all[minor]: Unified model params

docs/core_docs/docs/expression_language/how_to/message_history.mdx

@@ -61,7 +61,7 @@ const prompt = ChatPromptTemplate.fromMessages([
 ]);
 
 const chain = prompt.pipe(
-  new ChatAnthropic({ modelName: "claude-3-sonnet-20240229" })
+  new ChatAnthropic({ model: "claude-3-sonnet-20240229" })
 );
 ```
 

docs/core_docs/docs/get_started/quickstart.mdx

@@ -71,13 +71,13 @@ Accessing the API requires an API key, which you can get by creating an account
 OPENAI_API_KEY="..."
 ```
 
-If you'd prefer not to set an environment variable you can pass the key in directly via the `openAIApiKey` named parameter when initiating the OpenAI Chat Model class:
+If you'd prefer not to set an environment variable you can pass the key in directly via the `apiKey` named parameter when initiating the OpenAI Chat Model class:
 
 ```typescript
 import { ChatOpenAI } from "@langchain/openai";
 
 const chatModel = new ChatOpenAI({
-  openAIApiKey: "...",
+  apiKey: "...",
 });
 ```
 
@@ -135,13 +135,13 @@ Accessing the API requires an API key, which you can get by creating an account
 ANTHROPIC_API_KEY="..."
 ```
 
-If you'd prefer not to set an environment variable you can pass the key in directly via the `anthropicApiKey` named parameter when initiating the Anthropic Chat Model class:
+If you'd prefer not to set an environment variable you can pass the key in directly via the `apiKey` named parameter when initiating the Anthropic Chat Model class:
 
 ```typescript
 import { ChatAnthropic } from "@langchain/anthropic";
 
 const chatModel = new ChatAnthropic({
-  anthropicApiKey: "...",
+  apiKey: "...",
 });
 ```
 
@@ -602,7 +602,7 @@ const agentPrompt = await pull<ChatPromptTemplate>(
 );
 
 const agentModel = new ChatOpenAI({
-  modelName: "gpt-3.5-turbo-1106",
+  model: "gpt-3.5-turbo-1106",
   temperature: 0,
 });
 

docs/core_docs/docs/guides/langsmith_evaluation.mdx

@@ -73,7 +73,7 @@ const prompt = await pull<ChatPromptTemplate>(
 );
 
 const llm = new ChatOpenAI({
-  modelName: "gpt-3.5-turbo-1106",
+  model: "gpt-3.5-turbo-1106",
   temperature: 0,
 });
 

docs/core_docs/docs/integrations/chat/anthropic_tools.mdx

@@ -32,8 +32,8 @@ import { ChatAnthropicTools } from "@langchain/anthropic/experimental";
 
 const model = new ChatAnthropicTools({
   temperature: 0.1,
-  modelName: "claude-3-sonnet-20240229",
-  anthropicApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.ANTHROPIC_API_KEY
+  model: "claude-3-sonnet-20240229",
+  apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.ANTHROPIC_API_KEY
 });
 ```
 

docs/core_docs/docs/integrations/chat/azure.mdx

@@ -67,9 +67,9 @@ import { AzureChatOpenAI } from "@langchain/azure-openai";
 
 const model = new AzureChatOpenAI({
   azureOpenAIEndpoint: "<your_endpoint>",
-  azureOpenAIApiKey: "<your_key>",
+  apiKey: "<your_key>",
   azureOpenAIApiDeploymentName: "<your_embedding_deployment_name",
-  modelName: "<your_model>",
+  model: "<your_model>",
 });
 ```
 
@@ -85,7 +85,7 @@ const model = new AzureChatOpenAI({
   credentials,
   azureOpenAIEndpoint: "<your_endpoint>",
   azureOpenAIApiDeploymentName: "<your_embedding_deployment_name",
-  modelName: "<your_model>",
+  model: "<your_model>",
 });
 ```
 

docs/core_docs/docs/integrations/llms/azure.mdx

@@ -35,7 +35,7 @@ import { AzureOpenAI } from "@langchain/azure-openai";
 
 const model = new AzureOpenAI({
   azureOpenAIEndpoint: "<your_endpoint>",
-  azureOpenAIApiKey: "<your_key>",
+  apiKey: "<your_key>",
   azureOpenAIApiDeploymentName: "<your_deployment_name",
 });
 ```
@@ -67,7 +67,7 @@ const model = new AzureOpenAI({
   credentials,
   azureOpenAIEndpoint: "<your_endpoint>",
   azureOpenAIApiDeploymentName: "<your_deployment_name",
-  modelName: "<your_model>",
+  model: "<your_model>",
 });
 ```
 
