chore(langchain): remove dependency to openai provider package

examples/src/guides/evaluation/comparision_evaluator/pairwise_embedding_distance.ts

@@ -1,9 +1,12 @@
-import { OpenAIEmbeddings } from "@langchain/openai";
+import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
 import { loadEvaluator } from "langchain/evaluation";
 
 const embedding = new OpenAIEmbeddings();
 
-const chain = await loadEvaluator("pairwise_embedding_distance", { embedding });
+const chain = await loadEvaluator("pairwise_embedding_distance", {
+  embedding,
+  llm: new ChatOpenAI({ model: "gpt-4o-mini" }),
+});
 
 const res = await chain.evaluateStringPairs({
   prediction: "Seattle is hot in June",

examples/src/guides/evaluation/comparision_evaluator/pairwise_string_custom_criteria.ts

@@ -1,4 +1,5 @@
 import { loadEvaluator } from "langchain/evaluation";
+import { ChatOpenAI } from "@langchain/openai";
 
 const customCriterion = {
   simplicity: "Is the language straightforward and unpretentious?",
@@ -9,6 +10,7 @@ const customCriterion = {
 };
 
 const chain = await loadEvaluator("pairwise_string", {
+  llm: new ChatOpenAI({ model: "gpt-4o-mini" }),
   criteria: customCriterion,
 });
 

examples/src/guides/evaluation/comparision_evaluator/pairwise_string_custom_llm.ts

@@ -3,7 +3,9 @@ import { ChatAnthropic } from "@langchain/anthropic";
 
 const model = new ChatAnthropic({ temperature: 0 });
 
-const chain = await loadEvaluator("labeled_pairwise_string", { llm: model });
+const chain = await loadEvaluator("labeled_pairwise_string", {
+  llm: model,
+});
 
 const res = await chain.evaluateStringPairs({
   prediction: "there are three dogs",

examples/src/guides/evaluation/comparision_evaluator/pairwise_string_custom_prompt.ts

@@ -1,5 +1,6 @@
 import { loadEvaluator } from "langchain/evaluation";
 import { PromptTemplate } from "@langchain/core/prompts";
+import { ChatOpenAI } from "@langchain/openai";
 
 const promptTemplate = PromptTemplate.fromTemplate(
   `Given the input context, which do you prefer: A or B?
@@ -19,6 +20,7 @@ Reasoning:
 );
 
 const chain = await loadEvaluator("labeled_pairwise_string", {
+  llm: new ChatOpenAI({ model: "gpt-4o-mini" }),
   chainOptions: {
     prompt: promptTemplate,
   },

examples/src/guides/evaluation/comparision_evaluator/pairwise_string_with_reference.ts

@@ -1,6 +1,8 @@
 import { loadEvaluator } from "langchain/evaluation";
+import { ChatOpenAI } from "@langchain/openai";
 
 const chain = await loadEvaluator("labeled_pairwise_string", {
+  llm: new ChatOpenAI({ model: "gpt-4o-mini" }),
   criteria: "correctness",
 });
 

examples/src/guides/evaluation/comparision_evaluator/pairwise_string_without_reference.ts

@@ -1,6 +1,8 @@
 import { loadEvaluator } from "langchain/evaluation";
+import { ChatOpenAI } from "@langchain/openai";
 
 const chain = await loadEvaluator("pairwise_string", {
+  llm: new ChatOpenAI({ model: "gpt-4o-mini" }),
   criteria: "conciseness",
 });
 

examples/src/guides/evaluation/string/configuring_criteria_prompt.ts

@@ -1,5 +1,6 @@
 import { loadEvaluator } from "langchain/evaluation";
 import { PromptTemplate } from "@langchain/core/prompts";
+import { ChatOpenAI } from "@langchain/openai";
 
 const template = `Respond Y or N based on how well the following response follows the specified rubric. Grade only based on the rubric and expected response:
 
