FilipZmijewski · FilipZmijewski · Jan 30, 2025 · Jan 15, 2025 · Jan 18, 2025 · Jan 18, 2025
diff --git a/README.md b/README.md
@@ -43,7 +43,7 @@ The LangChain libraries themselves are made up of several different packages.
 - **[`@langchain/core`](https://github.com/langchain-ai/langchainjs/blob/main/langchain-core)**: Base abstractions and LangChain Expression Language.
 - **[`@langchain/community`](https://github.com/langchain-ai/langchainjs/blob/main/libs/langchain-community)**: Third party integrations.
 - **[`langchain`](https://github.com/langchain-ai/langchainjs/blob/main/langchain)**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
-- **[LangGraph.js](https://langchain-ai.github.io/langgraphjs/)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
+- **[LangGraph.js](https://langchain-ai.github.io/langgraphjs/)**: LangGraph powers production-grade agents, trusted by Linkedin, Uber, Klarna, GitLab, and many more. Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
 
 Integrations may also be split into their own compatible packages.
 

diff --git a/docs/core_docs/docs/concepts/structured_outputs.mdx b/docs/core_docs/docs/concepts/structured_outputs.mdx
@@ -79,7 +79,7 @@ Several more powerful methods that utilizes native features in the model provide
 
 Many [model providers support](/docs/integrations/chat/) tool calling, a concept discussed in more detail in our [tool calling guide](/docs/concepts/tool_calling/).
 In short, tool calling involves binding a tool to a model and, when appropriate, the model can _decide_ to call this tool and ensure its response conforms to the tool's schema.
-With this in mind, the central concept is straightforward: _simply bind our schema to a model as a tool!_
+With this in mind, the central concept is straightforward: _create a tool with our schema and bind it to the model!_
 Here is an example using the `ResponseFormatter` schema defined above:
 
 ```typescript
@@ -90,8 +90,14 @@ const model = new ChatOpenAI({
   temperature: 0,
 });
 
-// Bind ResponseFormatter schema as a tool to the model
-const modelWithTools = model.bindTools([ResponseFormatter]);
+// Create a tool with ResponseFormatter as its schema.
+const responseFormatterTool = tool(async () => {}, {
+  name: "responseFormatter",
+  schema: ResponseFormatter,
+});
+
+// Bind the created tool to the model
+const modelWithTools = model.bindTools([responseFormatterTool]);
 
 // Invoke the model
 const aiMsg = await modelWithTools.invoke(

diff --git a/docs/core_docs/docs/how_to/graph_constructing.ipynb b/docs/core_docs/docs/how_to/graph_constructing.ipynb
@@ -102,7 +102,7 @@
         "\n",
         "const model = new ChatOpenAI({\n",
         "    temperature: 0,\n",
-        "    model: \"gpt-4-turbo-preview\",\n",
+        "    model: \"gpt-4o-mini\",\n",
         "});\n",
         "\n",
         "const llmGraphTransformer = new LLMGraphTransformer({\n",

diff --git a/docs/core_docs/docs/how_to/query_high_cardinality.ipynb b/docs/core_docs/docs/how_to/query_high_cardinality.ipynb
@@ -392,7 +392,7 @@
       "metadata": {},
       "source": [
         "```{=mdx}\n",
-        "<ChatModelTabs customVarName=\"llmLong\" openaiParams={`{ model: \"gpt-4-turbo-preview\" }`} />\n",
+        "<ChatModelTabs customVarName=\"llmLong\" openaiParams={`{ model: \"gpt-4o-mini\" }`} />\n",
         "```"
       ]
     },
@@ -635,4 +635,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}
diff --git a/docs/core_docs/docs/integrations/chat/deepseek.ipynb b/docs/core_docs/docs/integrations/chat/deepseek.ipynb
@@ -0,0 +1,312 @@
+{
+  "cells": [
+    {
+      "cell_type": "raw",
+      "id": "afaf8039",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "---\n",
+        "sidebar_label: DeepSeek\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e49f1e0d",
+      "metadata": {},
+      "source": [
+        "# ChatDeepSeek\n",
+        "\n",
+        "This will help you getting started with DeepSeek [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatDeepSeek` features and configurations head to the [API reference](https://api.js.langchain.com/classes/_langchain_deepseek.ChatDeepSeek.html).\n",
+        "\n",
+        "## Overview\n",
+        "### Integration details\n",
+        "\n",
+        "| Class | Package | Local | Serializable | [PY support](https://python.langchain.com/docs/integrations/chat/deepseek) | Package downloads | Package latest |\n",
