](/docs/concepts#langchain-expression-language)
+
+- [Tool calling](/docs/concepts#tools)
+
+:::
+
+:::caution Compatibility
+**This feature is only intended to be used in Node environments. Use in non Node environments or with bundlers is not guaranteed to work and not officially supported.**
+
+`initChatModel` requires `langchain>=0.2.11`. See [this guide](/docs/how_to/installation/#installing-integration-packages) for some considerations to take when upgrading.
+
+See the [initChatModel()](https://v02.api.js.langchain.com/functions/langchain_chat_models_configurable.initChatModel.html) API reference for a full list of supported integrations.
+
+Make sure you have the integration packages installed for any model providers you want to support. E.g. you should have `@langchain/openai` installed to init an OpenAI model.
+:::
+
+## Basic usage
+
+import BasicExample from "@examples/models/chat/configurable/basic.ts";
+
+<CodeBlock language="typescript">{BasicExample}</CodeBlock>
+
+## Inferring model provider
+
+For common and distinct model names `initChatModel()` will attempt to infer the model provider.
+See the [API reference](https://v02.api.js.langchain.com/functions/langchain_chat_models_configurable.initChatModel.html) for a full list of inference behavior.
+E.g. any model that starts with `gpt-3...` or `gpt-4...` will be inferred as using model provider `openai`.
+
+import InferringProviderExample from "@examples/models/chat/configurable/inferring_model_provider.ts";
+
+<CodeBlock language="typescript">{InferringProviderExample}</CodeBlock>
+
+## Creating a configurable model
+
+You can also create a runtime-configurable model by specifying `configurableFields`.
+If you don't specify a `model` value, then "model" and "modelProvider" be configurable by default.
+
+import ConfigurableModelExample from "@examples/models/chat/configurable/configurable_model.ts";
+
+<CodeBlock language="typescript">{ConfigurableModelExample}</CodeBlock>
+
+### Configurable model with default values
+
+We can create a configurable model with default model values, specify which parameters are configurable, and add prefixes to configurable params:
+
+import ConfigurableModelWithDefaultsExample from "@examples/models/chat/configurable/configurable_model_with_defaults.ts";
+
+<CodeBlock language="typescript">
+  {ConfigurableModelWithDefaultsExample}
+</CodeBlock>
+
+### Using a configurable model declaratively
+
+We can call declarative operations like `bindTools`, `withStructuredOutput`, `withConfig`, etc. on a configurable model and chain a configurable model in the same way that we would a regularly instantiated chat model object.
+
+import ConfigurableModelDeclarativelyExample from "@examples/models/chat/configurable/configurable_model_declaratively.ts";
+
+<CodeBlock language="typescript">
+  {ConfigurableModelDeclarativelyExample}
+</CodeBlock>

docs/core_docs/docs/how_to/index.mdx

@@ -76,6 +76,7 @@ These are the core building blocks you can use when building applications.
 - [How to: stream tool calls](/docs/how_to/tool_streaming)
 - [How to: few shot prompt tool behavior](/docs/how_to/tool_calling#few-shotting-with-tools)
 - [How to: force a specific tool call](/docs/how_to/tool_choice)
+- [How to: init any model in one line](/docs/how_to/chat_models_universal_init/)
 
