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-0.5.7.tar.gz (56.4 kB view details)

Uploaded Source

Built Distribution

neural_rag-0.5.7-py310-none-any.whl (90.2 kB view details)

Uploaded Python 3.10

File details

Details for the file neural_rag-0.5.7.tar.gz.

File metadata

  • Download URL: neural_rag-0.5.7.tar.gz
  • Upload date:
  • Size: 56.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.2

File hashes

Hashes for neural_rag-0.5.7.tar.gz
Algorithm Hash digest
SHA256 f626270a1d0e2ec4a1fe55a38b62182155eee53efdbc9aaef9953a3a42c153cc
MD5 eae50a10b937d5374dac86db314b3b4f
BLAKE2b-256 f6b39e18bd3db365c1c853868be7b26e655d02fc0d2d48f03c8b7eb2989efc88

See more details on using hashes here.

File details

Details for the file neural_rag-0.5.7-py310-none-any.whl.

File metadata

  • Download URL: neural_rag-0.5.7-py310-none-any.whl
  • Upload date:
  • Size: 90.2 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-0.5.7-py310-none-any.whl
Algorithm Hash digest
SHA256 145e29455ba1a3ea7d3cd9b6ae88d94277087cd64d4e0d647340b6ac49b58b48
MD5 1970e1ffc25adf3f15fd457066dc5fb5
BLAKE2b-256 c9e7da8718172f0f3eae8a7e5a71c427f0291ec490239523767fd01b38441cc8

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