@@ -14,6 +15,7 @@ const template = `Respond Y or N based on how well the following response follow
         Write out your explanation for each criterion, then respond with Y or N on a new line.`;
 
 const chain = await loadEvaluator("labeled_criteria", {
+  llm: new ChatOpenAI({ model: "gpt-4o-mini" }),
   criteria: "correctness",
   chainOptions: {
     prompt: PromptTemplate.fromTemplate(template),

examples/src/guides/evaluation/string/constitutional_criteria.ts

@@ -1,5 +1,6 @@
 import { loadEvaluator } from "langchain/evaluation";
 import { PRINCIPLES } from "langchain/chains";
+import { ChatOpenAI } from "@langchain/openai";
 
 console.log(`${Object.keys(PRINCIPLES).length} available principles`);
 console.log(Object.entries(PRINCIPLES).slice(0, 5));
@@ -52,6 +53,7 @@ console.log(Object.entries(PRINCIPLES).slice(0, 5));
  */
 
 const chain = await loadEvaluator("criteria", {
+  llm: new ChatOpenAI({ model: "gpt-4o-mini" }),
   criteria: PRINCIPLES.harmful1,
 });
 

examples/src/guides/evaluation/string/criteria_with_reference.ts

@@ -1,6 +1,8 @@
 import { loadEvaluator } from "langchain/evaluation";
+import { ChatOpenAI } from "@langchain/openai";
 
 const evaluator = await loadEvaluator("labeled_criteria", {
+  llm: new ChatOpenAI({ model: "gpt-4o-mini" }),
   criteria: "correctness",
 });
 

examples/src/guides/evaluation/string/criteria_without_reference.ts

@@ -1,6 +1,10 @@
 import { loadEvaluator } from "langchain/evaluation";
+import { ChatOpenAI } from "@langchain/openai";
 
-const evaluator = await loadEvaluator("criteria", { criteria: "conciseness" });
+const evaluator = await loadEvaluator("criteria", {
+  criteria: "conciseness",
+  llm: new ChatOpenAI({ model: "gpt-4o-mini" }),
+});
 
 const res = await evaluator.evaluateStrings({
   input: "What's 2+2?",

examples/src/guides/evaluation/string/custom_criteria.ts

@@ -1,10 +1,12 @@
 import { loadEvaluator } from "langchain/evaluation";
+import { ChatOpenAI } from "@langchain/openai";
 
 const customCriterion = {
   numeric: "Does the output contain numeric or mathematical information?",
 };
 
 const evaluator = await loadEvaluator("criteria", {
+  llm: new ChatOpenAI({ model: "gpt-4o-mini" }),
   criteria: customCriterion,
 });
 
@@ -36,6 +38,7 @@ const customMultipleCriterion = {
 };
 
 const chain = await loadEvaluator("criteria", {
+  llm: new ChatOpenAI({ model: "gpt-4o-mini" }),
   criteria: customMultipleCriterion,
 });
 

examples/src/guides/evaluation/string/embedding_distance.ts

@@ -1,7 +1,10 @@
 import { loadEvaluator } from "langchain/evaluation";
 import { FakeEmbeddings } from "@langchain/core/utils/testing";
+import { ChatOpenAI } from "@langchain/openai";
 
-const chain = await loadEvaluator("embedding_distance");
+const chain = await loadEvaluator("embedding_distance", {
+  llm: new ChatOpenAI({ model: "gpt-4o-mini" }),
+});
 
 const res = await chain.evaluateStrings({
   prediction: "I shall go",
@@ -28,6 +31,7 @@ console.log({ res1 });
 // Select the Distance Metric
 // By default, the evalutor uses cosine distance. You can choose a different distance metric if you'd like.
 const evaluator = await loadEvaluator("embedding_distance", {
+  llm: new ChatOpenAI({ model: "gpt-4o-mini" }),
   distanceMetric: "euclidean",
 });
 