+        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+        "| [`ChatDeepSeek`](https://api.js.langchain.com/classes/_langchain_deepseek.ChatDeepSeek.html) | [`@langchain/deepseek`](https://npmjs.com/@langchain/deepseek) | ❌ (see [Ollama](/docs/integrations/chat/ollama)) | beta | ✅ | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/deepseek?style=flat-square&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/deepseek?style=flat-square&label=%20&) |\n",
+        "\n",
+        "### Model features\n",
+        "\n",
+        "See the links in the table headers below for guides on how to use specific features.\n",
+        "\n",
+        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: |\n",
+        "| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | \n",
+        "\n",
+        "Note that as of 1/27/25, tool calling and structured output are not currently supported for `deepseek-reasoner`.\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To access DeepSeek models you'll need to create a DeepSeek account, get an API key, and install the `@langchain/deepseek` integration package.\n",
+        "\n",
+        "You can also access the DeepSeek API through providers like [Together AI](/docs/integrations/chat/togetherai) or [Ollama](/docs/integrations/chat/ollama).\n",
+        "\n",
+        "### Credentials\n",
+        "\n",
+        "Head to https://deepseek.com/ to sign up to DeepSeek and generate an API key. Once you've done this set the `DEEPSEEK_API_KEY` environment variable:\n",
+        "\n",
+        "```bash\n",
+        "export DEEPSEEK_API_KEY=\"your-api-key\"\n",
+        "```\n",
+        "\n",
+        "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:\n",
+        "\n",
+        "```bash\n",
+        "# export LANGSMITH_TRACING=\"true\"\n",
+        "# export LANGSMITH_API_KEY=\"your-api-key\"\n",
+        "```\n",
+        "\n",
+        "### Installation\n",
+        "\n",
+        "The LangChain ChatDeepSeek integration lives in the `@langchain/deepseek` package:\n",
+        "\n",
+        "```{=mdx}\n",
+        "import IntegrationInstallTooltip from \"@mdx_components/integration_install_tooltip.mdx\";\n",
+        "import Npm2Yarn from \"@theme/Npm2Yarn\";\n",
+        "\n",
+        "<IntegrationInstallTooltip></IntegrationInstallTooltip>\n",
+        "\n",
+        "<Npm2Yarn>\n",
+        "  @langchain/deepseek @langchain/core\n",
+        "</Npm2Yarn>\n",
+        "\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+      "metadata": {},
+      "source": [
+        "## Instantiation\n",
+        "\n",
+        "Now we can instantiate our model object and generate chat completions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import { ChatDeepSeek } from \"@langchain/deepseek\";\n",
+        "\n",
+        "const llm = new ChatDeepSeek({\n",
+        "  model: \"deepseek-reasoner\",\n",
+        "  temperature: 0,\n",
+        "  // other params...\n",
+        "})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2b4f3e15",
+      "metadata": {},
+      "source": [
+        "<!-- ## Invocation -->"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "62e0dbc3",
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "AIMessage {\n",
+            "  \"id\": \"e2874482-68a7-4552-8154-b6a245bab429\",\n",
+            "  \"content\": \"J'adore la programmation.\",\n",
+            "  \"additional_kwargs\": {,\n",
+            "    \"reasoning_content\": \"...\",\n",
+            "  },\n",
+            "  \"response_metadata\": {\n",
+            "    \"tokenUsage\": {\n",
+            "      \"promptTokens\": 23,\n",
+            "      \"completionTokens\": 7,\n",
+            "      \"totalTokens\": 30\n",
+            "    },\n",
+            "    \"finish_reason\": \"stop\",\n",
+            "    \"model_name\": \"deepseek-reasoner\",\n",
+            "    \"usage\": {\n",
+            "      \"prompt_tokens\": 23,\n",
+            "      \"completion_tokens\": 7,\n",
+            "      \"total_tokens\": 30,\n",
+            "      \"prompt_tokens_details\": {\n",
+            "        \"cached_tokens\": 0\n",
+            "      },\n",
+            "      \"prompt_cache_hit_tokens\": 0,\n",
+            "      \"prompt_cache_miss_tokens\": 23\n",
