Skip to main content

Lightweight, LLM-agnostic RAG pipeline with pluggable corpora. Works with Claude, OpenAI, Gemini, or any LLM.

Project description

attune-rag

Lightweight, LLM-agnostic RAG pipeline with pluggable corpora. Works with Claude, OpenAI, Gemini, or any LLM.

  • No LLM SDK at install time. All provider deps are optional extras.
  • Pluggable corpus. Use attune-help (the default), any markdown directory, or your own CorpusProtocol.
  • Returns a prompt string by default — send it to whatever LLM you like. Optional provider adapters ship convenience wrappers.

Install

pip install attune-rag                     # core only
pip install 'attune-rag[attune-help]'      # + bundled help corpus
pip install 'attune-rag[claude]'           # + Claude adapter
pip install 'attune-rag[openai]'           # + OpenAI adapter
pip install 'attune-rag[gemini]'           # + Gemini adapter
pip install 'attune-rag[all]'              # everything

Quick start — Claude

pip install 'attune-rag[attune-help,claude]'
import asyncio
from attune_rag import RagPipeline

async def main():
    pipeline = RagPipeline()  # defaults to AttuneHelpCorpus
    response, result = await pipeline.run_and_generate(
        "How do I run a security audit with attune?",
        provider="claude",
    )
    print(response)
    print("\nSources:", [h.entry.path for h in result.citation.hits])

asyncio.run(main())

Quick start — OpenAI

pip install 'attune-rag[attune-help,openai]'
response, result = await pipeline.run_and_generate(
    "...", provider="openai", model="gpt-4o",
)

Quick start — Gemini

pip install 'attune-rag[attune-help,gemini]'
response, result = await pipeline.run_and_generate(
    "...", provider="gemini", model="gemini-1.5-pro",
)

Quick start — custom corpus, any LLM

from pathlib import Path
from attune_rag import RagPipeline, DirectoryCorpus

pipeline = RagPipeline(corpus=DirectoryCorpus(Path("./my-docs")))
result = pipeline.run("How do I...?")

# Send result.augmented_prompt to whatever LLM you use.
# The pipeline itself does NOT call an LLM unless you use
# run_and_generate or call a provider adapter yourself.

Status

v0.1.0 — initial release. Part of the attune ecosystem (attune-ai, attune-help, attune-author).

License

Apache 2.0. See LICENSE.

Project details


Download files

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

Source Distribution

attune_rag-0.1.5.tar.gz (29.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

attune_rag-0.1.5-py3-none-any.whl (36.3 kB view details)

Uploaded Python 3

File details

Details for the file attune_rag-0.1.5.tar.gz.

File metadata

  • Download URL: attune_rag-0.1.5.tar.gz
  • Upload date:
  • Size: 29.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for attune_rag-0.1.5.tar.gz
Algorithm Hash digest
SHA256 b35e116658fca88dc70660fc53fffb59e69e7b58877e2009ed28eff12585429f
MD5 87a4e9ebf281503614ad8adeb34288e1
BLAKE2b-256 dbc9b3ea4348209273e980c38a7e43f9c43cf4c9bd5eb15b9dce4092f95e1972

See more details on using hashes here.

Provenance

The following attestation bundles were made for attune_rag-0.1.5.tar.gz:

Publisher: publish.yml on Smart-AI-Memory/attune-rag

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file attune_rag-0.1.5-py3-none-any.whl.

File metadata

  • Download URL: attune_rag-0.1.5-py3-none-any.whl
  • Upload date:
  • Size: 36.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for attune_rag-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 f02e2ce1b660c71cd4850c456c3006d6ffb97cb0888d332262ee94e52aec9164
MD5 76cb08cbd3e703a68c13fea11a7966c7
BLAKE2b-256 275dd5971db69551fa0a15ec863ca230671368f48a9ed73e265ab1ad9ef9d013

See more details on using hashes here.

Provenance

The following attestation bundles were made for attune_rag-0.1.5-py3-none-any.whl:

Publisher: publish.yml on Smart-AI-Memory/attune-rag

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

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