@@ -103,7 +103,7 @@ import { OpenAI } from "@langchain/openai";
 
 const model = new OpenAI({
   temperature: 0.9,
-  azureOpenAIApiKey: "YOUR-API-KEY",
+  apiKey: "YOUR-API-KEY",
   azureOpenAIApiVersion: "YOUR-API-VERSION",
   azureOpenAIApiInstanceName: "{MY_INSTANCE_NAME}",
   azureOpenAIApiDeploymentName: "{DEPLOYMENT_NAME}",
@@ -122,7 +122,7 @@ import { OpenAI } from "@langchain/openai";
 
 const model = new OpenAI({
   temperature: 0.9,
-  azureOpenAIApiKey: "YOUR-API-KEY",
+  apiKey: "YOUR-API-KEY",
   azureOpenAIApiVersion: "YOUR-API-VERSION",
   azureOpenAIApiDeploymentName: "{DEPLOYMENT_NAME}",
   azureOpenAIBasePath:

docs/core_docs/docs/integrations/llms/openai.mdx

@@ -14,9 +14,9 @@ npm install @langchain/openai
 import { OpenAI } from "@langchain/openai";
 
 const model = new OpenAI({
-  modelName: "gpt-3.5-turbo-instruct", // Defaults to "gpt-3.5-turbo-instruct" if no model provided.
+  model: "gpt-3.5-turbo-instruct", // Defaults to "gpt-3.5-turbo-instruct" if no model provided.
   temperature: 0.9,
-  openAIApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.OPENAI_API_KEY
+  apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.OPENAI_API_KEY
 });
 const res = await model.invoke(
   "What would be a good company name a company that makes colorful socks?"

docs/core_docs/docs/integrations/llms/prompt_layer_openai.mdx

@@ -10,7 +10,7 @@ import { PromptLayerOpenAI } from "langchain/llms/openai";
 
 const model = new PromptLayerOpenAI({
   temperature: 0.9,
-  openAIApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.OPENAI_API_KEY
+  apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.OPENAI_API_KEY
   promptLayerApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.PROMPTLAYER_API_KEY
 });
 const res = await model.invoke(

docs/core_docs/docs/integrations/platforms/google.mdx

@@ -35,7 +35,7 @@ export GOOGLE_API_KEY=your-api-key
 import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
 
 const model = new ChatGoogleGenerativeAI({
-  modelName: "gemini-pro",
+  model: "gemini-pro",
   maxOutputTokens: 2048,
 });
 
@@ -52,7 +52,7 @@ Gemini vision models support image inputs when providing a single human message.
 
 ```typescript
 const visionModel = new ChatGoogleGenerativeAI({
-  modelName: "gemini-pro-vision",
+  model: "gemini-pro-vision",
   maxOutputTokens: 2048,
 });
 const image = fs.readFileSync("./hotdog.jpg").toString("base64");
@@ -105,7 +105,7 @@ import { ChatVertexAI } from "@langchain/google-vertexai";
 // import { ChatVertexAI } from "@langchain/google-vertexai-web";
 
 const model = new ChatVertexAI({
-  modelName: "gemini-1.0-pro",
+  model: "gemini-1.0-pro",
   maxOutputTokens: 2048,
 });
 
@@ -122,7 +122,7 @@ Gemini vision models support image inputs when providing a single human message.
 
 ```typescript
 const visionModel = new ChatVertexAI({
-  modelName: "gemini-pro-vision",
+  model: "gemini-pro-vision",
   maxOutputTokens: 2048,
 });
 const image = fs.readFileSync("./hotdog.png").toString("base64");

docs/core_docs/docs/integrations/text_embedding/azure_openai.mdx

@@ -41,7 +41,7 @@ import { AzureOpenAI } from "@langchain/azure-openai";
 
 const model = new AzureOpenAI({
   azureOpenAIEndpoint: "<your_endpoint>",
-  azureOpenAIApiKey: "<your_key>",
+  apiKey: "<your_key>",
   azureOpenAIApiDeploymentName: "<your_embedding_deployment_name",
 });
 ```

docs/core_docs/docs/integrations/text_embedding/openai.mdx

@@ -14,9 +14,9 @@ npm install @langchain/openai
 import { OpenAIEmbeddings } from "@langchain/openai";
 
 const embeddings = new OpenAIEmbeddings({
-  openAIApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.OPENAI_API_KEY
+  apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.OPENAI_API_KEY
   batchSize: 512, // Default value if omitted is 512. Max is 2048
-  modelName: "text-embedding-3-large",
+  model: "text-embedding-3-large",
 });
 ```
 
