feature: chat deployment implementation

docs/core_docs/docs/how_to/tool_calling_parallel.ipynb

@@ -56,7 +56,7 @@
         "});\n",
         "\n",
         "const multiplyTool = tool(async ({ a, b }) => {\n",
-        "  return a + b;\n",
+        "  return a * b;\n",
         "}, {\n",
         "  name: \"multiply\",\n",
         "  description: \"Multiplies a and b\",\n",
@@ -220,4 +220,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 4
-}
+}

docs/core_docs/docs/how_to/tool_results_pass_to_model.ipynb

@@ -18,7 +18,7 @@
         ":::\n",
         "```\n",
         "\n",
-        "Some models are capable of [**tool calling**](/docs/concepts/tool_calling) - generating arguments that conform to a specific user-provided schema. This guide will demonstrate how to use those tool cals to actually call a function and properly pass the results back to the model.\n",
+        "Some models are capable of [**tool calling**](/docs/concepts/tool_calling) - generating arguments that conform to a specific user-provided schema. This guide will demonstrate how to use those tool calls to actually call a function and properly pass the results back to the model.\n",
         "\n",
         "![](../../static/img/tool_invocation.png)\n",
         "\n",
@@ -367,4 +367,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 4
-}
+}

docs/core_docs/docs/integrations/tools/google_calendar.mdx

@@ -25,7 +25,7 @@ import IntegrationInstallTooltip from "@mdx_components/integration_install_toolt
 <IntegrationInstallTooltip></IntegrationInstallTooltip>
 
 ```bash npm2yarn
-npm install @langchain/openai @langchain/core
+npm install @langchain/openai @langchain/core @langchain/community @langchain/langgraph
 ```
 
 <CodeBlock language="typescript">{ToolExample}</CodeBlock>

docs/core_docs/src/theme/VectorStoreTabs.js

@@ -54,7 +54,7 @@ const ${vectorStoreVarName} = new MongoDBAtlasVectorSearch(embeddings, {
     {
       value: "PGVector",
       label: "PGVector",
-      text: `import PGVectorStore from "@langchain/community/vectorstores/pgvector";
+      text: `import { PGVectorStore } from "@langchain/community/vectorstores/pgvector";
 
 const ${vectorStoreVarName} = await PGVectorStore.initialize(embeddings, {})`,
       dependencies: "@langchain/community",

examples/package.json

@@ -93,7 +93,7 @@
     "ioredis": "^5.3.2",
     "js-yaml": "^4.1.0",
     "langchain": "workspace:*",
-    "langsmith": "^0.2.8",
+    "langsmith": ">=0.2.8 <0.4.0",
     "mongodb": "^6.3.0",
     "pg": "^8.11.0",
     "pickleparser": "^0.2.1",

examples/src/models/chat/integration_anthropic_pdf.ts

@@ -0,0 +1,52 @@
+import { ChatAnthropic } from "@langchain/anthropic";
+
+import * as fs from "fs";
+
+export const run = async () => {
+  const llm = new ChatAnthropic({
+    model: "claude-3-5-sonnet-20240620", // Only claude-3-5-sonnet-20240620 , claude-3-5-sonnet-20241022 as of Jan 2025 support PDF documents as in base64
+  });
+
+  // PDF needs to be in Base64.
+  const getLocalFile = async (path: string) => {
+    const localFile = await fs.readFileSync(path);
+    const base64File = localFile.toString("base64");
+    return base64File;
+  };
+
+  // Or remotely
+  const getRemoteFile = async (url: string) => {
+    const response = await fetch(url);
+    const arrayBuffer = await response.arrayBuffer();
+    const base64File = Buffer.from(arrayBuffer).toString("base64");
+    return base64File;
+  };
+
+  const base64 = await getRemoteFile(
+    "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
+  );
+
+  const prompt = "Summarise the contents of this PDF";
+
+  const response = await llm.invoke([
+    {
+      role: "user",
+      content: [
+        {
+          type: "text",
+          text: prompt,
+        },
+        {
+          type: "document",
+          source: {
+            media_type: "application/pdf",
+            type: "base64",
+            data: base64,
+          },
+        },
+      ],
+    },
+  ]);
+  console.log(response.content);
+  return response.content;
+};

examples/src/tools/google_calendar.ts

@@ -1,15 +1,16 @@
-import { initializeAgentExecutorWithOptions } from "langchain/agents";
-import { OpenAI } from "@langchain/openai";
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+import { ChatOpenAI } from "@langchain/openai";
 import { Calculator } from "@langchain/community/tools/calculator";
 import {
   GoogleCalendarCreateTool,
   GoogleCalendarViewTool,
 } from "@langchain/community/tools/google_calendar";
 
 export async function run() {
-  const model = new OpenAI({
+  const model = new ChatOpenAI({
     temperature: 0,
     apiKey: process.env.OPENAI_API_KEY,
+    model: "gpt-4o-mini",
   });
 
   const googleCalendarParams = {
@@ -31,22 +32,26 @@ export async function run() {
     new GoogleCalendarViewTool(googleCalendarParams),
   ];
 
