base on LangChain + Next.js starter template # 🦜️🔗 LangChain + Next.js Starter Template [](https://codespaces.new/langchain-ai/langchain-nextjs-template) [](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Flangchain-ai%2Flangchain-nextjs-template) This template scaffolds a LangChain.js + Next.js starter app. It showcases how to use and combine LangChain modules for several use cases. Specifically: - [Simple chat](/app/api/chat/route.ts) - [Returning structured output from an LLM call](/app/api/chat/structured_output/route.ts) - [Answering complex, multi-step questions with agents](/app/api/chat/agents/route.ts) - [Retrieval augmented generation (RAG) with a chain and a vector store](/app/api/chat/retrieval/route.ts) - [Retrieval augmented generation (RAG) with an agent and a vector store](/app/api/chat/retrieval_agents/route.ts) Most of them use Vercel's [AI SDK](https://github.com/vercel-labs/ai) to stream tokens to the client and display the incoming messages. The agents use [LangGraph.js](https://langchain-ai.github.io/langgraphjs/), LangChain's framework for building agentic workflows. They use preconfigured helper functions to minimize boilerplate, but you can replace them with custom graphs as desired.  It's free-tier friendly too! Check out the [bundle size stats below](#-bundle-size). You can check out a hosted version of this repo here: https://langchain-nextjs-template.vercel.app/ ## 🚀 Getting Started First, clone this repo and download it locally. Next, you'll need to set up environment variables in your repo's `.env.local` file. Copy the `.env.example` file to `.env.local`. To start with the basic examples, you'll just need to add your OpenAI API key. Because this app is made to run in serverless Edge functions, make sure you've set the `LANGCHAIN_CALLBACKS_BACKGROUND` environment variable to `false` to ensure tracing finishes if you are using [LangSmith tracing](https://docs.smith.langchain.com/). Next, install the required packages using your preferred package manager (e.g. `yarn`). Now you're ready to run the development server: ```bash yarn dev ``` Open [http://localhost:3000](http://localhost:3000) with your browser to see the result! Ask the bot something and you'll see a streamed response:  You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file. Backend logic lives in `app/api/chat/route.ts`. From here, you can change the prompt and model, or add other modules and logic. ## 🧱 Structured Output The second example shows how to have a model return output according to a specific schema using OpenAI Functions. Click the `Structured Output` link in the navbar to try it out:  The chain in this example uses a [popular library called Zod](https://zod.dev) to construct a schema, then formats it in the way OpenAI expects. It then passes that schema as a function into OpenAI and passes a `function_call` parameter to force OpenAI to return arguments in the specified format. For more details, [check out this documentation page](https://js.langchain.com/docs/how_to/structured_output). ## 🦜 Agents To try out the agent example, you'll need to give the agent access to the internet by populating the `SERPAPI_API_KEY` in `.env.local`. Head over to [the SERP API website](https://serpapi.com/) and get an API key if you don't already have one. You can then click the `Agent` example and try asking it more complex questions:  This example uses a [prebuilt LangGraph agent](https://langchain-ai.github.io/langgraphjs/tutorials/quickstart/), but you can customize your own as well. ## 🐶 Retrieval The retrieval examples both use Supabase as a vector store. However, you can swap in [another supported vector store](https://js.langchain.com/docs/integrations/vectorstores) if preferred by changing the code under `app/api/retrieval/ingest/route.ts`, `app/api/chat/retrieval/route.ts`, and `app/api/chat/retrieval_agents/route.ts`. For Supabase, follow [these instructions](https://js.langchain.com/docs/integrations/vectorstores/supabase) to set up your database, then get your database URL and private key and paste them into `.env.local`. You can then switch to the `Retrieval` and `Retrieval Agent` examples. The default document text is pulled from the LangChain.js retrieval use case docs, but you can change them to whatever text you'd like. For a given text, you'll only need to press `Upload` once. Pressing it again will re-ingest the docs, resulting in duplicates. You can clear your Supabase vector store by navigating to the console and running `DELETE FROM documents;`. After splitting, embedding, and uploading some text, you're ready to ask questions!   For more info on retrieval chains, [see this page](https://js.langchain.com/docs/tutorials/rag). The specific variant of the conversational retrieval chain used here is composed using LangChain Expression Language, which you can [read more about here](https://js.langchain.com/docs/how_to/qa_sources/). This chain example will also return cited sources via header in addition to the streaming response. For more info on retrieval agents, [see this page](https://langchain-ai.github.io/langgraphjs/tutorials/rag/langgraph_agentic_rag/). ## 📦 Bundle size The bundle size for LangChain itself is quite small. After compression and chunk splitting, for the RAG use case LangChain uses 37.32 KB of code space (as of [@langchain/core 0.1.15](https://npmjs.com/package/@langchain/core)), which is less than 4% of the total Vercel free tier edge function alottment of 1 MB:  This package has [@next/bundle-analyzer](https://www.npmjs.com/package/@next/bundle-analyzer) set up by default - you can explore the bundle size interactively by running: ```bash $ ANALYZE=true yarn build ``` ## 📚 Learn More The example chains in the `app/api/chat/route.ts` and `app/api/chat/retrieval/route.ts` files use [LangChain Expression Language](https://js.langchain.com/docs/concepts#langchain-expression-language) to compose different LangChain.js modules together. You can integrate other retrievers, agents, preconfigured chains, and more too, though keep in mind `HttpResponseOutputParser` is meant to be used directly with model output. To learn more about what you can do with LangChain.js, check out the docs here: - https://js.langchain.com/docs/ ## ▲ Deploy on Vercel When ready, you can deploy your app on the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme). Check out the [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details. ## Thank You! Thanks for reading! If you have any questions or comments, reach out to us on Twitter [@LangChainAI](https://twitter.com/langchainai), or [click here to join our Discord server](https://discord.gg/langchain). ", Assign "at most 3 tags" to the expected json: {"id":"1818","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"