docs/core_docs/README.md

@@ -4,6 +4,8 @@ This website is built using [Docusaurus 2](https://docusaurus.io/), a modern sta
 
 ### Installation
 
+First, make sure you have [dependencies installed](https://github.com/langchain-ai/langchainjs/blob/main/CONTRIBUTING.md#install-dependencies).
+
 ```
 $ yarn
 ```

docs/core_docs/docs/integrations/chat/anthropic.ipynb

@@ -509,7 +509,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 2,
       "id": "bba739ed",
       "metadata": {},
       "outputs": [],
@@ -783,7 +783,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 3,
       "id": "6e47de9b",
       "metadata": {},
       "outputs": [
@@ -793,16 +793,15 @@
           "text": [
             "USAGE: {\n",
             "  input_tokens: 19,\n",
-            "  cache_creation_input_tokens: 2925,\n",
+            "  cache_creation_input_tokens: 2921,\n",
             "  cache_read_input_tokens: 0,\n",
-            "  output_tokens: 327\n",
+            "  output_tokens: 355\n",
             "}\n"
           ]
         }
       ],
       "source": [
         "import { ChatAnthropic } from \"@langchain/anthropic\";\n",
-        "import { HumanMessage, SystemMessage } from \"@langchain/core/messages\";\n",
         "\n",
         "const modelWithCaching = new ChatAnthropic({\n",
         "  model: \"claude-3-haiku-20240307\",\n",
@@ -820,7 +819,8 @@
         "${CACHED_TEXT}`;\n",
         "\n",
         "const messages = [\n",
-        "  new SystemMessage({\n",
+        "  {\n",
+        "    role: \"system\",\n",
         "    content: [\n",
         "      {\n",
         "        type: \"text\",\n",
@@ -829,10 +829,11 @@
         "        cache_control: { type: \"ephemeral\" },\n",
         "      },\n",
         "    ],\n",
-        "  }),\n",
-        "  new HumanMessage({\n",
+        "  },\n",
+        "  {\n",
+        "    role: \"user\",\n",
         "    content: \"What types of messages are supported in LangChain?\",\n",
-        "  }),\n",
+        "  },\n",
         "];\n",
         "\n",
         "const res = await modelWithCaching.invoke(messages);\n",
@@ -852,7 +853,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 4,
       "id": "5d264f8b",
       "metadata": {},
       "outputs": [
@@ -863,8 +864,8 @@
             "USAGE: {\n",
             "  input_tokens: 19,\n",
             "  cache_creation_input_tokens: 0,\n",
-            "  cache_read_input_tokens: 2925,\n",
-            "  output_tokens: 250\n",
+            "  cache_read_input_tokens: 2921,\n",
+            "  output_tokens: 357\n",
             "}\n"
           ]
         }
@@ -961,6 +962,198 @@
         "await modelWithCustomClient.invoke([{ role: \"user\", content: \"Hello!\" }]);"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "id": "68a85a61",
+      "metadata": {},
+      "source": [
+        "## Citations\n",
+        "\n",
+        "Anthropic supports a [citations](https://docs.anthropic.com/en/docs/build-with-claude/citations) feature that lets Claude attach context to its answers based on source documents supplied by the user. When [document content blocks](https://docs.anthropic.com/en/docs/build-with-claude/citations#document-types) with `\"citations\": {\"enabled\": True}` are included in a query, Claude may generate citations in its response.\n",
+        "\n",
+        "### Simple example\n",
+        "\n",
+        "In this example we pass a [plain text document](https://docs.anthropic.com/en/docs/build-with-claude/citations#plain-text-documents). In the background, Claude [automatically chunks](https://docs.anthropic.com/en/docs/build-with-claude/citations#plain-text-documents) the input text into sentences, which are used when generating citations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "d3f1c754",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[\n",
+            "  {\n",
+            "    \"type\": \"text\",\n",
+            "    \"text\": \"Based on the document, I can tell you that:\\n\\n- \"\n",
+            "  },\n",
+            "  {\n",
+            "    \"type\": \"text\",\n",
+            "    \"text\": \"The grass is green\",\n",
+            "    \"citations\": [\n",
+            "      {\n",
+            "        \"type\": \"char_location\",\n",
+            "        \"cited_text\": \"The grass is green. \",\n",
+            "        \"document_index\": 0,\n",
+            "        \"document_title\": \"My Document\",\n",
+            "        \"start_char_index\": 0,\n",
+            "        \"end_char_index\": 20\n",
+            "      }\n",
+            "    ]\n",
+            "  },\n",
+            "  {\n",
+            "    \"type\": \"text\",\n",
+            "    \"text\": \"\\n- \"\n",
+            "  },\n",
+            "  {\n",
+            "    \"type\": \"text\",\n",
+            "    \"text\": \"The sky is blue\",\n",
+            "    \"citations\": [\n",
+            "      {\n",
+            "        \"type\": \"char_location\",\n",
+            "        \"cited_text\": \"The sky is blue.\",\n",
+            "        \"document_index\": 0,\n",
+            "        \"document_title\": \"My Document\",\n",
