`fabricatio-lancedb`

MIT Python Versions

LanceDB vector store backend for Fabricatio RAG. Provides async Rust-backed table operations — creation, document insertion, vector similarity search, and index rebuilding — plus Python-side RAG capability mixins and document models.

Architecture

The package has two layers:

Layer	Language	Key Types	Purpose
Rust (PyO3)	Rust	`VectorStoreService`, `VectorStoreTable`, `StoreDocument`, `SearchedDocument`	High-performance LanceDB table ops with async execution
Python	Python	`LancedbRAG`, `LancedbDocumentModel`, `LancedbConfig`	RAG capability, document models, configuration

The Rust layer manages LanceDB connections and tables. The Python layer implements the fabricatio-rag RAG interface on top of those primitives: batching embeddings, inserting documents, and searching by vector.

Installation

pip install fabricatio[lancedb]
# or
uv pip install fabricatio[lancedb]

For all Fabricatio extras:

pip install fabricatio[full]

Configuration

All options below are read through the fabricatio configuration chain (see the Configuration Guide at ../../docs/source/configuration.rst). Set them under the [ext.lancedb] table in fabricatio.toml, equivalently under [tool.fabricatio.ext.lancedb] in pyproject.toml, or via FABRICATIO_EXT__LANCEDB__<FIELD_UPPER> environment variables.

# fabricatio.toml
[ext.lancedb]
database_uri = "./lance.db"
default_table_name = "default"

Option	Type	Default	Description
`database_uri`	`str`	`"./lance.db"`	LanceDB connection URI (local path or S3 `s3://bucket/path`)
`default_table_name`	`str`	`"default"`	Table name used when none is specified

Access at runtime: from fabricatio_lancedb.config import lancedb_config.

Usage

Low-level Rust API

Direct table operations with async Rust execution:

import asyncio
from fabricatio_lancedb.rust import VectorStoreService, VectorStoreTable, StoreDocument

async def main():
    # Connect to LanceDB
    service = await VectorStoreService.connect("./lance.db")

    # Create a table for 1536-dim embeddings (OpenAI text-embedding-3-small)
    table = await service.create_table("my_table", ndim=1536)

    # Add documents
    docs = [
        StoreDocument(
            content="LanceDB is a fast, open-source vector database.",
            vector=[0.1] * 1536,
        ),
        StoreDocument.with_metadata(
            content="Fabricatio is an LLM application framework.",
            vector=[0.2] * 1536,
            metadata={"source": "docs", "version": "1.0"},
        ),
    ]
    ids = await table.add_documents(docs)
    print(f"Inserted: {ids}")

    # Search by embedding
    results = await table.search_document(embedding=[0.15] * 1536, limit=5)
    for r in results:
        print(f"{r.id}: {r.content[:60]}...")
        print(f"  metadata: {r.access_metadata()}")

    # Rebuild the index after bulk inserts with rebuild_index=False
    await table.rebuild_index()

asyncio.run(main())

RAG capability (high-level)

Integrate with Fabricatio's RAG system using the LancedbRAG mixin:

from fabricatio_lancedb.capabilities.lancedb import LancedbRAG, LancedbAddRAGConfig, LancedbFetchRAGConfig
from fabricatio_lancedb.models.lancedb import LancedbDocumentModel

class MyDoc(LancedbDocumentModel):
    """Custom document with additional fields."""
    title: str = ""

class MyRAGRole(SomeBaseRole, LancedbRAG[MyDoc, LancedbAddRAGConfig, LancedbFetchRAGConfig[MyDoc]]):
    pass

async def run():
    role = MyRAGRole(name="my-rag-role")
    # Add a document — handles embedding batching internally
    await role.add_document(
        MyDoc(content="Semantic search with LanceDB and Fabricatio.", title="Intro"),
        config=LancedbAddRAGConfig(table_name="docs", embedding_batch_size=20),
    )
    # Fetch relevant documents
    results = await role.afetch_document(
        "how does semantic search work",
        config=LancedbFetchRAGConfig(document_model=MyDoc, limit=10),
    )
    for doc in results:
        print(f"[{doc.title}] {doc.content}")

Cached service helper

Reuse connections across calls:

from fabricatio_lancedb.inited_service import get_service

service = await get_service("s3://my-bucket/lancedb")
table = await service.open_table("production_docs")

API Reference

Rust layer (`fabricatio_lancedb.rust`)

VectorStoreService

Method	Signature	Returns	Description
`connect`	`(uri: str) -> Awaitable[Self]`	`VectorStoreService`	Static — connect to a LanceDB instance
`create_table`	`(table_name: str, ndim: int) -> Awaitable[VectorStoreTable]`	`VectorStoreTable`	Create a new table with the given vector dimension
`open_table`	`(table_name: str) -> Awaitable[VectorStoreTable]`	`VectorStoreTable`	Open an existing table
`create_or_open_table`	`(table_name: str, ndim: int) -> Awaitable[VectorStoreTable]`	`VectorStoreTable`	Create if absent, otherwise open

