ragit

Crates.ioragit
lib.rsragit
version
sourcesrc
created_at2024-10-20 14:35:14.546414
updated_at2025-02-01 12:23:26.528369
descriptiongit-like rag pipeline
homepage
repository
max_upload_size
id1416249
Cargo.toml error:TOML parse error at line 17, column 1 | 17 | autolib = false | ^^^^^^^ unknown field `autolib`, expected one of `name`, `version`, `edition`, `authors`, `description`, `readme`, `license`, `repository`, `homepage`, `documentation`, `build`, `resolver`, `links`, `default-run`, `default_dash_run`, `rust-version`, `rust_dash_version`, `rust_version`, `license-file`, `license_dash_file`, `license_file`, `licenseFile`, `license_capital_file`, `forced-target`, `forced_dash_target`, `autobins`, `autotests`, `autoexamples`, `autobenches`, `publish`, `metadata`, `keywords`, `categories`, `exclude`, `include`
size0
(baehyunsol)

documentation

README

RAGIT

RAGIT (rag-it) is a git-like software that turns your local files into a knowledge-base. The main goal of this project is to make knowledge-bases easy-to-create and easy-to-share.

rag init;
rag add --all;
rag build;
rag query "What makes ragit special?";

Why another RAG framework?

RAGIT is very different from the other RAG frameworks.

  1. It adds a title and summary to every chunks. The summaries make AIs very easy to rerank chunks.
  2. It uses tfidf scores instead of vector searches. It first asks an AI to generate keywords from a query, then runs tfidf search with the keywords.
  3. It supports markdown files with images.
  4. It supports multi-turn queries (experimental).
  5. You can clone/push knowledge-bases, like git.
  • push command is WIP.

More documents

Commit count: 0

cargo fmt