@@ -29,7 +29,7 @@ With the `text-embedding-3` class of models, you can specify the size of the emb
 
 ```typescript
 const embeddings = new OpenAIEmbeddings({
-  modelName: "text-embedding-3-large",
+  model: "text-embedding-3-large",
 });
 
 const vectors = await embeddings.embedDocuments(["some text"]);
@@ -44,7 +44,7 @@ But by passing in `dimensions: 1024` we can reduce the size of our embeddings to
 
 ```typescript
 const embeddings1024 = new OpenAIEmbeddings({
-  modelName: "text-embedding-3-large",
+  model: "text-embedding-3-large",
   dimensions: 1024,
 });
 

docs/core_docs/docs/modules/agents/agent_types/openai_functions_agent.mdx

@@ -52,7 +52,7 @@ const prompt = await pull<ChatPromptTemplate>(
 );
 
 const llm = new ChatOpenAI({
-  modelName: "gpt-3.5-turbo-1106",
+  model: "gpt-3.5-turbo-1106",
   temperature: 0,
 });
 

docs/core_docs/docs/modules/agents/agent_types/openai_tools_agent.mdx

@@ -54,7 +54,7 @@ import type { ChatPromptTemplate } from "@langchain/core/prompts";
 const prompt = await pull<ChatPromptTemplate>("hwchase17/openai-tools-agent");
 
 const llm = new ChatOpenAI({
-  modelName: "gpt-3.5-turbo-1106",
+  model: "gpt-3.5-turbo-1106",
   temperature: 0,
 });
 

docs/core_docs/docs/modules/agents/agent_types/react.mdx

@@ -42,7 +42,7 @@ import type { PromptTemplate } from "@langchain/core/prompts";
 const prompt = await pull<PromptTemplate>("hwchase17/react");
 
 const llm = new OpenAI({
-  modelName: "gpt-3.5-turbo-instruct",
+  model: "gpt-3.5-turbo-instruct",
   temperature: 0,
 });
 

docs/core_docs/docs/modules/agents/agent_types/structured_chat.mdx

@@ -51,7 +51,7 @@ const prompt = await pull<ChatPromptTemplate>(
 );
 
 const llm = new ChatOpenAI({
-  modelName: "gpt-3.5-turbo-1106",
+  model: "gpt-3.5-turbo-1106",
   temperature: 0,
 });
 

docs/core_docs/docs/modules/agents/how_to/agent_structured.mdx

@@ -37,7 +37,7 @@ Next, we initialize an LLM and a search tool that wraps our web search retriever
 
 ```typescript
 const llm = new ChatOpenAI({
-  modelName: "gpt-4-1106-preview",
+  model: "gpt-4-1106-preview",
 });
 
 const searchTool = new DynamicTool({

docs/core_docs/docs/modules/agents/how_to/custom_agent.mdx

@@ -23,7 +23,7 @@ import { ChatOpenAI } from "@langchain/openai";
  * Define your chat model to use.
  */
 const model = new ChatOpenAI({
-  modelName: "gpt-3.5-turbo",
+  model: "gpt-3.5-turbo",
   temperature: 0,
 });
 ```

docs/core_docs/docs/modules/agents/how_to/max_iterations.mdx

@@ -16,7 +16,7 @@ import { AgentExecutor, createReactAgent } from "langchain/agents";
 const tools = [new Calculator()];
 
 const llm = new ChatOpenAI({
-  modelName: "gpt-3.5-turbo",
+  model: "gpt-3.5-turbo",
   temperature: 0,
 });
 

docs/core_docs/docs/modules/agents/quick_start.mdx

@@ -117,7 +117,7 @@ First, we choose the LLM we want to be guiding the agent.
 import { ChatOpenAI } from "@langchain/openai";
 
 const llm = new ChatOpenAI({
-  modelName: "gpt-3.5-turbo",
+  model: "gpt-3.5-turbo",
   temperature: 0,
 });
 ```

docs/core_docs/docs/modules/model_io/chat/caching.mdx

@@ -16,7 +16,7 @@ import { ChatOpenAI } from "@langchain/openai";
 
 // To make the caching really obvious, lets use a slower model.
 const model = new ChatOpenAI({
-  modelName: "gpt-4",
+  model: "gpt-4",
   cache: true,
 });
 ```