+            "        \"start_char_index\": 20,\n",
+            "        \"end_char_index\": 36\n",
+            "      }\n",
+            "    ]\n",
+            "  }\n",
+            "]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import { ChatAnthropic } from \"@langchain/anthropic\";\n",
+        "\n",
+        "const citationsModel = new ChatAnthropic({\n",
+        "  model: \"claude-3-5-haiku-latest\",\n",
+        "});\n",
+        "\n",
+        "const messagesWithCitations = [\n",
+        "  {\n",
+        "    role: \"user\",\n",
+        "    content: [\n",
+        "      {\n",
+        "        type: \"document\",\n",
+        "        source: {\n",
+        "          type: \"text\",\n",
+        "          media_type: \"text/plain\",\n",
+        "          data: \"The grass is green. The sky is blue.\",\n",
+        "        },\n",
+        "        title: \"My Document\",\n",
+        "        context: \"This is a trustworthy document.\",\n",
+        "        citations: {\n",
+        "          enabled: true,\n",
+        "        },\n",
+        "      },\n",
+        "      {\n",
+        "        type: \"text\",\n",
+        "        text: \"What color is the grass and sky?\",\n",
+        "      },\n",
+        "    ],\n",
+        "  }\n",
+        "];\n",
+        "\n",
+        "const responseWithCitations = await citationsModel.invoke(messagesWithCitations);\n",
+        "\n",
+        "console.log(JSON.stringify(responseWithCitations.content, null, 2));"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "14269f15",
+      "metadata": {},
+      "source": [
+        "### Using with text splitters\n",
+        "\n",
+        "Anthropic also lets you specify your own splits using [custom document](https://docs.anthropic.com/en/docs/build-with-claude/citations#custom-content-documents) types. LangChain [text splitters](/docs/concepts/text_splitters/) can be used to generate meaningful splits for this purpose. See the below example, where we split the LangChain.js README (a markdown document) and pass it to Claude as context:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5e9f3213",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[\n",
+            "  {\n",
+            "    \"type\": \"text\",\n",
+            "    \"text\": \"Based on the documentation, I can provide you with a link to LangChain's tutorials:\\n\\n\"\n",
+            "  },\n",
+            "  {\n",
+            "    \"type\": \"text\",\n",
+            "    \"text\": \"The tutorials can be found at: https://js.langchain.com/docs/tutorials/\",\n",
+            "    \"citations\": [\n",
+            "      {\n",
+            "        \"type\": \"content_block_location\",\n",
+            "        \"cited_text\": \"[Tutorial](https://js.langchain.com/docs/tutorials/)walkthroughs\",\n",
+            "        \"document_index\": 0,\n",
+            "        \"document_title\": null,\n",
+            "        \"start_block_index\": 191,\n",
+            "        \"end_block_index\": 194\n",
+            "      }\n",
+            "    ]\n",
+            "  }\n",
+            "]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import { ChatAnthropic } from \"@langchain/anthropic\";\n",
+        "import { MarkdownTextSplitter } from \"langchain/text_splitter\";\n",
+        "\n",
+        "function formatToAnthropicDocuments(documents: string[]) {\n",
+        "  return {\n",
+        "    type: \"document\",\n",
+        "    source: {\n",
+        "      type: \"content\",\n",
+        "      content: documents.map((document) => ({ type: \"text\", text: document })),\n",
+        "    },\n",
+        "    citations: { enabled: true },\n",
+        "  };\n",
+        "}\n",
+        "\n",
+        "// Pull readme\n",
+        "const readmeResponse = await fetch(\n",
+        "  \"https://raw.githubusercontent.com/langchain-ai/langchainjs/master/README.md\"\n",
+        ");\n",
+        "\n",
+        "const readme = await readmeResponse.text();\n",
+        "\n",
+        "// Split into chunks\n",
+        "const splitter = new MarkdownTextSplitter({\n",
+        "  chunkOverlap: 0,\n",
+        "  chunkSize: 50,\n",
+        "});\n",
+        "const documents = await splitter.splitText(readme);\n",
+        "\n",
+        "// Construct message\n",
+        "const messageWithSplitDocuments = {\n",
+        "  role: \"user\",\n",
+        "  content: [\n",
+        "    formatToAnthropicDocuments(documents),\n",
+        "    { type: \"text\", text: \"Give me a link to LangChain's tutorials. Cite your sources\" },\n",
+        "  ],\n",
+        "};\n",
+        "\n",
+        "// Query LLM\n",
+        "const citationsModelWithSplits = new ChatAnthropic({\n",
+        "  model: \"claude-3-5-sonnet-latest\",\n",
+        "});\n",
+        "const resWithSplits = await citationsModelWithSplits.invoke([messageWithSplitDocuments]);\n",
+        "\n",
+        "console.log(JSON.stringify(resWithSplits.content, null, 2));"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
@@ -992,4 +1185,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

