yarn add langsmith

yarn add langchain

process.env.LANGSMITH_TRACING = "true";
process.env.LANGSMITH_ENDPOINT = "https://api.smith.langchain.com";
// process.env.LANGSMITH_ENDPOINT = "https://eu.api.smith.langchain.com"; // If signed up in the EU region
process.env.LANGSMITH_API_KEY = "<YOUR-LANGSMITH-API-KEY>";
// process.env.LANGSMITH_PROJECT = "My Project Name"; // Optional: "default" is used if not set
// process.env.LANGSMITH_WORKSPACE_ID = "<YOUR-WORKSPACE-ID>"; // Required for org-scoped API keys

import { ChatOpenAI } from "langchain/chat_models/openai";

const chat = new ChatOpenAI({ temperature: 0 });
const response = await chat.predict(
  "Translate this sentence from English to French. I love programming."
);
console.log(response);

export LANGSMITH_TRACING="true";
export LANGSMITH_API_KEY=<YOUR-LANGSMITH-API-KEY>
# export LANGSMITH_PROJECT="My Project Name" #  Optional: "default" is used if not set
# export LANGSMITH_ENDPOINT=https://api.smith.langchain.com # or your own server

export LANGSMITH_TRACING="true";
export LANGSMITH_API_KEY=<your-api-key>

npm install langsmith openai

import { OpenAI } from "openai";
import { wrapOpenAI } from "langsmith/wrappers";

const openai = wrapOpenAI(new OpenAI());

await openai.chat.completions.create({
  model: "gpt-3.5-turbo",
  messages: [{ content: "Hi there!", role: "user" }],
});

import { traceable } from "langsmith/traceable";

const openai = new OpenAI();

const createCompletion = traceable(
  openai.chat.completions.create.bind(openai.chat.completions),
  { name: "OpenAI Chat Completion", run_type: "llm" }
);

await createCompletion({
  model: "gpt-3.5-turbo",
  messages: [{ content: "Hi there!", role: "user" }],
});

const nestedTrace = traceable(async (text: string) => {
  const completion = await openai.chat.completions.create({
    model: "gpt-3.5-turbo",
    messages: [{ content: text, role: "user" }],
  });
  return completion;
});

await nestedTrace("Why is the sky blue?");

{
  "id": "chatcmpl-8sPToJQLLVepJvyeTfzZMOMVIKjMo",
  "object": "chat.completion",
  "created": 1707978348,
  "model": "gpt-3.5-turbo-0613",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The sky appears blue because of a phenomenon known as Rayleigh scattering. The Earth's atmosphere is composed of tiny molecules, such as nitrogen and oxygen, which are much smaller than the wavelength of visible light. When sunlight interacts with these molecules, it gets scattered in all directions. However, shorter wavelengths of light (blue and violet) are scattered more compared to longer wavelengths (red, orange, and yellow). \n\nAs a result, when sunlight passes through the Earth's atmosphere, the blue and violet wavelengths are scattered in all directions, making the sky appear blue. This scattering of shorter wavelengths is also responsible for the vibrant colors observed during sunrise and sunset, when the sunlight has to pass through a thicker portion of the atmosphere, causing the longer wavelengths to dominate the scattered light."
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 13,
    "completion_tokens": 154,
    "total_tokens": 167
  },
  "system_fingerprint": null
}

import { NextRequest, NextResponse } from "next/server";

import { OpenAI } from "openai";
import { traceable } from "langsmith/traceable";
import { wrapOpenAI } from "langsmith/wrappers";

export const runtime = "edge";

const handler = traceable(
  async function () {
    const openai = wrapOpenAI(new OpenAI());

    const completion = await openai.chat.completions.create({
      model: "gpt-3.5-turbo",
      messages: [{ content: "Why is the sky blue?", role: "user" }],
    });

    const response1 = completion.choices[0].message.content;

    const completion2 = await openai.chat.completions.create({
      model: "gpt-3.5-turbo",
      messages: [
        { content: "Why is the sky blue?", role: "user" },
        { content: response1, role: "assistant" },
        { content: "Cool thank you!", role: "user" },
      ],
    });

    const response2 = completion2.choices[0].message.content;

    return {
      text: response2,
    };
  },
  {
    name: "Simple Next.js handler",
  }
);

export async function POST(req: NextRequest) {
  const result = await handler();
  return NextResponse.json(result);
}

import { traceable } from "langsmith/traceable";
import { OpenAIStream, StreamingTextResponse } from "ai";