VectorStoreTable

Method	Signature	Returns	Description
`add_documents`	`(documents: list[StoreDocument], rebuild_index: bool = True) -> Awaitable[list[str]]`	Document IDs	Insert documents; set `rebuild_index=False` for bulk inserts
`search_document`	`(embedding: list[float], limit: int, dedup_threshold: float \| None = None) -> Awaitable[list[SearchedDocument]]`	Search results	Nearest-neighbor vector search; with `dedup_threshold`, near-duplicates (cosine similarity ≥ threshold vs. an already-kept result) are dropped and replaced by deeper candidates
`rebuild_index`	`() -> Awaitable[None]`	None	Rebuild the vector index (no-op if <256 rows)

StoreDocument

Field	Type	Description
`content`	`str`	Document text content
`vector`	`list[float]`	Dense embedding vector
`metadata`	`str \| None`	Optional JSON-serialized metadata

Static constructor StoreDocument.with_metadata(content, vector, metadata: dict) serializes the metadata dict to JSON.

SearchedDocument

Property	Type	Description
`id`	`str`	UUID document identifier
`content`	`str`	Matched document text
`timestamp`	`int`	Microsecond-precision timestamp
`metadata`	`str \| None`	Raw JSON metadata string

Method access_metadata() -> dict parses the JSON metadata into a Python dict.

Python layer

LancedbRAG

Extends RAG from fabricatio-rag with LanceDB storage.

Method	Description
`add_document(data, config)`	Vectorize and insert one or more documents
`afetch_document(query, config)`	Vectorize a query string and return matching documents
`rebuild_index(table_name?)`	Rebuild the vector index on a table

LancedbAddRAGConfig

Dataclass config for add_document:

Field	Default	Description
`table_name`	`lancedb_config.default_table_name`	Target table
`embedding_batch_size`	`10`	Documents per embedding batch
`embedding_parallel_size`	`10`	Max concurrent embedding calls
`rebuild_index`	`False`	Rebuild index after insertion

LancedbFetchRAGConfig

Dataclass config for afetch_document:

Field	Default	Description
`table_name`	`lancedb_config.default_table_name`	Source table
`document_model`	`None` (required)	Document model class for deserialization
`limit`	`15`	Max results returned
`dedup_cos_threshold`	`0.95`	Cosine similarity at which a candidate is treated as a duplicate of an already-selected result; `None` disables deduplication

LancedbDocumentModel

Extends StoredDocumentModel and SearchedDocumentModel. Fields: content (str), metadata (dict | None).

Method	Description
`prepare_insertion(vector) -> StoreDocument`	Build a Rust `StoreDocument` ready for insertion
`from_raw(raw: SearchedDocument) -> Self`	Deserialize a Rust `SearchedDocument`
`with_text_chunk(chunk: str) -> Self`	Create from a plain text chunk

Schema

Each LanceDB table uses this Arrow schema:

Column	Type	Nullable	Description
`item`	`Utf8`	no	Primary key (UUID v7)
`timestamp`	`Time64(us)`	no	Insertion timestamp
`vector`	`FixedSizeList(Float32, ndim)`	no	Embedding vector
`content`	`Utf8`	no	Document text
`metadata`	`Utf8`	yes	JSON-serialized metadata

Dependencies

fabricatio-core — core interfaces and configuration
fabricatio-rag — base RAG abstractions (RAG, document models)
more-itertools — chunked iteration for batch processing
async-lru — async LRU caching

Rust dependencies (via PyO3): lancedb, arrow, pyo3, tokio.

License

This project is licensed under the MIT License — see LICENSE.

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Size: 53.9 MB
Tags: CPython 3.12, macOS 11.0+ ARM64
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This release

0.3.4 This release

Aug 23, 2026 12 files

0.3.3

Jul 25, 2026 12 files

0.3.2

Jun 17, 2026 12 files

0.3.0

May 29, 2026 12 files

0.2.2

May 25, 2026 12 files

0.2.1

May 22, 2026 12 files

0.2.0

May 19, 2026 12 files

0.1.1

May 18, 2026 12 files

fabricatio-lancedb 0.3.4

fabricatio-lancedb

Architecture

Installation

Configuration

Usage

Low-level Rust API

RAG capability (high-level)

Cached service helper

API Reference

Rust layer (fabricatio_lancedb.rust)

VectorStoreService

VectorStoreTable

StoreDocument

SearchedDocument

Python layer

LancedbRAG

LancedbAddRAGConfig

LancedbFetchRAGConfig

LancedbDocumentModel

Schema

Dependencies

License

Project links

Key dates

1 maintainer

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License

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`fabricatio-lancedb`

Rust layer (`fabricatio_lancedb.rust`)