Conversational search with Typesense + Next.js App router

This demo showcases Typesense's conversational search features.

The dataset contains essays authored by Paul Graham, indexed in Typesense.

Questions are sent directly to Typesense, which has a built-in RAG pipeline to return a conversation response, using the indexed dataset as context.

Pre-requisites

Node.js 20.x and npm
Typesense server. Can be hosted locally using the docker-compose.yml file in the repository, instructions in Local Setup section.

Clone the project.
Install dependencies at the root of the project using npm.
```
npm install
```
(Optional) To run a local instance of Typesense server using the docker-compose.yml config in this repository, run the following command.
```
docker compose up -d
```
Note: This requires Docker to be installed on the system.
Copy .env.example file and create a .env file at the root of the project.
Set the values of required environment variables in the .env file that was created.
Run the following command to create the dataset by fetching Paul Graham's essays:
```
npm run fetchData
```
Import the data into Typesense by running the following command.
```
npm run indexInTypesense
```
This command may take a while depending on the size of the data.
Now start the Next.js application.
- For production:
```
npm run build
npm start
```
- For development:
```
npm run dev
```
Access the application at localhost:3000.

This project is licensed under Apache License 2.0. The dataset used is Paul Graham's essays by Paul Graham.

Name		Name	Last commit message	Last commit date
Latest commit History 64 Commits
dataset		dataset
src		src
.env.example		.env.example
.gitignore		.gitignore
.nvmrc		.nvmrc
LICENSE		LICENSE
README.md		README.md
docker-compose.yml		docker-compose.yml
next.config.mjs		next.config.mjs
package-lock.json		package-lock.json
package.json		package.json
postcss.config.js		postcss.config.js
tailwind.config.ts		tailwind.config.ts
tsconfig.json		tsconfig.json
wrangler.toml		wrangler.toml