raglineage 0.2.6

Lineage-aware RAG engine for auditable, reproducible, versioned retrieval and answers

raglineage

Lineage-aware RAG engine for auditable, reproducible, versioned retrieval and answers

The Unique Idea

Most RAG tools store text chunks and embeddings. They lose provenance and cannot explain answer drift.

raglineage treats RAG as a data lineage and provenance problem, not just vector search. Every retrievable unit is a Lineage Node (LN) with:

• Immutable ID and dataset version
• Precise source reference (file path, page, row, URL, etc.)
• Full transform chain (ordered list of transforms applied)
• Content hash for integrity
• Timestamps for auditing

The system maintains a Lineage Graph (DAG) linking nodes through structural and semantic relationships, enabling:

• Dataset versioning and diffing
• Incremental rebuilds (only recompute what changed)
• Answer auditing (reconstruct provenance of any answer)
• Version consistency checks
• Staleness detection

This is not a LangChain/LlamaIndex wrapper—it's a first-class lineage system.

Architecture

┌─────────────────────────────────────────────────────────────┐
│                      Data Sources                            │
│  (PDFs, CSVs, JSON, APIs, Text Files)                       │
└──────────────────────┬──────────────────────────────────────┘
                       │
                       ▼
┌─────────────────────────────────────────────────────────────┐
│                    Ingestion Layer                           │
│  AutoIngestor → FileIngestor → TabularIngestor              │
└──────────────────────┬──────────────────────────────────────┘
                       │
                       ▼
┌─────────────────────────────────────────────────────────────┐
│                   Transform Layer                            │
│  Chunkers → Dedupe → Normalize                              │
│  (Each transform recorded in transform_chain)               │
└──────────────────────┬──────────────────────────────────────┘
                       │
                       ▼
┌─────────────────────────────────────────────────────────────┐
│              Lineage Node Creation                           │
│  ln_id, source, transform_chain, content_hash, version      │
└──────────────────────┬──────────────────────────────────────┘
                       │
                       ▼
┌─────────────────────────────────────────────────────────────┐
│              Lineage Graph (DAG)                             │
│  networkx DAG: nodes=LN, edges=relationships                │
└──────────────────────┬──────────────────────────────────────┘
                       │
                       ▼
┌─────────────────────────────────────────────────────────────┐
│              Embedding + Vector Store                        │
│  Embeddings → FAISS Store → LN ID Mapping                   │
└──────────────────────┬──────────────────────────────────────┘
                       │
                       ▼
┌─────────────────────────────────────────────────────────────┐
│              Retrieval + Audit                               │
│  Query → Top-K → Graph Walk → Answer + Lineage             │
│  Audit → Version Check → Staleness → Risk Flags             │
└─────────────────────────────────────────────────────────────┘

Lineage Node Example

Every retrievable chunk is a Lineage Node with complete provenance:

{
  "ln_id": "ln_92af",
  "content": "Revenue declined due to supply constraints",
  "source": {
    "type": "pdf",
    "uri": "data/10Q_Q3_2023.pdf",
    "page": 14,
    "section": "Management Discussion"
  },
  "dataset_version": "v3.1",
  "transform_chain": [
    "pdf_parse",
    "section_split",
    "semantic_chunk",
    "deduplicate"
  ],
  "content_hash": "sha256:a3f5b8c9d2e1f4a6b7c8d9e0f1a2b3c4d5e6f7a8b9c0d1e2f3a4b5c6d7e8f9a0",
  "created_at": "2026-01-20T00:00:00Z"
}

Audited Answer Example

Every answer includes full lineage and audit metadata:

{
  "question": "Why did revenue fall in Q3?",
  "answer": "Revenue declined primarily due to supply constraints affecting shipments.",
  "lineage": [
    {
      "ln_id": "ln_92af",
      "score": 0.91,
      "source": {
        "uri": "data/10Q_Q3_2023.pdf",
        "page": 14
      },
      "dataset_version": "v3.1",
      "transform_chain": ["pdf_parse","section_split","semantic_chunk","deduplicate"]
    }
  ],
  "audit": {
    "staleness_check": "pass",
    "version_consistency": "single_version",
    "transform_risk_flags": []
  }
}

Quickstart

Installation

pip install raglineage

Basic Usage

from raglineage import RagLineage

rag = RagLineage(
    source="examples/data",
    store_backend="faiss",
    embed_backend="local"
)

# Build initial version
rag.build(version="v1.0")

# Query with lineage
ans = rag.query("What is the refund policy?", k=5)
print(ans.model_dump_json(indent=2))

# Audit the answer
report = rag.audit(ans)
print(report.model_dump_json(indent=2))

Comprehensive Example

For a complete demonstration of all features, see:

• examples/comprehensive_example/comprehensive_example.py - Shows all functionalities
• examples/basic_usage/example.py - Minimal getting started example

CLI Usage

# Initialize a project
raglineage init ./my_project

# Build from source
raglineage build --source ./data --version v1.0

# Update incrementally
raglineage update --source ./data --version v1.1 --changed-only

# Query
raglineage query "What is the refund policy?" --k 5

# Diff versions
raglineage diff v1.0 v1.1

Comparison with Other RAG Tools

Feature	raglineage	LangChain	LlamaIndex
Lineage Tracking	First-class	Not built-in	Not built-in
Dataset Versioning	Native	Manual	Manual
Incremental Updates	Automatic	Full rebuild	Full rebuild
Answer Auditing	Built-in	Manual	Manual
Transform Chain Tracking	Every LN	Not tracked	Not tracked
Version Diffing	Structured	Not available	Not available
Graph Relationships	DAG-based	Optional	Optional
Source Provenance	Complete	Basic	Basic

Key Difference: raglineage treats lineage as a core requirement, not an afterthought. Every operation preserves and tracks provenance.

