LiteGraf

Lightweight knowledge graph ingestion and query pipeline. Insert text or documents, extract entities and relationships with an LLM, store them in a graph database, and query with natural language.

from pipeline.litegraf import LiteGraf

kg = LiteGraf()
kg.insert("TP53 is associated with multiple cancers including breast and lung cancer.")
result = kg.query("What cancers are associated with TP53?")
print(result.answer)

Features

• Single entry point — LiteGraf dataclass with sensible defaults, override only what you need
• Pluggable backends — Neo4j, Memgraph, Ollama, Cloudflare Workers AI, AWS Bedrock
• Sync and async — insert() / ainsert(), query() / aquery()
• Content deduplication — hash-based, idempotent inserts
• LLM response caching — disk-based, avoids redundant API calls
• Rate limiting — async concurrency limiter for LLM providers
• PDF and document ingestion — via MarkItDown + PyMuPDF
• Benchmarking suite — compare extraction quality across LLM providers
• Enrichment pipeline — entity resolution, ontology integration, evidence scoring

Install

Requires Python 3.11+.

pip install litegraf

With optional backends:

pip install litegraf[neo4j]       # Neo4j graph store
pip install litegraf[bedrock]     # AWS Bedrock LLM
pip install litegraf[all]         # Everything

Or from source with uv:

git clone https://github.com/graffold/litegraf.git
cd litegraf
uv sync --all-extras

Quick Start

Default setup (Ollama + Neo4j)

Start Ollama and Neo4j locally, then:

from pipeline.litegraf import LiteGraf

kg = LiteGraf()  # connects to localhost defaults

# Insert text
kg.insert("BRCA1 interacts with RAD51 in DNA repair pathways.")

# Insert a PDF
kg.insert(open("paper.pdf", "rb").read())

# Query
result = kg.query("What proteins interact with BRCA1?")
print(result.answer)
print(result.context)  # retrieved graph context

Cloudflare Workers AI (free tier)

kg = LiteGraf(
    llm="cloudflare",
    llm_model="@cf/meta/llama-3.1-8b-instruct-fp8",
)

Memgraph backend

kg = LiteGraf(
    graph_store="memgraph",
    graph_uri="bolt://localhost:7687",
    graph_user="",
    graph_password="",
)

Async usage

import asyncio
from pipeline.litegraf import LiteGraf

async def main():
    kg = LiteGraf()
    await kg.ainsert("TP53 suppresses tumor growth.")
    result = await kg.aquery("What does TP53 do?")
    print(result.answer)

asyncio.run(main())

Query modes

# Full pipeline: retrieve context → LLM synthesis
result = kg.query("What cancers involve TP53?")

# Context only (bring your own LLM prompt)
result = kg.query("TP53", mode="only_context")
for chunk in result.context:
    print(chunk.text, chunk.score)

Configuration

All parameters can be set via the LiteGraf constructor:

Benchmarks

Compare extraction quality across LLM providers on biomedical datasets:

python -m pipeline.benchmarks

Results are published to docs/ for GitHub Pages viewing.

Development

uv sync --all-extras --group dev
uv run pytest
uv run ruff check src/

License

AGPL-3.0