// Note: There are no types for the Mistral API client yet.
import MistralClient from "@mistralai/mistralai";

const client = new MistralClient(process.env.MISTRAL_API_KEY || "");

export async function POST(req: Request) {
  // Extract the `messages` from the body of the request
  const { messages } = await req.json();

  const mistralChatStream = traceable(client.chatStream.bind(client), {
    name: "Mistral Stream",
    run_type: "llm",
  });

  const response = await mistralChatStream({
    model: "mistral-tiny",
    maxTokens: 1000,
    messages,
  });

  // Convert the response into a friendly text-stream. The Mistral client responses are
  // compatible with the Vercel AI SDK OpenAIStream adapter.
  const stream = OpenAIStream(response as any);

  // Respond with the stream
  return new StreamingTextResponse(stream);
}

import { wrapSDK } from "langsmith/wrappers";
import { Anthropic } from "@anthropic-ai/sdk";

const originalSDK = new Anthropic();
const sdkWithTracing = wrapSDK(originalSDK);

const response = await sdkWithTracing.messages.create({
  messages: [
    {
      role: "user",
      content: `What is 1 + 1? Respond only with "2" and nothing else.`,
    },
  ],
  model: "claude-3-sonnet-20240229",
  max_tokens: 1024,
});

import { RunTree, RunTreeConfig } from "langsmith";

const parentRunConfig: RunTreeConfig = {
  name: "My Chat Bot",
  run_type: "chain",
  inputs: {
    text: "Summarize this morning's meetings.",
  },
  serialized: {}, // Serialized representation of this chain
  // project_name: "Defaults to the LANGSMITH_PROJECT env var"
  // apiUrl: "Defaults to the LANGSMITH_ENDPOINT env var"
  // apiKey: "Defaults to the LANGSMITH_API_KEY env var"
};

const parentRun = new RunTree(parentRunConfig);

await parentRun.postRun();

const childLlmRun = await parentRun.createChild({
  name: "My Proprietary LLM",
  run_type: "llm",
  inputs: {
    prompts: [
      "You are an AI Assistant. The time is XYZ." +
        " Summarize this morning's meetings.",
    ],
  },
});

await childLlmRun.postRun();

await childLlmRun.end({
  outputs: {
    generations: [
      "I should use the transcript_loader tool" +
        " to fetch meeting_transcripts from XYZ",
    ],
  },
});

await childLlmRun.patchRun();

const childToolRun = await parentRun.createChild({
  name: "transcript_loader",
  run_type: "tool",
  inputs: {
    date: "XYZ",
    content_type: "meeting_transcripts",
  },
});
await childToolRun.postRun();

await childToolRun.end({
  outputs: {
    meetings: ["Meeting1 notes.."],
  },
});

await childToolRun.patchRun();

const childChainRun = await parentRun.createChild({
  name: "Unreliable Component",
  run_type: "tool",
  inputs: {
    input: "Summarize these notes...",
  },
});

await childChainRun.postRun();

try {
  // .... the component does work
  throw new Error("Something went wrong");
} catch (e) {
  await childChainRun.end({
    error: `I errored again ${e.message}`,
  });
  await childChainRun.patchRun();
  throw e;
}

await childChainRun.patchRun();

await parentRun.end({
  outputs: {
    output: ["The meeting notes are as follows:..."],
  },
});

// False directs to not exclude child runs
await parentRun.patchRun();

import { Client } from "langsmith/client";
const client = new Client({
  // apiUrl: "https://api.langchain.com", // Defaults to the LANGSMITH_ENDPOINT env var
  // apiKey: "my_api_key", // Defaults to the LANGSMITH_API_KEY env var
  /* callerOptions: {
         maxConcurrency?: Infinity; // Maximum number of concurrent requests to make
         maxRetries?: 6; // Maximum number of retries to make
    */
});
const datasetName = "Example Dataset";
// We will only use examples from the top level AgentExecutor run here,
// and exclude runs that errored.
const runs = await client.listRuns({
  projectName: "my_project",
  executionOrder: 1,
  error: false,
});

const dataset = await client.createDataset(datasetName, {
  description: "An example dataset",
});

for (const run of runs) {
  await client.createExample(run.inputs, run.outputs ?? {}, {
    datasetId: dataset.id,
  });
}