e34b1e4c6d
Refactor the `kotaemon/pipelines` module to `kotaemon/indices`. Create the VectorIndex. Note: currently I place `qa` to be inside `kotaemon/indices` since at the moment we only have `qa` in RAG. At the same time, I think `qa` can be an independent module in `kotaemon/qa`. Since this can be changed later, I still go at the 1st option for now to observe if we can change it later.
93 lines
2.9 KiB
Python
93 lines
2.9 KiB
Python
from __future__ import annotations
|
|
|
|
from abc import abstractmethod
|
|
from typing import Any, Type
|
|
|
|
from llama_index.node_parser.interface import NodeParser
|
|
|
|
from kotaemon.base import BaseComponent, Document, RetrievedDocument
|
|
|
|
|
|
class DocTransformer(BaseComponent):
|
|
"""This is a base class for document transformers
|
|
|
|
A document transformer transforms a list of documents into another list
|
|
of documents. Transforming can mean splitting a document into multiple documents,
|
|
reducing a large list of documents into a smaller list of documents, or adding
|
|
metadata to each document in a list of documents, etc.
|
|
"""
|
|
|
|
@abstractmethod
|
|
def run(
|
|
self,
|
|
documents: list[Document],
|
|
**kwargs,
|
|
) -> list[Document]:
|
|
...
|
|
|
|
|
|
class LlamaIndexDocTransformerMixin:
|
|
"""Allow automatically wrapping a Llama-index component into kotaemon component
|
|
|
|
Example:
|
|
class TokenSplitter(LlamaIndexMixin, BaseSplitter):
|
|
def _get_li_class(self):
|
|
from llama_index.text_splitter import TokenTextSplitter
|
|
return TokenTextSplitter
|
|
|
|
To use this mixin, please:
|
|
1. Use this class as the 1st parent class, so that Python will prefer to use
|
|
the attributes and methods of this class whenever possible.
|
|
2. Overwrite `_get_li_class` to return the relevant LlamaIndex component.
|
|
"""
|
|
|
|
def _get_li_class(self) -> Type[NodeParser]:
|
|
raise NotImplementedError(
|
|
"Please return the relevant LlamaIndex class in _get_li_class"
|
|
)
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
_li_cls = self._get_li_class()
|
|
self._obj = _li_cls(*args, **kwargs)
|
|
super().__init__()
|
|
|
|
def __setattr__(self, name: str, value: Any) -> None:
|
|
if name.startswith("_") or name in self._protected_keywords():
|
|
return super().__setattr__(name, value)
|
|
|
|
return setattr(self._obj, name, value)
|
|
|
|
def __getattr__(self, name: str) -> Any:
|
|
return getattr(self._obj, name)
|
|
|
|
def run(
|
|
self,
|
|
documents: list[Document],
|
|
**kwargs,
|
|
) -> list[Document]:
|
|
"""Run Llama-index node parser and convert the output to Document from
|
|
kotaemon
|
|
"""
|
|
docs = self._obj(documents, **kwargs) # type: ignore
|
|
return [Document.from_dict(doc.to_dict()) for doc in docs]
|
|
|
|
|
|
class BaseIndexing(BaseComponent):
|
|
"""Define the base interface for indexing pipeline"""
|
|
|
|
def to_retrieval_pipeline(self, **kwargs):
|
|
"""Convert the indexing pipeline to a retrieval pipeline"""
|
|
raise NotImplementedError
|
|
|
|
def to_qa_pipeline(self, **kwargs):
|
|
"""Convert the indexing pipeline to a QA pipeline"""
|
|
raise NotImplementedError
|
|
|
|
|
|
class BaseRetrieval(BaseComponent):
|
|
"""Define the base interface for retrieval pipeline"""
|
|
|
|
@abstractmethod
|
|
def run(self, *args, **kwargs) -> list[RetrievedDocument]:
|
|
...
|