🦜️🔗 LangChain + Next.js Starter Template
The LangChain + Next.js Starter Template is an engaging project that marries the power of LangChain.js with the flexibility of Next.js, offering a robust foundation for developers to build applications utilizing modern AI-based solutions. This template takes developers through various use cases by showcasing how to leverage and combine LangChain modules effectively.
Features
This template presents five exciting practical examples:
- Simple Chat: A basic setup that demonstrates how to manage a straightforward conversation with AI.
- Structured Output: A unique feature where the response from a language model (LLM) is formatted according to a defined schema.
- Complex Questions with Agents: Strategically answering layered and multi-step questions using agentic workflows.
- Retrieval Augmented Generation (RAG): Enhancing content retrieval, whether with a straightforward chain or combined with an agent, both employing a vector store.
Most of these examples utilize Vercel’s AI SDK to efficiently stream responses to the client, enhancing real-time interactions.
Getting Started
To kick off with the LangChain + Next.js Template, one should clone the repository and set up the environment variables in an .env.local file by copying from an existing .env.example. The primary requirement is the OpenAI API key, crucial for initiating simple tasks.
The application is primarily designed for serverless Edge functions, which may necessitate configuring LANGCHAIN_CALLBACKS_BACKGROUND to false for effective tracing using LangSmith.
The next step involves installing necessary packages and running the development server with the command:
yarn dev
This will launch the application locally, allowing users to engage with the AI through their browser by simply visiting http://localhost:3000. The setup allows real-time modifications, where changes in code reflect instantaneously on the browser side.
Detailed Examples
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Structured Output: Through a detailed example of structured output, users can see how responses are structured using OpenAI Functions. The methodology relies on the Zod library to construct schemas, forcing the LLM to output in a designated format.
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Agents: To explore more complex questions through agents, setting up the
SERPAPI_API_KEYis a prerequisite. This feature leverages prebuilt LangGraph agents, providing flexibility for customization based on user needs. -
Retrieval with Vector Stores: Demonstrations around retrieval make use of Supabase as a vector store. This setup allows fetching and processing of data efficiently, though other vector stores can be integrated as per the developer’s choice. Users are guided on establishing connections, uploading text data, and querying through pre-defined processes.
Technical Insights
LangChain’s bundle size is intentionally minimized to fit within Vercel's free-tier limits, ensuring an optimized deployment experience. Users can dive deeper into the bundle’s makeup with the analysis tool, providing insights into the codebase efficiency.
Learning and Deploying
For users aiming to expand their knowledge, comprehensive documentation on LangChain.js is available to explore various functionalities—from expression languages to configuration of intricate chains.
Once satisfied with the project's development, deploying on Vercel is streamlined, offering an easy transition from local development to live deployment.
Conclusion
The LangChain + Next.js Starter Template represents a laid-back yet potent approach to building AI-driven applications. By presenting clear examples and streamlined processes, it allows developers to seamlessly incorporate AI solutions into modern web applications while learning through hands-on examples.