labelrag 0.1.2

A label-driven RAG pipeline built on top of paralabelgen.

labelrag

labelrag is a Python library for label-driven retrieval-augmented generation pipelines built on top of paralabelgen.

• PyPI distribution: labelrag
• Python import package: labelrag
• Core dependency target: paralabelgen==0.2.3
• Primary supported extraction path: paralabelgen LLM concept extraction
• First semantic-reranking embedding provider: sentence-transformers

Install

pip install labelrag

If you want to use the spaCy-backed extraction path, install a compatible English pipeline such as:

python -m spacy download en_core_web_sm

en_core_web_sm is a convenient local option, but the semantic-retrieval release line targets the paralabelgen==0.2.3 LLM concept-extraction pipeline as the primary supported extraction path.

The upstream paralabelgen==0.2.3 runtime also supports DeepSeek-backed extraction through its own configuration surface. labelrag does not introduce DeepSeek-specific APIs; it continues to pass extraction configuration through to paralabelgen.

The first shipped semantic-reranking provider uses sentence-transformers. Its model weights may be downloaded on first use if they are not already cached locally.

Quick Start

Retrieval-only workflow

from labelrag import (
    RAGPipeline,
    RAGPipelineConfig,
)

paragraphs = [
    "OpenAI builds language models for developers.",
    "Developers use language models in production systems.",
    "Production systems need monitoring and evaluation tooling.",
]

config = RAGPipelineConfig()
config.labelgen.extractor_mode = "heuristic"
config.labelgen.use_graph_community_detection = False

pipeline = RAGPipeline(config)
pipeline.fit(paragraphs)

retrieval = pipeline.build_context("How do developers use language models?")
print(retrieval.prompt_context)
print(retrieval.metadata)

Retrieval plus provider-backed answer generation

from labelrag import (
    OpenAICompatibleAnswerGenerator,
    OpenAICompatibleConfig,
    RAGPipeline,
    RAGPipelineConfig,
)

paragraphs = [
    "OpenAI builds language models for developers.",
    "Developers use language models in production systems.",
    "Production systems need monitoring and evaluation tooling.",
]

config = RAGPipelineConfig()
config.labelgen.extractor_mode = "heuristic"
config.labelgen.use_graph_community_detection = False

pipeline = RAGPipeline(config)
pipeline.fit(paragraphs)

generator = OpenAICompatibleAnswerGenerator(
    OpenAICompatibleConfig(
        model="mistral-small-latest",
        api_key_env_var="MISTRAL_API_KEY",
        base_url="https://api.mistral.ai/v1",
    )
)

answer = pipeline.answer_with_generator(
    "How do developers use language models?",
    generator,
)
print(answer.answer_text)
print(answer.metadata)

Retrieval Model

The current retrieval layer is deterministic and still label-driven at the candidate-generation stage.

• fit(...) delegates paragraph analysis to labelgen.LabelGenerator
• fit(...) also builds paragraph embeddings
• build_context(...) maps the question into the fitted label space
• retrieval uses greedy coverage over query label IDs
• semantic similarity is used as a secondary ranking signal inside greedy selection
• label-free queries can use configurable fallback strategies
• require_full_label_coverage=True suppresses partial retrieval results while preserving attempted coverage trace in metadata

Greedy selection order is:

1. larger overlap with remaining query labels
2. larger semantic similarity
3. larger overlap on query concept IDs
4. larger total paragraph label count
5. lexicographically smaller paragraph_id

0.1.2 supports two main-path retrieval strategies:

• greedy_label_coverage_semantic_rerank
• label_gate_semantic_rank

label_gate_semantic_rank keeps label overlap as a candidate gate but lets semantic similarity become the primary ranking signal inside that gated set.

0.1.2 supports four label-free fallback strategies:

• concept_overlap_only
• concept_overlap_semantic_rerank
• concept_gate_semantic_rank
• semantic_only

The default is concept_overlap_semantic_rerank.

concept_gate_semantic_rank mirrors the main gated semantic-first behavior for label-free queries:

• paragraph concepts must still intersect the query concepts to enter the candidate set
• semantic similarity is then the primary ranking signal inside that gated set

The default strategies remain unchanged in 0.1.2:

• main path:
  • greedy_label_coverage_semantic_rerank
• label-free fallback:
  • concept_overlap_semantic_rerank

OpenAI-Compatible Provider Notes

The built-in answer-generation adapter targets a minimal OpenAI-compatible chat-completions API surface.

It supports:

• standard base URLs such as https://api.openai.com/v1
• full endpoint URLs such as https://api.mistral.ai/v1/chat/completions
• API key injection through explicit config or optional environment-variable lookup
• non-streaming text generation for answer_with_generator(...)

