We recommend when deploying Chat LangChain, you use Vercel for the frontend, LangGraph Cloud for the backend API, and GitHub action for the recurring ingestion tasks. This setup provides a simple and effective way to deploy and manage your application.
First, fork chat-langchain to your GitHub account.
We'll use Weaviate for our vector store. You can sign up for an account here.
After creating an account click "Create Cluster". Follow the steps to create a new cluster. Once finished wait for the cluster to create, this may take a few minutes.
Once your cluster has been created you should see a few sections on the page. The first is the cluster URL. Save this as your WEAVIATE_URL environment variable.
Next, click "API Keys" and save the API key in the environment variable WEAVIATE_API_KEY.
The final Weaviate environment variable is "WEAVIATE_INDEX_NAME". This is the name of the index you want to use. You can name it whatever you want, but for this example, we'll use "langchain".
After this your vector store will be setup. We can now move onto the record manager.
Visit Supabase to create an account here.
Once you've created an account, click "New project" on the dashboard page. Follow the steps, saving the database password after creating it, we'll need this later.
Once your project is setup (this also takes a few minutes), navigate to the "Settings" tab, then select "Database" under "Configuration".
Here, you should see a "Connection string" section. Copy this string, and insert your database password you saved earlier. This is your RECORD_MANAGER_DB_URL environment variable.
That's all you need to do for the record manager. The LangChain RecordManager API will handle creating tables for you.
First, build the frontend and confirm it's working locally:
cd frontend
yarn
yarn buildThen, create a Vercel account for hosting here.
Once you've created your Vercel account, navigate to your dashboard and click the button "Add New..." in the top right. This will open a dropdown. From there select "Project".
On the next screen, search for "chat-langchain" (if you did not modify the repo name when forking). Once shown, click "Import".
Finally, click "Deploy" and your frontend will be deployed!
Now, in order for your vector store to be updated with new data, you'll need to setup a recurring ingestion task (this will also populate the vector store for the first time).
Go to your forked repository, and navigate to the "Settings" tab.
Select "Environments" from the left-hand menu, and click "New environment". Enter the name "Indexing" and click "Configure environment".
When configuring, click "Add secret" and add the following secrets:
OPENAI_API_KEY=
RECORD_MANAGER_DB_URL=
WEAVIATE_API_KEY=
WEAVIATE_INDEX_NAME=langchain
WEAVIATE_URL=
These should be the same secrets as were added to Vercel.
Next, navigate to the "Actions" tab and confirm you understand your workflows, and enable them.
Then, click on the "Update index" workflow, and click "Enable workflow". Finally, click on the "Run workflow" dropdown and click "Run workflow".
Once this has finished you can visit your production URL from Vercel, and start using the app!
If you have a valid LangGraph Cloud license key, you can run a fully functional LangGraph server locally with a langgraph up command. Otherwise, you can use langgraph test to test that the API server is functional.
Note
When running langgraph test, you will only be able to create stateless runs, and the previous chats functionality will not be available.
Once you confirm that the server is working locally, you can deploy your app with LangGraph Cloud.
In Vercel add an environment variable NEXT_PUBLIC_API_BASE_URL to match your LangGraph Cloud URL as well as NEXT_PUBLIC_LANGCHAIN_API_KEY.