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-0.5.3.tar.gz (56.1 kB view details)

Uploaded Source

Built Distribution

neural_rag-0.5.3-py310-none-any.whl (88.1 kB view details)

Uploaded Python 3.10

File details

Details for the file neural_rag-0.5.3.tar.gz.

File metadata

  • Download URL: neural_rag-0.5.3.tar.gz
  • Upload date:
  • Size: 56.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.2

File hashes

Hashes for neural_rag-0.5.3.tar.gz
Algorithm Hash digest
SHA256 badd610cecf2ef81d178812115fbee7fc0590cabffb9e8d08b7dc871674f624c
MD5 370ef046aff680c41d4eb41084bcfdf8
BLAKE2b-256 a2e1e8c22660f760aa51bd97a40738f32c15b948285df2ebc335ae20240a4985

See more details on using hashes here.

File details

Details for the file neural_rag-0.5.3-py310-none-any.whl.

File metadata

  • Download URL: neural_rag-0.5.3-py310-none-any.whl
  • Upload date:
  • Size: 88.1 kB
  • Tags: Python 3.10
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.2

File hashes

Hashes for neural_rag-0.5.3-py310-none-any.whl
Algorithm Hash digest
SHA256 f4f03a49ce4662744344ad0a3c4f010766b5b21f123f6d405e93f878bad04ca7
MD5 6af73a4f14ee86dc97fe8952826e3125
BLAKE2b-256 5a147537f20c7e3c508b4da32c1e4d898d664469ff9c230ae5ab09163331cda8

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