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-0.4.29.tar.gz (56.6 kB view details)

Uploaded Source

Built Distribution

neural_rag-0.4.29-py310-none-any.whl (80.4 kB view details)

Uploaded Python 3.10

File details

Details for the file neural_rag-0.4.29.tar.gz.

File metadata

  • Download URL: neural_rag-0.4.29.tar.gz
  • Upload date:
  • Size: 56.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.2

File hashes

Hashes for neural_rag-0.4.29.tar.gz
Algorithm Hash digest
SHA256 396f2b825f2bb236c9f5f7aeb972d76d118026a266c1ff2b2ac37922eaed452d
MD5 23d516afc985e71ebfb64d9d36b73ce5
BLAKE2b-256 00da33b6a6284481d2231f15dd75c30b50b962ada14baee6ad4041b78f6e87b5

See more details on using hashes here.

File details

Details for the file neural_rag-0.4.29-py310-none-any.whl.

File metadata

File hashes

Hashes for neural_rag-0.4.29-py310-none-any.whl
Algorithm Hash digest
SHA256 0f89011eb785a59ddf242c2bf9d8c3a32d84c7bdfac4486033be61a20c355338
MD5 19e90a7fe850edfabfff29d7388db1c1
BLAKE2b-256 12435d1dd5edaa6bbf6eb980df504e2011878d47567728f1f0a81b7b128d23f4

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