@@ -37,6 +41,7 @@ const evaluator = await loadEvaluator("embedding_distance", {
 const embedding = new FakeEmbeddings();
 
 const customEmbeddingEvaluator = await loadEvaluator("embedding_distance", {
+  llm: new ChatOpenAI({ model: "gpt-4o-mini" }),
   embedding,
 });
 

internal/build/constants.ts

@@ -44,36 +44,6 @@ export const extraImportMapEntries: Record<
   }[]
 > = {
   langchain: [
-    {
-      modules: ["ChatOpenAI"],
-      alias: ["chat_models", "openai"],
-      path: "@langchain/openai",
-    },
-    {
-      modules: ["AzureChatOpenAI"],
-      alias: ["chat_models", "azure_openai"],
-      path: "@langchain/openai",
-    },
-    {
-      modules: ["OpenAI"],
-      alias: ["llms", "openai"],
-      path: "@langchain/openai",
-    },
-    {
-      modules: ["AzureOpenAI"],
-      alias: ["llms", "azure_openai"],
-      path: "@langchain/openai",
-    },
-    {
-      modules: ["OpenAIEmbeddings"],
-      alias: ["embeddings", "openai"],
-      path: "@langchain/openai",
-    },
-    {
-      modules: ["AzureOpenAIEmbeddings"],
-      alias: ["embeddings", "azure_openai"],
-      path: "@langchain/openai",
-    },
     {
       modules: ["PromptTemplate"],
       alias: ["prompts", "prompt"],

libs/langchain/package.json

@@ -36,6 +36,7 @@
     "@langchain/cohere": "workspace:*",
     "@langchain/core": "workspace:*",
     "@langchain/eslint": "workspace:*",
+    "@langchain/openai": "workspace:*",
     "@tsconfig/recommended": "^1.0.2",
     "@types/js-yaml": "^4",
     "@types/jsdom": "^21.1.1",
@@ -77,7 +78,6 @@
     }
   },
   "dependencies": {
-    "@langchain/openai": "workspace:*",
     "@langchain/textsplitters": "workspace:*",
     "@langchain/langgraph": "^1.0.0-alpha",
     "@langchain/langgraph-checkpoint": "^0.1.1",

libs/langchain/src/chains/index.ts

@@ -72,7 +72,6 @@ export type {
   SerializedVectorDBQAChain,
   SerializedRefineDocumentsChain,
 } from "./serde.js";
-export { OpenAIModerationChain } from "./openai_moderation.js";
 export {
   MultiRouteChain,
   type MultiRouteChainInput,

libs/langchain/src/chains/openai_functions/index.ts

@@ -8,11 +8,6 @@ export {
   createTaggingChainFromZod,
 } from "./tagging.js";
 export { type OpenAPIChainOptions, createOpenAPIChain } from "./openapi.js";
-export {
-  type StructuredOutputChainInput,
-  createStructuredOutputChain,
-  createStructuredOutputChainFromZod,
-} from "./structured_output.js";
 export {
   type CreateStructuredOutputRunnableConfig,
   createStructuredOutputRunnable,

libs/langchain/src/chains/openai_functions/openapi.ts

@@ -7,7 +7,6 @@ import {
 import type { OpenAPIV3_1 } from "openapi-types";
 
 import { ChainValues } from "@langchain/core/utils/types";
-import { ChatOpenAI } from "@langchain/openai";
 import { BaseChatModel } from "@langchain/core/language_models/chat_models";
 import { BaseFunctionCallOptions } from "@langchain/core/language_models/base";
 import {
@@ -482,8 +481,13 @@ export async function createOpenAPIChain(
       `Could not parse any valid operations from the provided spec.`
     );
   }
+
+  if (!options.llm) {
+    throw new Error("`llm` option is required");
+  }
+
   const {
-    llm = new ChatOpenAI({ model: "gpt-3.5-turbo-0613" }),
+    llm = options.llm,
     prompt = ChatPromptTemplate.fromMessages([
       HumanMessagePromptTemplate.fromTemplate(
         "Use the provided API's to respond to this user query:\n\n{query}"