Rusty RAG - LLM-powered Book Recommendation

This project implements a simple semantic search and RAG use-case with rust (mistral.rs/fastembed-rs) + pgvector and serves a frontend and rest-api to recommend books based on the CMU Book Summary Dataset.

Setup

Start database and ollama instance

First run:

docker-compose up

to start the postgres database.

Generate book summary embeddings

Then download the dataset from Kaggle and start the embedding generation:

cargo run --release --bin embedding-generator -- --num-summaries 16384 --num-chunks 2048 --summaries-file ./booksummaries.txt

Set Hugging Face Token

This project uses hugging face to remotely load models from the internet. To authenticate your account, save the hugging face api token from token-settings to ~/.cache/huggingface/token.

Run the web application

cargo run --release --bin rustyrag

Features

You can accelerate embedding generation and chat completion via metal (Apple SNE) or cuda. Simply run the above command with

cargo run --release --features cuda ...

or

cargo run --release --features metal ...

Troubleshooting

Request Error 401

If you are facing this error:

thread 'main' panicked at /Users/z0011bjk/.cargo/git/checkouts/mistral.rs-0a2607fe9768eac5/5c3e9f1/mistralrs-core/src/pipeline/gguf.rs:282:58:
RequestError(Status(401, Response[status: 401, status_text: Unauthorized, url:
https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2/resolve/main/tokenizer.json]))

You need to save your hugging face token first to: ~/.cache/huggingface/token

Request Error 403

If you are facing the following error:

thread 'main' panicked at /Users/z0011bjk/.cargo/git/checkouts/mistral.rs-0a2607fe9768eac5/5c3e9f1/mistralrs-core/src/pipeline/gguf.rs:282:58:
RequestError(Status(403, Response[status: 403, status_text: Forbidden, url:
https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2/resolve/main/tokenizer.json]))

If you are using the default mistralai/Mistral-7B-Instruct-v0.2 model, you have to be accepted the license at huggingface first.