Integrations
π οΈ LangSmith
Integrate with Langsmith to debug and monitor your LLM app
EmbedJs now supports integration with LangSmith.
To use LangSmith, you need to do the following steps.
- Have an account on LangSmith and keep the environment variables in handy
- Set the environment variables in your app so that EmbedJs has context about it.
- Just use EmbedJs and everything will be logged to LangSmith, so that you can better test and monitor your application.
Letβs cover each step in detail.
-
First make sure that you have created a LangSmith account and have all the necessary variables handy. LangSmith has a good documentation on how to get started with their service.
-
Once you have setup the account, we will need the following environment variables
# Setting environment variable for LangChain Tracing V2 integration.
export LANGCHAIN_TRACING_V2=true
# Setting the API endpoint for LangChain.
export LANGCHAIN_ENDPOINT=https://api.smith.langchain.com
# Replace '<your-api-key>' with your LangChain API key.
export LANGCHAIN_API_KEY=<your-api-key>
# Replace '<your-project>' with your LangChain project name, or it defaults to "default".
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
- Now create an app using EmbedJs and everything will be automatically visible in LangSmith automatically.
import { RAGApplicationBuilder } from '@llm-tools/embedjs';
import { OpenAiEmbeddings } from '@llm-tools/embedjs-openai';
//Replace this with your OpenAI key
process.env.OPENAI_API_KEY = "sk-xxxx"
//Build a new application
const ragApplication = await new RAGApplicationBuilder()
.setModel(SIMPLE_MODELS.OPENAI_GPT4_O)
.setEmbeddingModel(new OpenAiEmbeddings())
.build();
//Add new documents
ragApplication.addLoader('https://www.forbes.com/profile/elon-musk')
ragApplication.addLoader('https://en.wikipedia.org/wiki/Elon_Musk')
//Query your app
await ragApplication.query('What is the net worth of Elon Musk today?')
- Now the entire log for this will be visible in langsmith.
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