Core Concepts

Lineage Nodes (LN)

A Lineage Node is the atomic unit of retrieval. Each LN has:

• ln_id: Stable, deterministic identifier
• content: The actual text content
• source: Precise reference to origin (file, page, row, etc.)
• dataset_version: Version tag for the dataset
• transform_chain: Ordered list of transforms applied
• content_hash: SHA-256 hash for integrity
• timestamps: Created/updated timestamps

Lineage Graph

A directed acyclic graph (DAG) where:

• Nodes: Lineage Node IDs
• Edges: Typed relationships (adjacent, semantic, references, same_entity, etc.)

Enables graph-walk retrieval and relationship exploration.

Dataset Versioning

Each dataset build produces a versioned manifest:

• Tracks all source files and their hashes
• Enables diffing between versions
• Supports incremental updates (only recompute changed files)

Answer Auditing

Every answer includes:

• Lineage: List of LNs used with scores and metadata
• Audit Report:
  • Version consistency check
  • Staleness detection
  • Transform risk flags

Complete Feature Set

1. Data Ingestion

• Auto-detection: Automatically detects file types and routes to appropriate ingestor
• File Ingestion: Text files (.txt, .md, .rst)
• Tabular Ingestion: CSV, JSON files (row-by-row processing)
• Extensible: Easy to add custom ingestors for PDFs, APIs, databases

2. Transform Pipeline

• Chunking Strategies:
  • Simple chunking (character-based with overlap)
  • Semantic chunking (sentence-aware with overlap)
• Deduplication: Content hash-based duplicate detection
• Normalization: Text cleanup and normalization (with aggressive mode)
• Transform Chain Tracking: Every transform is recorded in the lineage

3. Embedding Backends

• Local Embeddings: sentence-transformers (default: all-MiniLM-L6-v2)
• OpenAI Embeddings: Optional OpenAI API integration
• Extensible: Easy to add custom embedding backends

4. Vector Storage

• FAISS Store: Efficient similarity search with L2 distance
• LN ID Mapping: Bidirectional mapping between vector indices and Lineage Node IDs
• Persistence: Stores index and mappings to disk
• Incremental Updates: Add/update vectors without full rebuild

5. Retrieval

• Top-K Retrieval: Standard vector similarity search
• Graph-Walk Expansion: Expand results using graph relationships
• Filtering: Filter by version, source URI, source type, minimum score
• Configurable Depth: Control graph walk depth for expansion

6. Lineage Graph

• DAG Structure: NetworkX-based directed acyclic graph
• Relationship Types: adjacent, semantic, references, same_entity, derived, parent_child
• Graph Operations:
  • Add nodes and edges
  • Get neighbors at specified depth
  • Export/import as JSON
  • Query node information

7. Dataset Versioning

• Version Manifests: Track all versions with file lists and hashes
• File Tracking: SHA-256 hashes for change detection
• Version Comparison: Diff between any two versions
• Current Version: Track active version

8. Incremental Updates

• Change Detection: Automatically detects changed files using hashes
• Selective Processing: Only processes added/modified files
• Efficient Rebuilds: Recomputes embeddings only for changed content
• Graph Updates: Incrementally updates graph relationships

9. Answer Auditing

• Staleness Detection: Checks if answer uses outdated data
• Version Consistency: Verifies all sources are from same version
• Transform Risk Analysis: Flags risky transforms (OCR, aggressive normalization, etc.)
• Complete Audit Reports: JSON-serializable audit metadata

10. CLI Interface

• raglineage init: Initialize a new project
• raglineage build: Build database from source
• raglineage update: Incrementally update database
• raglineage query: Query with lineage output
• raglineage diff: Compare dataset versions

11. Export and Integration

• JSON Export: Export lineage graph, answers, audit reports as JSON
• Python API: Full programmatic access to all features
• Type Hints: Complete type annotations for IDE support
• Pydantic Models: All data structures are Pydantic models for validation

Requirements

• Python ≥ 3.10
• Strict type hints throughout
• Pydantic models for schemas
• NetworkX for graph operations
• FAISS for vector storage
• Sentence-transformers for local embeddings

Development

# Clone repository
git clone https://github.com/PranavMotarwar/raglineage.git
cd raglineage

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest

# Run linting
ruff check .

Contributing

See CONTRIBUTING.md for guidelines.

License

Apache-2.0 License. See LICENSE for details.

Author

Pranav Motarwar - GitHub

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raglineage - Where every answer has a traceable origin.