 ### Messages
 

examples/src/models/chat/configurable/basic.ts

@@ -0,0 +1,34 @@
+import { initChatModel } from "langchain/chat_models/universal";
+
+// Returns a @langchain/openai ChatOpenAI instance.
+const gpt4o = await initChatModel("gpt-4o", {
+  modelProvider: "openai",
+  temperature: 0,
+});
+// Returns a @langchain/anthropic ChatAnthropic instance.
+const claudeOpus = await initChatModel("claude-3-opus-20240229", {
+  modelProvider: "anthropic",
+  temperature: 0,
+});
+// Returns a @langchain/google-vertexai ChatVertexAI instance.
+const gemini15 = await initChatModel("gemini-1.5-pro", {
+  modelProvider: "google-vertexai",
+  temperature: 0,
+});
+
+// Since all model integrations implement the ChatModel interface, you can use them in the same way.
+console.log(`GPT-4o: ${(await gpt4o.invoke("what's your name")).content}\n`);
+console.log(
+  `Claude Opus: ${(await claudeOpus.invoke("what's your name")).content}\n`
+);
+console.log(
+  `Gemini 1.5: ${(await gemini15.invoke("what's your name")).content}\n`
+);
+
+/*
+GPT-4o: I'm an AI language model created by OpenAI, and I don't have a personal name. You can call me Assistant or any other name you prefer! How can I help you today?
+
+Claude Opus: My name is Claude. It's nice to meet you!
+
+Gemini 1.5: I don't have a name. I am a large language model, and I am not a person. I am a computer program that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.
+*/

examples/src/models/chat/configurable/configurable_model.ts

@@ -0,0 +1,19 @@
+import { initChatModel } from "langchain/chat_models/universal";
+
+const configurableModel = await initChatModel(undefined, { temperature: 0 });
+
+const gpt4Res = await configurableModel.invoke("what's your name", {
+  configurable: { model: "gpt-4o" },
+});
+console.log("gpt4Res: ", gpt4Res.content);
+/*
+gpt4Res:  I'm an AI language model created by OpenAI, and I don't have a personal name. You can call me Assistant or any other name you prefer! How can I assist you today?
+*/
+
+const claudeRes = await configurableModel.invoke("what's your name", {
+  configurable: { model: "claude-3-5-sonnet-20240620" },
+});
+console.log("claudeRes: ", claudeRes.content);
+/*
+claudeRes:  My name is Claude. It's nice to meet you!
+*/

examples/src/models/chat/configurable/configurable_model_declaratively.ts

@@ -0,0 +1,92 @@
+import { z } from "zod";
+import { tool } from "@langchain/core/tools";
+import { initChatModel } from "langchain/chat_models/universal";
+
+const GetWeather = z
+  .object({
+    location: z.string().describe("The city and state, e.g. San Francisco, CA"),
+  })
+  .describe("Get the current weather in a given location");
+const weatherTool = tool(
+  (_) => {
+    // do something
+    return "138 degrees";
+  },
+  {
+    name: "GetWeather",
+    schema: GetWeather,
+  }
+);
+
+const GetPopulation = z
+  .object({
+    location: z.string().describe("The city and state, e.g. San Francisco, CA"),
+  })
+  .describe("Get the current population in a given location");
+const populationTool = tool(
+  (_) => {
+    // do something
+    return "one hundred billion";
+  },
+  {
+    name: "GetPopulation",
+    schema: GetPopulation,
+  }
+);
+
+const llm = await initChatModel(undefined, { temperature: 0 });
+const llmWithTools = llm.bindTools([weatherTool, populationTool]);
+
+const toolCalls1 = (
+  await llmWithTools.invoke("what's bigger in 2024 LA or NYC", {
+    configurable: { model: "gpt-4o" },
+  })
+).tool_calls;
+console.log("toolCalls1: ", JSON.stringify(toolCalls1, null, 2));
+/*
+toolCalls1:  [
+  {
+    "name": "GetPopulation",
+    "args": {
+      "location": "Los Angeles, CA"
+    },
+    "type": "tool_call",
+    "id": "call_DXRBVE4xfLYZfhZOsW1qRbr5"
+  },
+  {
+    "name": "GetPopulation",
+    "args": {
+      "location": "New York, NY"
+    },
+    "type": "tool_call",
+    "id": "call_6ec3m4eWhwGz97sCbNt7kOvC"
+  }
+]
+*/
+
+const toolCalls2 = (
+  await llmWithTools.invoke("what's bigger in 2024 LA or NYC", {
+    configurable: { model: "claude-3-5-sonnet-20240620" },
+  })
+).tool_calls;
+console.log("toolCalls2: ", JSON.stringify(toolCalls2, null, 2));
+/*
+toolCalls2:  [
+  {
+    "name": "GetPopulation",
+    "args": {
+      "location": "Los Angeles, CA"
+    },
+    "id": "toolu_01K3jNU8jx18sJ9Y6Q9SooJ7",
+    "type": "tool_call"
+  },
+  {
+    "name": "GetPopulation",
+    "args": {
+      "location": "New York City, NY"
+    },
+    "id": "toolu_01UiANKaSwYykuF4hi3t5oNB",
+    "type": "tool_call"
+  }
+]
+*/

