vectorless 0.1.30

Vectorless is a reasoning-native document engine with the core written in Rust. It will reason through any of your structured documents — PDFs, Markdown, reports, contracts — and retrieve only what's relevant. Nothing more, nothing less.

Reason, don't vector. — Retrieval is guided by reasoning over document structure.
Model fails, we fail. — No silent degradation. No heuristic fallbacks.
No thought, no answer. — Only LLM-reasoned output counts as an answer.

Quick Start

pip install vectorless

import asyncio
from vectorless import Engine, IndexContext, QueryContext

async def main():
    engine = Engine(api_key="sk-...", model="gpt-4o", endpoint="https://api.openai.com/v1")

    # Index a document
    result = await engine.index(IndexContext.from_path("./report.pdf"))
    doc_id = result.doc_id

    # Query
    result = await engine.query(
        QueryContext("What is the total revenue?").with_doc_ids([doc_id])
    )
    print(result.single().content)

asyncio.run(main())

What It's For

Vectorless is designed for applications that need precise document retrieval:

Financial analysis — Extract specific figures from reports, compare across filings
Legal research — Find relevant clauses, trace definitions across documents
Technical documentation — Navigate large manuals, locate specific procedures
Academic research — Cross-reference findings across papers
Compliance — Audit trails with source references for every answer

Examples

See examples/ for complete usage patterns.

Contributing

Contributions welcome! If you find this useful, please ⭐ the repo — it helps others discover it.

Star History

License

Apache License 2.0