Merge branch 'main' into brace/standard-tests

docs/core_docs/docs/integrations/chat/mistral.mdx

@@ -7,21 +7,16 @@ import CodeBlock from "@theme/CodeBlock";
 # ChatMistralAI
 
 [Mistral AI](https://mistral.ai/) is a research organization and hosting platform for LLMs.
-They're most known for their family of 7B models ([`mistral7b` // `mistral-tiny`](https://mistral.ai/news/announcing-mistral-7b/), [`mixtral8x7b` // `mistral-small`](https://mistral.ai/news/mixtral-of-experts/)).
-
 The LangChain implementation of Mistral's models uses their hosted generation API, making it easier to access their models without needing to run them locally.
 
-## Models
-
-Mistral's API offers access to two of their open source, and proprietary models:
+:::tip
+Want to run Mistral's models locally? Check out our [Ollama integration](/docs/integrations/chat/ollama).
+:::
 
-- `open-mistral-7b` (aka `mistral-tiny-2312`)
-- `open-mixtral-8x7b` (aka `mistral-small-2312`)
-- `mistral-small-latest` (aka `mistral-small-2402`) (default)
-- `mistral-medium-latest` (aka `mistral-medium-2312`)
-- `mistral-large-latest` (aka `mistral-large-2402`)
+## Models
 
-See [this page](https://docs.mistral.ai/guides/model-selection/) for an up to date list.
+Mistral's API offers access to two of their open source, and proprietary models.
+See [this page](https://docs.mistral.ai/getting-started/models/) for an up to date list.
 
 ## Setup
 

docs/core_docs/docs/integrations/chat/openai.mdx

@@ -125,3 +125,16 @@ You can also use the callbacks system:
 ### With `.generate()`
 
 <CodeBlock language="typescript">{OpenAIGenerationInfo}</CodeBlock>
+
+### Streaming tokens
+
+OpenAI supports streaming token counts via an opt-in call option. This can be set by passing `{ stream_options: { include_usage: true } }`.
+Setting this call option will cause the model to return an additional chunk at the end of the stream, containing the token usage.
+
+import OpenAIStreamTokens from "@examples/models/chat/integration_openai_stream_tokens.ts";
+
+<CodeBlock language="typescript">{OpenAIStreamTokens}</CodeBlock>
+
+:::tip
+See the LangSmith trace [here](https://smith.langchain.com/public/66bf7377-cc69-4676-91b6-25929a05e8b7/r)
+:::

docs/core_docs/docs/integrations/llms/mistral.ipynb

@@ -0,0 +1,160 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MistralAI\n",
+    "\n",
+    "```{=mdx}\n",
+    ":::tip\n",
+    "Want to run Mistral's models locally? Check out our [Ollama integration](/docs/integrations/chat/ollama).\n",
+    ":::\n",
+    "```\n",
+    "\n",
+    "Here's how you can initialize an `MistralAI` LLM instance:\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/mistralai\n",
+    "</Npm2Yarn>\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "console.log('hello world');\n",
+      "```\n",
+      "This will output 'hello world' to the console.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import { MistralAI } from \"@langchain/mistralai\";\n",
+    "\n",
+    "const model = new MistralAI({\n",
+    "  model: \"codestral-latest\", // Defaults to \"codestral-latest\" if no model provided.\n",
+    "  temperature: 0,\n",
+    "  apiKey: \"YOUR-API-KEY\", // In Node.js defaults to process.env.MISTRAL_API_KEY\n",
+    "});\n",
+    "const res = await model.invoke(\n",
+    "  \"You can print 'hello world' to the console in javascript like this:\\n```javascript\"\n",
+    ");\n",
+    "console.log(res);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the Mistral LLM is a completions model, they also allow you to insert a `suffix` to the prompt. Suffixes can be passed via the call options when invoking a model like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "console.log('hello world');\n",
+      "```\n"
+     ]
+    }
+   ],
+   "source": [
+    "const res = await model.invoke(\n",
+    "  \"You can print 'hello world' to the console in javascript like this:\\n```javascript\", {\n",
+    "    suffix: \"```\"\n",
+    "  }\n",
+    ");\n",
+    "console.log(res);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As seen in the first example, the model generated the requested `console.log('hello world')` code snippet, but also included extra unwanted text. By adding a suffix, we can constrain the model to only complete the prompt up to the suffix (in this case, three backticks). This allows us to easily parse the completion and extract only the desired response without the suffix using a custom output parser."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "console.log('hello world');\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import { MistralAI } from \"@langchain/mistralai\";\n",
+    "\n",
+    "const model = new MistralAI({\n",
+    "  model: \"codestral-latest\",\n",
+    "  temperature: 0,\n",
+    "  apiKey: \"YOUR-API-KEY\",\n",
+    "});\n",
+    "\n",
+    "const suffix = \"```\";\n",
+    "\n",
+    "const customOutputParser = (input: string) => {\n",
+    "  if (input.includes(suffix)) {\n",
+    "    return input.split(suffix)[0];\n",
+    "  }\n",
+    "  throw new Error(\"Input does not contain suffix.\")\n",
+    "};\n",
+    "\n",
+    "const res = await model.invoke(\n",
+    "  \"You can print 'hello world' to the console in javascript like this:\\n```javascript\", {\n",
+    "    suffix,\n",
+    "  }\n",
+    ");\n",
+    "\n",
+    "console.log(customOutputParser(res));"
+   ]
+  }
+ ],
+ "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": 2
+}

examples/src/models/chat/chat_mistralai_tools.ts

@@ -33,7 +33,7 @@ class CalculatorTool extends StructuredTool {
 
 const model = new ChatMistralAI({
   apiKey: process.env.MISTRAL_API_KEY,
-  model: "mistral-large",
+  model: "mistral-large-latest",
 });
 
