attune-rag 0.1.16

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

attune-rag

Lightweight, LLM-agnostic RAG pipeline with pluggable corpora. Works with Claude, 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.
• Optional hybrid retrieval. QueryExpander and LLMReranker layer Claude Haiku on top of keyword retrieval to improve recall and precision — both opt-in, both fail-safe.

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[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 — 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.

Hybrid retrieval (optional)

QueryExpander and LLMReranker require the [claude] extra and an ANTHROPIC_API_KEY. Both are opt-in and fail-safe — any API error falls back to keyword-only order automatically.

from attune_rag import RagPipeline, LLMReranker, QueryExpander

# Reranker only (recommended for precision):
pipeline = RagPipeline(reranker=LLMReranker())

# Expander + reranker (max coverage):
pipeline = RagPipeline(
    expander=QueryExpander(),
    reranker=LLMReranker(),
)

Template editor primitives (attune_rag.editor)

Headless toolkit for tools that need to validate, lint, and refactor a template corpus — used by the attune-gui template editor and the attune-author edit CLI, but works standalone with any CorpusProtocol.

API	What it does
load_schema()	Loads template_schema.json (the v1 frontmatter contract: required type enum + name; optional tags, aliases, summary, source, hash; additionalProperties: true).
parse_frontmatter(text) / validate_frontmatter(data)	Split a template into frontmatter + body and report typed FrontmatterIssues — used by linters and editors.
lint_template(text, rel_path, corpus)	Returns Diagnostic[] for schema violations, broken [[alias]] references, and depth-marker sequence errors. 1-indexed line/col ranges.
autocomplete_tags(corpus, prefix, limit) / autocomplete_aliases(corpus, prefix, limit)	Prefix-match completions ranked by frequency (tags) or lexical proximity (aliases). Sub-ms on 1k templates.
find_references(corpus, name, kind)	Locate every alias/tag/path occurrence across body, frontmatter, and cross_links.json.
plan_rename(corpus, old, new, kind)	Build a RenamePlan (one FileEdit per affected file with unified-diff hunks) for kind="alias" or "tag". Raises RenameCollisionError on existing alias targets.
apply_rename(corpus, plan)	Atomically apply the plan (tempfile-per-file + sequential rename + drift-detection rollback). Returns the list of affected paths.

Schema, lint, and rename are pure functions over CorpusProtocol — no I/O, no global state. All three pieces are tested as a unit and used live by the attune-gui editor's /api/corpus/<id>/lint, /autocomplete, and /refactor/rename/{preview,apply} routes.

from attune_rag import DirectoryCorpus
from attune_rag.editor import lint_template, plan_rename, apply_rename

corpus = DirectoryCorpus(Path("./templates")).load()

# Validate a template before saving
diagnostics = lint_template(
    text=Path("./templates/concepts/foo.md").read_text(),
    rel_path="concepts/foo.md",
    corpus=corpus,
)

# Rename an alias across the whole corpus
plan = plan_rename(corpus, old="oldname", new="newname", kind="alias")
print(f"Affects {len(plan.edits)} files")
affected = apply_rename(corpus, plan)

Dashboard

attune-rag dashboard show    # live terminal dashboard
attune-rag dashboard render --out report.html  # HTML snapshot

Roadmap — embeddings (next minor release)

Keyword retrieval + optional Claude reranker currently carry attune-rag past 87% P@1 on the attune-help golden set. The remaining misses are queries with zero token overlap against their target doc (e.g. "vulnerability scan" → tool-security-audit.md). Closing that gap needs vector search.

Next minor release will ship attune-rag[embeddings] using fastembed for local, CPU-only embeddings — no new network dependency, no API key required at retrieval time. Keyword retrieval stays the default; embeddings layer in opt-in, same shape as QueryExpander and LLMReranker.

See CHANGELOG.md for the decision record and remaining-gap analysis.

Prompt caching (Claude only)

When using the Claude provider, run_and_generate automatically enables Anthropic prompt caching on the stable RAG context prefix (≥ 1 024 chars). This eliminates repeated token costs on the corpus portion of the prompt when the same context block is reused across calls.

No configuration needed — the provider handles the cache_control header automatically.

Status

v0.1.10. Part of the attune ecosystem (attune-ai, attune-help, attune-author).

License

Apache 2.0. See LICENSE.