python-nlql 0.3.0

SQL-style semantic query language and retrieval middleware for Agents & RAG

NLQL

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NLQL lets you do semantic search with SQL-style statements. Relevance scoring, filtering, and sorting live in one query — no more scattered embedding calls and post-processing code.

Built for Agent and RAG applications: the query itself is structured data, usable directly as an LLM tool-call payload.

What it looks like

import nlql

engine = nlql.Engine(nlql.embed.FakeEmbedder())  # or OpenAIEmbedder, or any Embedder
engine.add_text("AI agents plan tasks and call tools.", metadata={"status": "published"})
engine.add_text("Banana bread needs flour and sugar.", metadata={"status": "draft"})

for unit in engine.search('''
    SELECT SENTENCE
    LET rel = SIMILARITY(content, "autonomous agents")
    WHERE rel >= 0.2 AND meta.status == "published"
    ORDER BY rel DESC
    LIMIT 5
'''):
    print(f"{unit.scores['rel']:+.3f}  {unit.content}")

The statement reads almost like SQL: SELECT sets the return granularity, LET computes relevance, WHERE filters, ORDER BY / LIMIT sort and cap.

Features

• One statement, full intent — relevance, filtering, and sorting in one place, not scattered across business code
• Three ways to write, identical results — SQL statement, Python chained builder, or JSON IR; all compile to the same internal representation
• Pluggable backends — built-in store works out of the box; switch to Qdrant / Faiss / Chroma / HnswLib / pgvector with one line
• Two-stage retrieval — attach a reranker after recall for higher accuracy
• Multimodal — text and images share one vector space; retrieve images with text
• Explainable — engine.explain() prints the query plan

Installation

pip install python-nlql

Optional extras:

Command	Purpose
pip install "python-nlql[faiss]"	Faiss backend
pip install "python-nlql[hnsw]"	HnswLib backend (for large-scale data)
pip install "python-nlql[qdrant]"	Qdrant backend
pip install "python-nlql[chroma]"	Chroma backend
pip install "python-nlql[pgvector]"	Postgres + pgvector backend
pip install "python-nlql[local]"	local sentence-transformers / CLIP / cross-encoder
pip install "python-nlql[loaders]"	DOCX / PDF file loaders

Switching backends

One line; ingestion and query code stay the same:

from nlql.store.qdrant_store import QdrantStore
engine = nlql.Engine(embedder, store=QdrantStore(location=":memory:"))

Documentation

Full docs, tutorials, and API reference: https://natural-language-query-language.github.io/python-nlql/en/

• Quick start
• Design
• API reference
• 中文文档

More examples in the examples/ directory.

License

MIT