scinr

AI-Powered Document Knowledge Library for Life Sciences

scinr (scinr.newton) is a Python library and command-line interface (newton) designed to transform unstructured and structured life sciences documents into queryable, connected knowledge graphs in Neo4j and document stores in MongoDB.

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Key Features

• 🚀 5-Stage Pipeline: Seamlessly convert, extract, ingest, annotate, and extract entities from raw files.
• 📄 Multi-Format Ingestion: Supports .pdf, .docx, .pptx, .xlsx, .csv, .json, .html, and .txt.
• 📊 Tabular Pipeline: Auto-detection, structural normalization, and LLM entity extraction for scientific spreadsheets.
• 🏷️ Pydantic Extraction Models: Define structured schemas for scientific target entities (e.g. compound synthesis, clinical trials, assays).
• 🕸️ Neo4j Graph Integration: Automatically map extracted entities and triples into Neo4j subgraphs.
• 🤖 Agent Ready: Native support for llms.txt and llms-full.txt context windows for AI agents.

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Quick Example

import asyncio
from scinr.newton import configure, run_pipeline

async def main():
    # 1. Configure backend connections and model defaults
    configure(
        neo4j_uri="bolt://localhost:7687",
        neo4j_auth=("neo4j", "password"),
        llm_provider="openai",
        llm_model="gpt-4o",
    )

    # 2. Run the end-to-end ingestion pipeline on a directory of documents
    result = await run_pipeline(
        input_raw="./raw_documents",
        model_class="CompoundAssayModel",
        parallel_docs=4,
    )

    print(f"Pipeline Succeeded: {result.success}")
    print(f"Total Duration: {result.total_duration_seconds:.2f}s")
    print(f"Executed Stages: {result.stages_executed}")

if __name__ == "__main__":
    asyncio.run(main())