+            "    },\n",
+            "    \"system_fingerprint\": \"fp_3a5770e1b4\"\n",
+            "  },\n",
+            "  \"tool_calls\": [],\n",
+            "  \"invalid_tool_calls\": [],\n",
+            "  \"usage_metadata\": {\n",
+            "    \"output_tokens\": 7,\n",
+            "    \"input_tokens\": 23,\n",
+            "    \"total_tokens\": 30,\n",
+            "    \"input_token_details\": {\n",
+            "      \"cache_read\": 0\n",
+            "    },\n",
+            "    \"output_token_details\": {}\n",
+            "  }\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "const aiMsg = await llm.invoke([\n",
+        "  [\n",
+        "    \"system\",\n",
+        "    \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
+        "  ],\n",
+        "  [\"human\", \"I love programming.\"],\n",
+        "])\n",
+        "aiMsg"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "J'adore la programmation.\n"
+          ]
+        }
+      ],
+      "source": [
+        "console.log(aiMsg.content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+      "metadata": {},
+      "source": [
+        "## Chaining\n",
+        "\n",
+        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "AIMessage {\n",
+            "  \"id\": \"6e7f6f8c-8d7a-4dad-be07-425384038fd4\",\n",
+            "  \"content\": \"Ich liebe es zu programmieren.\",\n",
+            "  \"additional_kwargs\": {,\n",
+            "    \"reasoning_content\": \"...\",\n",
+            "  },\n",
+            "  \"response_metadata\": {\n",
+            "    \"tokenUsage\": {\n",
+            "      \"promptTokens\": 18,\n",
+            "      \"completionTokens\": 9,\n",
+            "      \"totalTokens\": 27\n",
+            "    },\n",
+            "    \"finish_reason\": \"stop\",\n",
+            "    \"model_name\": \"deepseek-reasoner\",\n",
+            "    \"usage\": {\n",
+            "      \"prompt_tokens\": 18,\n",
+            "      \"completion_tokens\": 9,\n",
+            "      \"total_tokens\": 27,\n",
+            "      \"prompt_tokens_details\": {\n",
+            "        \"cached_tokens\": 0\n",
+            "      },\n",
+            "      \"prompt_cache_hit_tokens\": 0,\n",
+            "      \"prompt_cache_miss_tokens\": 18\n",
+            "    },\n",
+            "    \"system_fingerprint\": \"fp_3a5770e1b4\"\n",
+            "  },\n",
+            "  \"tool_calls\": [],\n",
+            "  \"invalid_tool_calls\": [],\n",
+            "  \"usage_metadata\": {\n",
+            "    \"output_tokens\": 9,\n",
+            "    \"input_tokens\": 18,\n",
+            "    \"total_tokens\": 27,\n",
+            "    \"input_token_details\": {\n",
+            "      \"cache_read\": 0\n",
+            "    },\n",
+            "    \"output_token_details\": {}\n",
+            "  }\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "import { ChatPromptTemplate } from \"@langchain/core/prompts\"\n",
+        "\n",
+        "const prompt = ChatPromptTemplate.fromMessages(\n",
+        "  [\n",
+        "    [\n",
+        "      \"system\",\n",
+        "      \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+        "    ],\n",
+        "    [\"human\", \"{input}\"],\n",
+        "  ]\n",
+        ")\n",
+        "\n",
+        "const chain = prompt.pipe(llm);\n",
+        "await chain.invoke(\n",
+        "  {\n",
+        "    input_language: \"English\",\n",
+        "    output_language: \"German\",\n",
+        "    input: \"I love programming.\",\n",
+        "  }\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+      "metadata": {},
+      "source": [
+        "## API reference\n",
+        "\n",
+        "For detailed documentation of all ChatDeepSeek features and configurations head to the API reference: https://api.js.langchain.com/classes/_langchain_deepseek.ChatDeepSeek.html"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "TypeScript",
+      "language": "typescript",
+      "name": "tslab"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "mode": "typescript",
+        "name": "javascript",
+        "typescript": true
+      },
+      "file_extension": ".ts",
+      "mimetype": "text/typescript",
+      "name": "typescript",
+      "version": "3.7.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}