fabricatio-rag 0.6.0

A Python library for Retrieval-Augmented Generation (RAG) capabilities in LLM applications.

fabricatio-rag

MIT

Abstract framework for building Retrieval-Augmented Generation (RAG) pipelines on top of Fabricatio's agent architecture. Provides typed base classes, document models, and workflow actions for embedding, storing, retrieving, and reranking documents.

Requires Python 3.12+.

Installation

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

Key Components

RAG Base Class (RAG)

Type-parameterized abstract class inheriting UseEmbedding, UseReranker, and UseLLM from fabricatio-core. Defines the core RAG contract that concrete implementations must fulfill:

• add_document(data, config) — embed and store documents
• afetch_document(query, config) — retrieve documents by semantic similarity
• arefined_query(question, **kwargs) — refine user queries via a configurable template before retrieval
• arank_documents(query, documents, **kwargs) — rerank previously retrieved documents by relevance

Built-in refinement uses TEMPLATE_MANAGER.render_template with the template named in RagConfig.refined_query_template (default: "built-in/refined_query").

from fabricatio_rag.capabilities.rag import RAG, RAGConfigBase
from fabricatio_rag.models.document import StoredDocumentModel, SearchedDocumentModel

class MyRAG(
    RAG[MyStoredDoc, MySearchedDoc, MyAddConfig, MyFetchConfig]
):
    async def add_document(self, data, config=None):
        # embed with self.aembedding(...), store in vector db
        ...

    async def afetch_document(self, query, config=None):
        # embed query, search vector db, return results
        ...

Document Models (StoredDocumentModel, SearchedDocumentModel)

Generic abstract base classes for document representations.

StoredDocumentModel[ST] extends Base and Vectorizable. Key methods:

• prepare_insertion(vector) -> ST — produce a database-ready record from an embedding vector
• from_txt_files(files, chunk_size, overlap) -> List[Self] — chunk text files using the Rust-backed split_into_chunks, creating one model instance per chunk
• with_text_chunk(chunk) -> Self — create an instance from a single text chunk (subclass must implement)

SearchedDocumentModel[SD] extends Base and AsPrompt. Key methods:

• from_raw(raw) -> Self — construct from raw database result
• as_prompt() -> str — render as prompt text (from AsPrompt mixin)

from fabricatio_rag.models.document import StoredDocumentModel, SearchedDocumentModel

class MyStoredDoc(StoredDocumentModel[dict]):
    content: str

    def prepare_insertion(self, vector):
        return {"text": self.content, "vector": vector}

    @classmethod
    def with_text_chunk(cls, chunk):
        return cls(content=chunk)

class MySearchedDoc(SearchedDocumentModel[dict]):
    content: str

    @classmethod
    def from_raw(cls, raw):
        return cls(content=raw["text"])

Workflow Actions (StoreTextFile, StoreDocuments)

Ready-to-use Action subclasses that bridge the Fabricatio workflow engine with RAG storage.

StoreTextFile — ingests a list of Path objects, chunks them according to chunk_size (default 512) and chunk_overlap_ratio (default 0.3), then stores the resulting chunks via add_document.

StoreDocuments — stores pre-built model instances directly, without any chunking step.

Both accept an optional store_config for passing configuration to the underlying add_document call.

from fabricatio_rag.actions.db import StoreTextFile

class MyStoreAction(StoreTextFile[MyStoredDoc, MySearchedDoc, MyAddConfig, MyFetchConfig]):
    store_model = MyStoredDoc
    chunk_size = 1024
    chunk_overlap_ratio = 0.2
    store_config = MyAddConfig(collection="docs")

Configuration (RagConfig)

Dataclass loaded from Fabricatio's configuration system under the "rag" section.

Field	Default	Description
refined_query_template	"built-in/refined_query"	Template name for query refinement

Access via fabricatio_rag.config.rag_config.

Package Structure

fabricatio-rag/
├── python/fabricatio_rag/
│   ├── capabilities/      - RAG abstract base class and config
│   ├── actions/           - StoreTextFile, StoreDocuments workflow actions
│   ├── models/            - StoredDocumentModel, SearchedDocumentModel
│   ├── workflows/         - Workflow definitions (extend here)
│   ├── config.py          - RagConfig dataclass
│   └── __init__.py
└── pyproject.toml

Dependencies

• fabricatio-core — LLM routing, embedding, reranking, event system, workflow engine, and Rust text-chunking utilities

License

MIT — see LICENSE