Sensei Search

Sensei Search is an AI-powered tool designed to deliver the relevant search results.

🎥 Demo

https://www.heysensei.app

📸 Screenshots

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💡 Insights from Utilizing Open Source LLMs

The key takeaways and experiences of working with open source Large Language Models are summarized in a detailed discussion. For more information, you can read the full discussion on Reddit:

Building an Open Source Perplexity AI with Open Source LLMs - Reddit Post

🛠️ Tech Stack

Sensei Search is built using the following technologies:

Frontend: Next.js, Tailwind CSS
Backend: FastAPI, OpenAI client
LLMs: Mistral-7b and Command-R
Search: SearxNG
Memory: Redis
Deployment: AWS, Paka

🏃‍♂️ How to Run Sensei Search

You can run Sensei Search either locally on your machine or in the cloud.

Running Locally

Follow these steps to run Sensei Search locally:

Prepare the backend environment:
```
cd sensei_root_folder/backend/
mv .env.development.example .env.development
```
Edit .env.development as needed. The example environment assumes you run models through Ollama. Make sure you have reasonably good GPUs to run the command-r model.
No need to do anything for the frontend.

Run the app with the following command:

cd sensei_root_folder/
docker compose up

Open your browser and go to http://localhost:3000

Running in the Cloud

We deploy the app to AWS using paka. Please note that the models require GPU instances to run.

Before you start, make sure you have:

An AWS account
Requested GPU quota in your AWS account

The configuration for the cluster is located in the cluster.yaml file. You'll need to replace the HF_TOKEN value in cluster.yaml with your own Hugging Face token. This is necessary because the mistral-7b and command-r models require your account to have accepted their terms and conditions.

Follow these steps to run Sensei Search in the cloud:

Install paka:
```
pip install paka
```
Provision the cluster in AWS:
```
make provision-prod
```
Deploy the backend:
```
make deploy-backend
```
Deploy the frontend:
```
make deploy-frontend
```
Get the URL of the frontend:
```
paka function list
```
Open the URL in your browser.

jjleng/sensei