docs/core_docs/docs/integrations/llms/replicate.mdx

@@ -9,7 +9,7 @@ import IntegrationInstallTooltip from "@mdx_components/integration_install_toolt
 <IntegrationInstallTooltip></IntegrationInstallTooltip>
 
 ```bash npm2yarn
-npm install replicate @langchain/community @langchain/core
+npm install replicate@1 @langchain/community @langchain/core
 ```
 
 import ReplicateLlama2 from "@examples/models/llm/replicate_llama2.ts";

docs/core_docs/docusaurus.config.js

@@ -135,6 +135,11 @@ const config = {
   themeConfig:
     /** @type {import('@docusaurus/preset-classic').ThemeConfig} */
     ({
+      announcementBar: {
+        content:
+          '<strong class="announcement-bar-text">Join us at <a href="https://interrupt.langchain.com/" target="_blank" rel="noopener noreferrer"> Interrupt: The Agent AI Conference by LangChain</a> on May 13 & 14 in San Francisco!</strong>',
+        backgroundColor: "#d0c9fe",
+      },
       prism: {
         theme: {
           ...baseLightCodeBlockTheme,

docs/core_docs/src/css/custom.css

@@ -311,3 +311,11 @@ nav, h1, h2, h3, h4 {
 [data-theme='dark'] .announcementBar_node_modules-\@docusaurus-theme-classic-lib-theme-AnnouncementBar-styles-module button {
   color: #fff;
 }
+
+.announcement-bar-text {
+  font-size: 16px;
+}
+
+div[class^='announcementBar_'] {
+  padding: 20px 0; 
+}

docs/core_docs/src/theme/ChatModelTabs.js

@@ -70,11 +70,11 @@ export default function ChatModelTabs(props) {
   const groqParams = props.groqParams ?? DEFAULTS.groqParams;
   const vertexParams = props.vertexParams ?? DEFAULTS.vertexParams;
   const providers = props.providers ?? [
+    "groq",
     "openai",
     "anthropic",
     "fireworks",
     "mistral",
-    "groq",
     "vertex",
   ];
 

examples/src/document_loaders/openai_whisper_audio.ts

@@ -2,7 +2,11 @@ import { OpenAIWhisperAudio } from "@langchain/community/document_loaders/fs/ope
 
 const filePath = "./src/document_loaders/example_data/test.mp3";
 
-const loader = new OpenAIWhisperAudio(filePath);
+const loader = new OpenAIWhisperAudio(filePath, {
+  transcriptionCreateParams: {
+    language: "en",
+  },
+});
 
 const docs = await loader.load();
 

langchain-core/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/core",
-  "version": "0.3.37",
+  "version": "0.3.38",
   "description": "Core LangChain.js abstractions and schemas",
   "type": "module",
   "engines": {