 // Bind the tool to the model

examples/src/models/chat/chat_mistralai_wsa.ts

@@ -14,7 +14,7 @@ const calculatorSchema = z
 
 const model = new ChatMistralAI({
   apiKey: process.env.MISTRAL_API_KEY,
-  model: "mistral-large",
+  model: "mistral-large-latest",
 });
 
 // Pass the schema and tool name to the withStructuredOutput method

examples/src/models/chat/chat_mistralai_wsa_json.ts

@@ -21,7 +21,7 @@ const calculatorJsonSchema = {
 
 const model = new ChatMistralAI({
   apiKey: process.env.MISTRAL_API_KEY,
-  model: "mistral-large",
+  model: "mistral-large-latest",
 });
 
 // Pass the schema and tool name to the withStructuredOutput method

examples/src/models/chat/integration_openai_stream_tokens.ts

@@ -0,0 +1,30 @@
+import { AIMessageChunk } from "@langchain/core/messages";
+import { ChatOpenAI } from "@langchain/openai";
+
+// Instantiate the model
+const model = new ChatOpenAI();
+
+const response = await model.stream("Hello, how are you?", {
+  // Pass the stream options
+  stream_options: {
+    include_usage: true,
+  },
+});
+
+// Iterate over the response, only saving the last chunk
+let finalResult: AIMessageChunk | undefined;
+for await (const chunk of response) {
+  if (finalResult) {
+    finalResult = finalResult.concat(chunk);
+  } else {
+    finalResult = chunk;
+  }
+}
+
+console.log(finalResult?.usage_metadata);
+
+/*
+
+{ input_tokens: 13, output_tokens: 30, total_tokens: 43 }
+
+*/

langchain-core/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/core",
-  "version": "0.2.4",
+  "version": "0.2.5",
   "description": "Core LangChain.js abstractions and schemas",
   "type": "module",
   "engines": {

langchain-core/src/messages/ai.ts

@@ -18,6 +18,25 @@ import {
 export type AIMessageFields = BaseMessageFields & {
   tool_calls?: ToolCall[];
   invalid_tool_calls?: InvalidToolCall[];
+  usage_metadata?: UsageMetadata;
+};
+
+/**
+ * Usage metadata for a message, such as token counts.
+ */
+export type UsageMetadata = {
+  /**
+   * The count of input (or prompt) tokens.
+   */
+  input_tokens: number;
+  /**
+   * The count of output (or completion) tokens
+   */
+  output_tokens: number;
+  /**
+   * The total token count
+   */
+  total_tokens: number;
 };
 
 /**
@@ -30,6 +49,11 @@ export class AIMessage extends BaseMessage {
 
   invalid_tool_calls?: InvalidToolCall[] = [];
 
+  /**
+   * If provided, token usage information associated with the message.
+   */
+  usage_metadata?: UsageMetadata;
+
   get lc_aliases(): Record<string, string> {
     // exclude snake case conversion to pascal case
     return {
@@ -94,6 +118,7 @@ export class AIMessage extends BaseMessage {
       this.invalid_tool_calls =
         initParams.invalid_tool_calls ?? this.invalid_tool_calls;
     }
+    this.usage_metadata = initParams.usage_metadata;
   }
 
   static lc_name() {
@@ -127,6 +152,11 @@ export class AIMessageChunk extends BaseMessageChunk {
 
   tool_call_chunks?: ToolCallChunk[] = [];
 
+  /**
+   * If provided, token usage information associated with the message.
+   */
+  usage_metadata?: UsageMetadata;
+
   constructor(fields: string | AIMessageChunkFields) {
     let initParams: AIMessageChunkFields;
     if (typeof fields === "string") {
@@ -177,10 +207,11 @@ export class AIMessageChunk extends BaseMessageChunk {
     // properties with initializers, so we have to check types twice.
     super(initParams);
     this.tool_call_chunks =
-      initParams?.tool_call_chunks ?? this.tool_call_chunks;
-    this.tool_calls = initParams?.tool_calls ?? this.tool_calls;
+      initParams.tool_call_chunks ?? this.tool_call_chunks;
+    this.tool_calls = initParams.tool_calls ?? this.tool_calls;
     this.invalid_tool_calls =
-      initParams?.invalid_tool_calls ?? this.invalid_tool_calls;
+      initParams.invalid_tool_calls ?? this.invalid_tool_calls;
+    this.usage_metadata = initParams.usage_metadata;
   }
 
   get lc_aliases(): Record<string, string> {
@@ -226,6 +257,27 @@ export class AIMessageChunk extends BaseMessageChunk {
         combinedFields.tool_call_chunks = rawToolCalls;
       }
     }
+    if (
+      this.usage_metadata !== undefined ||
+      chunk.usage_metadata !== undefined
+    ) {
+      const left: UsageMetadata = this.usage_metadata ?? {
+        input_tokens: 0,
+        output_tokens: 0,
+        total_tokens: 0,
+      };
+      const right: UsageMetadata = chunk.usage_metadata ?? {
+        input_tokens: 0,
+        output_tokens: 0,
+        total_tokens: 0,
+      };
+      const usage_metadata: UsageMetadata = {
+        input_tokens: left.input_tokens + right.input_tokens,
+        output_tokens: left.output_tokens + right.output_tokens,
+        total_tokens: left.total_tokens + right.total_tokens,
+      };
+      combinedFields.usage_metadata = usage_metadata;
+    }
     return new AIMessageChunk(combinedFields);
   }
 }