examples/src/models/chat/configurable/configurable_model_with_defaults.ts

@@ -0,0 +1,25 @@
+import { initChatModel } from "langchain/chat_models/universal";
+
+const firstLlm = await initChatModel("gpt-4o", {
+  temperature: 0,
+  configurableFields: ["model", "modelProvider", "temperature", "maxTokens"],
+  configPrefix: "first", // useful when you have a chain with multiple models
+});
+
+const openaiRes = await firstLlm.invoke("what's your name");
+console.log("openaiRes: ", openaiRes.content);
+/*
+openaiRes:  I'm an AI language model created by OpenAI, and I don't have a personal name. You can call me Assistant or any other name you prefer! How can I assist you today?
+*/
+
+const claudeRes = await firstLlm.invoke("what's your name", {
+  configurable: {
+    first_model: "claude-3-5-sonnet-20240620",
+    first_temperature: 0.5,
+    first_maxTokens: 100,
+  },
+});
+console.log("claudeRes: ", claudeRes.content);
+/*
+claudeRes:  My name is Claude. It's nice to meet you!
+*/

examples/src/models/chat/configurable/inferring_model_provider.ts

@@ -0,0 +1,11 @@
+import { initChatModel } from "langchain/chat_models/universal";
+
+const gpt4o = await initChatModel("gpt-4o", {
+  temperature: 0,
+});
+const claudeOpus = await initChatModel("claude-3-opus-20240229", {
+  temperature: 0,
+});
+const gemini15 = await initChatModel("gemini-1.5-pro", {
+  temperature: 0,
+});

langchain-core/src/runnables/base.ts

@@ -1311,7 +1311,6 @@ export class RunnableBinding<
   }
 
   async *transform(
-    // eslint-disable-next-line @typescript-eslint/no-explicit-any
     generator: AsyncGenerator<RunInput>,
     options: Partial<CallOptions>
   ): AsyncGenerator<RunOutput> {

langchain/.gitignore

@@ -122,6 +122,10 @@ chains/graph_qa/cypher.cjs
 chains/graph_qa/cypher.js
 chains/graph_qa/cypher.d.ts
 chains/graph_qa/cypher.d.cts
+chat_models/universal.cjs
+chat_models/universal.js
+chat_models/universal.d.ts
+chat_models/universal.d.cts
 embeddings/cache_backed.cjs
 embeddings/cache_backed.js
 embeddings/cache_backed.d.ts

langchain/langchain.config.js

@@ -65,6 +65,8 @@ export const config = {
     "chains/retrieval": "chains/retrieval",
     "chains/sql_db": "chains/sql_db/index",
     "chains/graph_qa/cypher": "chains/graph_qa/cypher",
+    // chat models
+    "chat_models/universal": "chat_models/universal",
     // embeddings
     "embeddings/cache_backed": "embeddings/cache_backed",
     "embeddings/fake": "embeddings/fake",
@@ -226,6 +228,7 @@ export const config = {
     "chains/load",
     "chains/sql_db",
     "chains/graph_qa/cypher",
+    "chat_models/universal",
     "llms/load",
     "prompts/load",
     "memory/zep",