FilipZmijewski · FilipZmijewski · Feb 28, 2025 · Feb 7, 2025 · Feb 7, 2025 · Feb 7, 2025
diff --git a/docs/core_docs/docs/how_to/message_history.ipynb b/docs/core_docs/docs/how_to/message_history.ipynb
@@ -202,7 +202,7 @@
         "]\n",
         "const output = await app.invoke({ messages: input }, config)\n",
         "// The output contains all messages in the state.\n",
-        "// This will long the last message in the conversation.\n",
+        "// This will log the last message in the conversation.\n",
         "console.log(output.messages[output.messages.length - 1]);"
       ]
     },
@@ -583,4 +583,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}
diff --git a/docs/core_docs/docs/how_to/sequence.ipynb b/docs/core_docs/docs/how_to/sequence.ipynb
@@ -36,7 +36,7 @@
         "\n",
         "## The pipe method\n",
         "\n",
-        "To show off how this works, let's go through an example. We'll walk through a common pattern in LangChain: using a [prompt template](/docs/concepts/prompt_templates) to format input into a [chat model](/docs/concepts/chat_models), and finally converting the chat message output into a string with an [output parser](/docs/concepts/output_parsers.\n",
+        "To show off how this works, let's go through an example. We'll walk through a common pattern in LangChain: using a [prompt template](/docs/concepts/prompt_templates) to format input into a [chat model](/docs/concepts/chat_models), and finally converting the chat message output into a string with an [output parser](/docs/concepts/output_parsers).\n",
         "\n",
         "```{=mdx}\n",
         "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
@@ -249,4 +249,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}
diff --git a/docs/core_docs/docs/integrations/chat/google_vertex_ai.ipynb b/docs/core_docs/docs/integrations/chat/google_vertex_ai.ipynb
@@ -48,7 +48,8 @@
         "## Setup\n",
         "\n",
         "LangChain.js supports two different authentication methods based on whether\n",
-        "you're running in a Node.js environment or a web environment.\n",
+        "you're running in a Node.js environment or a web environment. It also supports\n",
+        "the authentication method used by Vertex AI Express Mode using either package.\n",
         "\n",
         "To access `ChatVertexAI` models you'll need to setup Google VertexAI in your Google Cloud Platform (GCP) account, save the credentials file, and install the `@langchain/google-vertexai` integration package.\n",
         "\n",
@@ -66,6 +67,13 @@
         "GOOGLE_VERTEX_AI_WEB_CREDENTIALS={\"type\":\"service_account\",\"project_id\":\"YOUR_PROJECT-12345\",...}\n",
         "```\n",
         "\n",
+        "If you are using Vertex AI Express Mode, you can install either the `@langchain/google-vertexai` or `@langchain/google-vertexai-web` package.\n",
+        "You can then go to the [Express Mode](https://console.cloud.google.com/vertex-ai/studio) API Key page and set your API Key in the `GOOGLE_API_KEY` environment variable:\n",
+        "\n",
+        "```bash\n",
+        "export GOOGLE_API_KEY=\"api_key_value\"\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",

diff --git a/docs/core_docs/docs/integrations/chat/ibm.ipynb b/docs/core_docs/docs/integrations/chat/ibm.ipynb
@@ -171,7 +171,8 @@
         "  version: \"YYYY-MM-DD\",\n",
         "  serviceUrl: process.env.API_URL,\n",
         "  projectId: \"<PROJECT_ID>\",\n",
-        "  spaceId: \"<SPACE_ID>\",\n",
+        "  // spaceId: \"<SPACE_ID>\",\n",
+        "  // idOrName: \"<DEPLOYMENT_ID>\",\n",
         "  model: \"<MODEL_ID>\",\n",
         "  ...props\n",
         "});"
@@ -184,7 +185,7 @@
       "source": [
         "Note:\n",
         "\n",
-        "- You must provide `spaceId` or `projectId` in order to proceed.\n",
+        "- You must provide `spaceId`, `projectId` or `idOrName`(deployment id) unless you use lighweight engine which works without specifying either (refer to [watsonx.ai docs](https://www.ibm.com/docs/en/cloud-paks/cp-data/5.0.x?topic=install-choosing-installation-mode))\n",
         "- Depending on the region of your provisioned service instance, use correct serviceUrl."
       ]
     },

