Skip to main content

No project description provided

Project description

# Neural RAG

Neural Rag is a LLM framework to build Vector RAG and Graph RAG knowledge base. It provides the foundation to quickly build agents.

## What is RAG?

RAG stands for Retrieval Augmented Generation.

It was introduced in the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401).

Each step can be roughly broken down to:

  • Retrieval - Seeking relevant information from a source given a query. For example, getting relevant passages of Wikipedia text from a database given a question.

  • Augmented - Using the relevant retrieved information to modify an input to a generative model (e.g. an LLM).

  • Generation - Generating an output given an input. For example, in the case of an LLM, generating a passage of text given an input prompt.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

neural_rag-0.3.16.tar.gz (42.2 kB view details)

Uploaded Source

Built Distribution

neural_rag-0.3.16-py310-none-any.whl (55.4 kB view details)

Uploaded Python 3.10

File details

Details for the file neural_rag-0.3.16.tar.gz.

File metadata

  • Download URL: neural_rag-0.3.16.tar.gz
  • Upload date:
  • Size: 42.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.2

File hashes

Hashes for neural_rag-0.3.16.tar.gz
Algorithm Hash digest
SHA256 9b26fa0d0385af961738bea550e88ebb2273984bb7dfbe838bda3765d7e7230b
MD5 b5ce6fba2403380dddc09d7c9ca240c5
BLAKE2b-256 44acbfbd5950d1b64318953e0038018347e9b14b253db04a9e3e71ffbda0aaba

See more details on using hashes here.

File details

Details for the file neural_rag-0.3.16-py310-none-any.whl.

File metadata

File hashes

Hashes for neural_rag-0.3.16-py310-none-any.whl
Algorithm Hash digest
SHA256 68447daa71b648629ce344ff7931ece071444fbfba04ceb2865644a920b874f2
MD5 3776003c76ed8a4b145e2aadb5dbad91
BLAKE2b-256 1f8f7d70f7e9f81d39a89c024df6a6b159af1b3d494391be9275d5be6645f308

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page