-  const calendarAgent = await initializeAgentExecutorWithOptions(tools, model, {
-    agentType: "zero-shot-react-description",
-    verbose: true,
+  const calendarAgent = createReactAgent({
+    llm: model,
+    tools,
   });
 
   const createInput = `Create a meeting with John Doe next Friday at 4pm - adding to the agenda of it the result of 99 + 99`;
 
-  const createResult = await calendarAgent.invoke({ input: createInput });
+  const createResult = await calendarAgent.invoke({
+    messages: [{ role: "user", content: createInput }],
+  });
   //   Create Result {
   //     output: 'A meeting with John Doe on 29th September at 4pm has been created and the result of 99 + 99 has been added to the agenda.'
   //   }
   console.log("Create Result", createResult);
 
   const viewInput = `What meetings do I have this week?`;
 
-  const viewResult = await calendarAgent.invoke({ input: viewInput });
+  const viewResult = await calendarAgent.invoke({
+    messages: [{ role: "user", content: viewInput }],
+  });
   //   View Result {
   //     output: "You have no meetings this week between 8am and 8pm."
   //   }

langchain-core/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/core",
-  "version": "0.3.30",
+  "version": "0.3.32",
   "description": "Core LangChain.js abstractions and schemas",
   "type": "module",
   "engines": {
@@ -38,7 +38,7 @@
     "camelcase": "6",
     "decamelize": "1.2.0",
     "js-tiktoken": "^1.0.12",
-    "langsmith": "^0.2.8",
+    "langsmith": ">=0.2.8 <0.4.0",
     "mustache": "^4.2.0",
     "p-queue": "^6.6.2",
     "p-retry": "4",

langchain-core/src/messages/tests/message_utils.test.ts

@@ -4,10 +4,11 @@ import {
   mergeMessageRuns,
   trimMessages,
 } from "../transformers.js";
-import { AIMessage } from "../ai.js";
+import { AIMessage, AIMessageChunk } from "../ai.js";
 import { ChatMessage } from "../chat.js";
 import { HumanMessage } from "../human.js";
 import { SystemMessage } from "../system.js";
+import { ToolMessage } from "../tool.js";
 import { BaseMessage } from "../base.js";
 import {
   getBufferString,
@@ -187,6 +188,7 @@ describe("trimMessages can trim", () => {
             defaultMsgSuffixLen;
         }
       }
+      console.log(count);
       return count;
     };
 
@@ -196,6 +198,84 @@ describe("trimMessages can trim", () => {
     };
   };
 
+  it("should not mutate messages if no trimming occurs with strategy last", async () => {
+    const trimmer = trimMessages({
+      maxTokens: 128000,
+      strategy: "last",
+      startOn: [HumanMessage],
+      endOn: [AIMessage, ToolMessage],
+      tokenCounter: () => 1,
+    });
+    const messages = [
+      new HumanMessage({
+        content: "Fetch the last 5 emails from Flora Testington's inbox.",
+        additional_kwargs: {},
+        response_metadata: {},
+      }),
+      new AIMessageChunk({
+        id: "chatcmpl-abcdefg",
+        content: "",
+        additional_kwargs: {
+          tool_calls: [
+            {
+              function: {
+                name: "getEmails",
+                arguments: JSON.stringify({
+                  inboxName: "flora@foo.org",
+                  amount: 5,
+                  folder: "Inbox",
+                  searchString: null,
+                  from: null,
+                  subject: null,
+                }),
+              },
+              id: "foobarbaz",
+              index: 0,
+              type: "function",
+            },
+          ],
+        },
+        response_metadata: {
+          usage: {},
+        },
+        tool_calls: [
+          {
+            name: "getEmails",
+            args: {
+              inboxName: "flora@foo.org",
+              amount: 5,
+              folder: "Inbox",
+              searchString: null,
+              from: null,
+              subject: null,
+            },
+            id: "foobarbaz",
+            type: "tool_call",
+          },
+        ],
+        tool_call_chunks: [
+          {
+            name: "getEmails",
+            args: '{"inboxName":"flora@foo.org","amount":5,"folder":"Inbox","searchString":null,"from":null,"subject":null,"cc":[],"bcc":[]}',
+            id: "foobarbaz",
+            index: 0,
+            type: "tool_call_chunk",
+          },
+        ],
+        invalid_tool_calls: [],
+      }),
+      new ToolMessage({
+        content: "a whole bunch of emails!",
+        name: "getEmails",
+        additional_kwargs: {},
+        response_metadata: {},
+        tool_call_id: "foobarbaz",
+      }),
+    ];
+    const trimmedMessages = await trimmer.invoke(messages);
+    expect(trimmedMessages).toEqual(messages);
+  });
+
   it("First 30 tokens, not allowing partial messages", async () => {
     const { messages, dummyTokenCounter } = messagesAndTokenCounterFactory();
     const trimmedMessages = await trimMessages(messages, {
@@ -319,6 +399,7 @@ describe("trimMessages can trim", () => {
 