diff --git a/...re_docs/docs/integrations/document_loaders/web_loaders/google_cloud_storage.mdx b/...re_docs/docs/integrations/document_loaders/web_loaders/google_cloud_storage.mdx
@@ -0,0 +1,33 @@
+---
+hide_table_of_contents: true
+sidebar_class_name: node-only
+---
+
+# Google Cloud Storage
+
+:::tip Compatibility
+Only available on Node.js.
+:::
+
+This covers how to load a Google Cloud Storage File into LangChain documents.
+
+## Setup
+
+To use this loader, you'll need to have Unstructured already set up and ready to use at an available URL endpoint. It can also be configured to run locally.
+
+See the docs [here](/docs/integrations/document_loaders/file_loaders/unstructured) for information on how to do that.
+
+You'll also need to install the official Google Cloud Storage SDK:
+
+```bash npm2yarn
+npm install @langchain/community @langchain/core @google-cloud/storage
+```
+
+## Usage
+
+Once Unstructured is configured, you can use the Google Cloud Storage loader to load files and then convert them into a Document.
+
+import CodeBlock from "@theme/CodeBlock";
+import Example from "@examples/document_loaders/google_cloud_storage.ts";
+
+<CodeBlock language="typescript">{Example}</CodeBlock>
diff --git a/docs/core_docs/docs/integrations/llms/ibm.ipynb b/docs/core_docs/docs/integrations/llms/ibm.ipynb
@@ -171,8 +171,8 @@
         "  version: \"YYYY-MM-DD\",\n",
         "  serviceUrl: process.env.API_URL,\n",
         "  projectId: \"<PROJECT_ID>\",\n",
-        "  spaceId: \"<SPACE_ID>\",\n",
-        "  idOrName: \"<DEPLOYMENT_ID>\",\n",
+        "  // spaceId: \"<SPACE_ID>\",\n",
+        "  // idOrName: \"<DEPLOYMENT_ID>\",\n",
         "  model: \"<MODEL_ID>\",\n",
         "  ...props,\n",
         "});"
@@ -185,7 +185,7 @@
       "source": [
         "Note:\n",
         "\n",
-        "- You must provide `spaceId`, `projectId` or `idOrName`(deployment id) in order to proceed.\n",
+        "- You must provide `spaceId`, `projectId` or `idOrName`(deployment id) unless you use lighweight engine which works without specifying either (refer to [watsonx.ai docs](https://www.ibm.com/docs/en/cloud-paks/cp-data/5.0.x?topic=install-choosing-installation-mode))\n",
         "- Depending on the region of your provisioned service instance, use correct serviceUrl.\n",
         "- You need to specify the model you want to use for inferencing through model_id."
       ]

