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WORK IN PROGRESS - An app that shows you how to build a conversation search experience, using Typesense's built-in RAG pipeline

License: Apache License 2.0

TypeScript 98.81% JavaScript 0.89% CSS 0.30%

showcase-conversational-search-pg-essays's Introduction

Note

Work in progress 🚧

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. (Skip the TYPESENSE_CONVERSATION_MODEL_ID env variable for now, we'll come back to it).
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.
The script will output a conversational model ID, which you want to set as the TYPESENSE_CONVERSATION_MODEL_ID in your .env file.
Now start the Next.js application.
- For production:
```
npm run build
npm start
```
- For development:
```
npm run dev
```
Access the application at localhost:3000.