   it("Last 30 tokens, including system message, allowing partial messages, end on HumanMessage", async () => {
     const { messages, dummyTokenCounter } = messagesAndTokenCounterFactory();
+    console.log(messages);
     const trimmedMessages = await trimMessages(messages, {
       maxTokens: 30,
       tokenCounter: dummyTokenCounter,

langchain-core/src/messages/transformers.ts

@@ -274,7 +274,7 @@ function _mergeMessageRuns(messages: BaseMessage[]): BaseMessage[] {
   }
   const merged: BaseMessage[] = [];
   for (const msg of messages) {
-    const curr = msg; // Create a shallow copy of the message
+    const curr = msg;
     const last = merged.pop();
     if (!last) {
       merged.push(curr);
@@ -861,20 +861,24 @@ async function _lastMaxTokens(
     ...rest
   } = options;
 
+  // Create a copy of messages to avoid mutation
+  let messagesCopy = [...messages];
+
   if (endOn) {
     const endOnArr = Array.isArray(endOn) ? endOn : [endOn];
     while (
-      messages &&
-      !_isMessageType(messages[messages.length - 1], endOnArr)
+      messagesCopy.length > 0 &&
+      !_isMessageType(messagesCopy[messagesCopy.length - 1], endOnArr)
     ) {
-      messages.pop();
+      messagesCopy = messagesCopy.slice(0, -1);
     }
   }
 
-  const swappedSystem = includeSystem && messages[0]._getType() === "system";
+  const swappedSystem =
+    includeSystem && messagesCopy[0]?._getType() === "system";
   let reversed_ = swappedSystem
-    ? messages.slice(0, 1).concat(messages.slice(1).reverse())
-    : messages.reverse();
+    ? messagesCopy.slice(0, 1).concat(messagesCopy.slice(1).reverse())
+    : messagesCopy.reverse();
 
   reversed_ = await _firstMaxTokens(reversed_, {
     ...rest,

langchain/package.json

@@ -1,6 +1,6 @@
 {
   "name": "langchain",
-  "version": "0.3.11",
+  "version": "0.3.12",
   "description": "Typescript bindings for langchain",
   "type": "module",
   "engines": {
@@ -530,7 +530,7 @@
     "js-tiktoken": "^1.0.12",
     "js-yaml": "^4.1.0",
     "jsonpointer": "^5.0.1",
-    "langsmith": "^0.2.8",
+    "langsmith": ">=0.2.8 <0.4.0",
     "openapi-types": "^12.1.3",
     "p-retry": "4",
     "uuid": "^10.0.0",

langchain/src/chains/openai_moderation.ts

@@ -27,7 +27,7 @@ export interface OpenAIModerationChainInput
  * OpenAIModerationChainInput interface.
  * @example
  * ```typescript
- * const moderation = new ChatOpenAIModerationChain({ throwError: true });
+ * const moderation = new OpenAIModerationChain({ throwError: true });
  *
  * const badString = "Bad naughty words from user";
  *

libs/langchain-anthropic/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/anthropic",
-  "version": "0.3.11",
+  "version": "0.3.12",
   "description": "Anthropic integrations for LangChain.js",
   "type": "module",
   "engines": {

libs/langchain-anthropic/src/utils/message_inputs.ts

@@ -131,6 +131,13 @@ function _formatContent(content: MessageContent) {
           source,
           ...(cacheControl ? { cache_control: cacheControl } : {}),
         };
+      } else if (contentPart.type === "document") {
+        // PDF
+        return {
+          type: "document",
+          source: contentPart.source,
+          ...(cacheControl ? { cache_control: cacheControl } : {}),
+        };
       } else if (
         textTypes.find((t) => t === contentPart.type) &&
         "text" in contentPart

libs/langchain-azure-cosmosdb/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/azure-cosmosdb",
-  "version": "0.2.6",
+  "version": "0.2.7",
   "description": "Azure CosmosDB integration for LangChain.js",
   "type": "module",
   "engines": {