diff --git a/docs/core_docs/docs/integrations/memory/aurora_dsql.mdx b/docs/core_docs/docs/integrations/memory/aurora_dsql.mdx
@@ -0,0 +1,43 @@
+---
+hide_table_of_contents: true
+sidebar_class_name: node-only
+---
+
+import CodeBlock from "@theme/CodeBlock";
+
+# Aurora DSQL Chat Memory
+
+For longer-term persistence across chat sessions, you can swap out the default in-memory `chatHistory` for the serverless PostgreSQL-compatible [Amazon Aurora DSQL](https://aws.amazon.com/rds/aurora/dsql/) Database.
+
+This is very similar to the PostgreSQL integration with a few differences to make it compatible with DSQL:
+
+1. The `id` column in PostgreSQL is SERIAL auto-incrementent, and DSQL is UUID using the database function `gen_random_uuid`.
+2. A `created_at` column is created to track the order and history of the messages.
+3. The `message` column in PostgreSQL is JSONB, and DSQL is TEXT with Javascript parsing handling
+
+## Setup
+
+Go to you AWS Console and create an Aurora DSQL Cluster, https://console.aws.amazon.com/dsql/clusters
+
+import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
+
+<IntegrationInstallTooltip></IntegrationInstallTooltip>
+
+```bash npm2yarn
+npm install @langchain/openai @langchain/community @langchain/core pg @aws-sdk/dsql-signer
+```
+
+## Usage
+
+Each chat history session is stored in a Aurora DSQL (Postgres-compatible) database and requires a session id.
+
+The connection to Aurora DSQL is handled through a PostgreSQL pool. You can either pass an instance of a pool via the `pool` parameter or pass a pool config via the `poolConfig` parameter. See [pg-node docs on pools](https://node-postgres.com/apis/pool)
+for more information. A provided pool takes precedence, thus if both a pool instance and a pool config are passed, only the pool will be used.
+
+For options on how to do the authentication and authorization for DSQL please check https://docs.aws.amazon.com/aurora-dsql/latest/userguide/authentication-authorization.html.
+
+The following example uses the AWS-SDK to generate an authentication token that is passed to the pool configuration:
+
+import Example from "@examples/memory/aurora_dsql.ts";
+
+<CodeBlock language="typescript">{Example}</CodeBlock>
diff --git a/docs/core_docs/docs/integrations/retrievers/self_query/pinecone.ipynb b/docs/core_docs/docs/integrations/retrievers/self_query/pinecone.ipynb
@@ -56,7 +56,7 @@
     "\n",
     "The vector store lives in the `@langchain/pinecone` package. You'll also need to install the `langchain` package to import the main `SelfQueryRetriever` class.\n",
     "\n",
-    "The official Pinecone SDK (`@pinecone-database/pinecone`) is automatically installed as a dependency of `@langchain/pinecone`, but you may wish to install it independently as well.\n",
+    "You will also need to install the official Pinecone SDK (`@pinecone-database/pinecone@5`).\n",
     "\n",
     "For this example, we'll also use OpenAI embeddings, so you'll need to install the `@langchain/openai` package and [obtain an API key](https://platform.openai.com):\n",
     "\n",

diff --git a/docs/core_docs/docs/integrations/text_embedding/pinecone.ipynb b/docs/core_docs/docs/integrations/text_embedding/pinecone.ipynb
@@ -60,7 +60,7 @@
         "<IntegrationInstallTooltip></IntegrationInstallTooltip>\n",
         "\n",
         "<Npm2Yarn>\n",
-        "  @langchain/pinecone @langchain/core\n",
+        "  @langchain/pinecone @langchain/core @pinecone-database/pinecone@5\n",
         "</Npm2Yarn>\n",
         "```"
       ]

diff --git a/docs/core_docs/docs/integrations/text_embedding/voyageai.mdx b/docs/core_docs/docs/integrations/text_embedding/voyageai.mdx
@@ -8,12 +8,23 @@ The `inputType` parameter allows you to specify the type of input text for bette
 - `document`: Use this for documents or content that you want to be retrievable. Voyage AI will prepend a prompt to optimize the embeddings for document use cases.
 - `None` (default): The input text will be directly encoded without any additional prompt.
 
+Additionally, the class supports new parameters for further customization of the embedding process:
+
+- **truncation**: Whether to truncate the input texts to the maximum length allowed by the model.
+- **outputDimension**: The desired dimension of the output embeddings.
+- **outputDtype**: The data type of the output embeddings. Can be `"float"` or `"int8"`.
+- **encodingFormat**: The format of the output embeddings. Can be `"float"`, `"base64"`, or `"ubinary"`.
+
 ```typescript
 import { VoyageEmbeddings } from "@langchain/community/embeddings/voyage";
 
 const embeddings = new VoyageEmbeddings({
   apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.VOYAGEAI_API_KEY
   inputType: "document", // Optional: specify input type as 'query', 'document', or omit for None / Undefined / Null
+  truncation: true, // Optional: enable truncation of input texts
+  outputDimension: 768, // Optional: set desired output embedding dimension
+  outputDtype: "float", // Optional: set output data type ("float" or "int8")
+  encodingFormat: "float", // Optional: set output encoding format ("float", "base64", or "ubinary")
 });
 ```
 