This adapter is intended to cover providers such as OpenAI, Mistral, and Qwen when they expose an OpenAI-compatible endpoint shape.

Public API

The main public entrypoints are:

• RAGPipeline
• RAGPipelineConfig, RetrievalConfig, PromptConfig
• IndexedParagraph, LabelRecord, ConceptRecord
• QueryAnalysis, RetrievedParagraph
• RetrievalResult, RAGAnswerResult
• GeneratedAnswer, AnswerGenerator
• OpenAICompatibleAnswerGenerator, OpenAICompatibleConfig
• convenience re-export: Paragraph

RAGPipeline also exposes record-oriented inspection helpers for paragraph/label/concept lookup workflows:

• get_paragraph(...)
• get_label(...)
• get_paragraph_labels(...)
• get_paragraph_concepts(...)
• get_label_paragraphs(...)
• get_concept_paragraphs(...)

Lower-level ID-oriented helpers remain available when you only need stable IDs:

• get_label_paragraph_ids(...)
• get_paragraph_label_ids(...)
• get_paragraph_concept_ids(...)
• get_concept_paragraph_ids(...)

Detailed API notes are available in docs/public_api.md.

Embedding Notes

• RAGPipeline.fit(...) now requires an embedding provider
• the default runtime path is RAGPipeline(config) and resolves the embedding provider from config.embedding
• explicit embedding_provider= is still available as an advanced override
• the first shipped provider is SentenceTransformerEmbeddingProvider
• the default model is sentence-transformers/all-MiniLM-L6-v2
• the model may be downloaded on first use
• offline environments should pre-cache the embedding model before running fit(...)

Common runtime failures:

• missing sentence-transformers package:
  • reinstall project dependencies, for example pip install -e .
• model load/download failure:
  • verify the configured model name
  • ensure the model is already cached locally or that the environment can reach Hugging Face

Examples

Runnable examples are available in examples/:

• examples/basic_usage.py
• examples/custom_config.py
• examples/inspection_api.py
• examples/fallback_policies.py
• examples/gated_semantic_rank.py
• examples/semantic_rerank.py
• examples/save_and_load.py
• examples/provider_answer.py

Example note:

• the runnable example scripts use a tiny local demo embedding provider so they stay runnable offline
• production usage should prefer SentenceTransformerEmbeddingProvider

Persistence Notes

save(path) produces a human-inspectable directory containing:

• manifest.json
• config.json
• label_generator.json
• corpus_index.json
• fit_result.json
• paragraph_embeddings.npz

The persistence layer now supports:

• json
• json.gz

Compression is applied to the full saved snapshot rather than mixing compressed and uncompressed artifacts in one directory.

Snapshots written by the current release include a lightweight manifest describing the saved version, persistence format, and expected artifacts.

Public guarantee:

• a saved and reloaded pipeline should preserve retrieval behavior for the same fitted state, question, and config

Current update boundary:

• fit(...) is batch-only
• adding new paragraphs currently requires a full refit
• save/load restores a static fitted state rather than an incrementally updateable corpus state

Legacy snapshot note:

• loading pre-embedding snapshots remains a best-effort compatibility path
• when older snapshots are missing derived concept inspection tables, load() may rebuild them from paragraph-side concept data that is still present
• when older snapshots predate paragraph_embeddings.npz, load() may rebuild paragraph embeddings from persisted paragraph texts if an embedding provider is available
• persisted manifests include a non-empty labelrag_version
• save() fails explicitly if the current package version cannot be determined for manifest writing

Configuration Notes

• RetrievalConfig.max_paragraphs sets the hard retrieval limit
• RetrievalConfig.retrieval_strategy selects one of:
  • greedy_label_coverage_semantic_rerank
  • label_gate_semantic_rank
• RetrievalConfig.allow_label_free_fallback enables deterministic concept fallback behavior for label-free queries
• RetrievalConfig.label_free_fallback_strategy selects one of:
  • concept_overlap_only
  • concept_overlap_semantic_rerank
  • concept_gate_semantic_rank
  • semantic_only
• RetrievalConfig.require_full_label_coverage suppresses partial retrieval output when not all query labels can be covered
• PromptConfig.include_paragraph_ids includes stable paragraph IDs in the rendered prompt context
• PromptConfig.include_label_annotations includes paragraph label annotations in rendered prompt context
• PromptConfig.max_context_characters applies a hard cap to rendered context length

Development Checks

.venv/bin/ruff check . --fix
.venv/bin/pyright
.venv/bin/pytest

Release Checks

.venv/bin/python -m build
.venv/bin/python -m twine check dist/*