diff --git a/docs/core_docs/docs/integrations/vectorstores/pinecone.ipynb b/docs/core_docs/docs/integrations/vectorstores/pinecone.ipynb
@@ -58,7 +58,7 @@
         "<IntegrationInstallTooltip></IntegrationInstallTooltip>\n",
         "\n",
         "<Npm2Yarn>\n",
-        "  @langchain/pinecone @langchain/openai @langchain/core @pinecone-database/pinecone \n",
+        "  @langchain/pinecone @langchain/openai @langchain/core @pinecone-database/pinecone@5\n",
         "</Npm2Yarn>\n",
         "```\n",
         "\n",
@@ -363,4 +363,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}
diff --git a/docs/core_docs/docs/tutorials/chatbot.ipynb b/docs/core_docs/docs/tutorials/chatbot.ipynb
@@ -470,7 +470,7 @@
     "]\n",
     "const output = await app.invoke({ messages: input }, config)\n",
     "// The output contains all messages in the state.\n",
-    "// This will long the last message in the conversation.\n",
+    "// This will log the last message in the conversation.\n",
     "console.log(output.messages[output.messages.length - 1]);"
    ]
   },

diff --git a/docs/core_docs/docs/tutorials/rag.ipynb b/docs/core_docs/docs/tutorials/rag.ipynb
@@ -372,7 +372,7 @@
     "\n",
     "- [Docs](/docs/concepts/document_loaders): Detailed documentation on how to use\n",
     "- [Integrations](/docs/integrations/document_loaders/)\n",
-    "- [Interface](https:/api.js.langchain.com/classes/langchain.document_loaders_base.BaseDocumentLoader.html): API reference for the base interface.\n",
+    "- [Interface](https://api.js.langchain.com/classes/langchain.document_loaders_base.BaseDocumentLoader.html): API reference for the base interface.\n",
     "\n",
     "### Splitting documents\n",
     "\n",

diff --git a/docs/core_docs/vercel.json b/docs/core_docs/vercel.json
@@ -100,6 +100,18 @@
     {
       "source": "/docs/modules/model_io/prompts/quick_start/",
       "destination": "/docs/concepts/prompt_templates"
+    },
+    {
+      "source": "/docs/modules/model_io/prompts(/?)",
+      "destination": "/docs/concepts/prompt_templates"
+    },
+    {
+      "source": "/docs/guides/expression_language/cookbook(/?)",
+      "destination": "/docs/how_to/sequence"
+    },
+    {
+      "source": "/docs/modules/model_io/models(/?)",
+      "destination": "/docs/integrations/chat/"
     }
   ]
 }
diff --git a/examples/package.json b/examples/package.json
@@ -24,6 +24,7 @@
   "author": "LangChain",
   "license": "MIT",
   "dependencies": {
+    "@aws-sdk/dsql-signer": "^3.738.0",
     "@azure/identity": "^4.2.1",
     "@browserbasehq/stagehand": "^1.3.0",
     "@clickhouse/client": "^0.2.5",
@@ -68,7 +69,7 @@
     "@langchain/yandex": "workspace:*",
     "@layerup/layerup-security": "^1.5.12",
     "@opensearch-project/opensearch": "^2.2.0",
-    "@pinecone-database/pinecone": "^4.0.0",
+    "@pinecone-database/pinecone": "^5.0.2",
     "@planetscale/database": "^1.8.0",
     "@prisma/client": "^4.11.0",
     "@qdrant/js-client-rest": "^1.9.0",
@@ -87,7 +88,6 @@
     "date-fns": "^3.3.1",
     "duck-duck-scrape": "^2.2.5",
     "exa-js": "^1.0.12",
-    "faiss-node": "^0.5.1",
     "firebase-admin": "^12.0.0",
     "graphql": "^16.6.0",
     "hdb": "^0.19.8",

diff --git a/examples/src/document_loaders/google_cloud_storage.ts b/examples/src/document_loaders/google_cloud_storage.ts
@@ -0,0 +1,17 @@
+import { GoogleCloudStorageLoader } from "@langchain/community/document_loaders/web/google_cloud_storage";
+
+const loader = new GoogleCloudStorageLoader({
+  bucket: "my-bucket-123",
+  file: "path/to/file.pdf",
+  storageOptions: {
+    keyFilename: "/path/to/keyfile.json",
+  },
+  unstructuredLoaderOptions: {
+    apiUrl: "http://localhost:8000/general/v0/general",
+    apiKey: "", // this will be soon required
+  },
+});
+
+const docs = await loader.load();
+
+console.log(docs);
diff --git a/examples/src/memory/aurora_dsql.ts b/examples/src/memory/aurora_dsql.ts
@@ -0,0 +1,88 @@
+import pg from "pg";
+
+import { DsqlSigner } from "@aws-sdk/dsql-signer";
+import { AuroraDsqlChatMessageHistory } from "@langchain/community/stores/message/aurora_dsql";
+import { ChatOpenAI } from "@langchain/openai";
+import { RunnableWithMessageHistory } from "@langchain/core/runnables";
+
+import {
+  ChatPromptTemplate,
+  MessagesPlaceholder,
+} from "@langchain/core/prompts";
+import { StringOutputParser } from "@langchain/core/output_parsers";
+
+async function getPostgresqlPool() {
+  const signer = new DsqlSigner({
+    hostname: process.env.DSQL_ENDPOINT!,
+  });
+
+  const token = await signer.getDbConnectAdminAuthToken();
+
+  if (!token) throw new Error("Auth token error for DSQL");
+
+  const poolConfig: pg.PoolConfig = {
+    host: process.env.DSQL_ENDPOINT,
+    port: 5432,
+    user: "admin",
+    password: token,
+    ssl: true,
+    database: "postgres",
+  };
+
+  const pool = new pg.Pool(poolConfig);
+  return pool;
+}
+
+const pool = await getPostgresqlPool();
+
+const model = new ChatOpenAI();
+
+const prompt = ChatPromptTemplate.fromMessages([
+  [
+    "system",
+    "You are a helpful assistant. Answer all questions to the best of your ability.",
+  ],
+  new MessagesPlaceholder("chat_history"),
+  ["human", "{input}"],
+]);
+
+const chain = prompt.pipe(model).pipe(new StringOutputParser());
+
+const chainWithHistory = new RunnableWithMessageHistory({
+  runnable: chain,
+  inputMessagesKey: "input",
+  historyMessagesKey: "chat_history",
+  getMessageHistory: async (sessionId) => {
+    const chatHistory = new AuroraDsqlChatMessageHistory({
+      sessionId,
+      pool,
+      // Can also pass `poolConfig` to initialize the pool internally,
+      // but easier to call `.end()` at the end later.
+    });
+    return chatHistory;
+  },
+});
+
+const res1 = await chainWithHistory.invoke(
+  {
+    input: "Hi! I'm MJDeligan.",
+  },
+  { configurable: { sessionId: "langchain-test-session" } }
+);
+console.log(res1);
+/*
+  "Hello MJDeligan! It's nice to meet you. My name is AI. How may I assist you today?"
+*/
+
+const res2 = await chainWithHistory.invoke(
+  { input: "What did I just say my name was?" },
+  { configurable: { sessionId: "langchain-test-session" } }
+);
+console.log(res2);
+
+/*
+  "You said your name was MJDeligan."
+*/
+
+// If you provided a pool config you should close the created pool when you are done
+await pool.end();