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80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
from abc import ABC, abstractmethod
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from collections.abc import Sequence
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from typing import Any, Optional
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from pydantic import BaseModel, Field
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class Document(BaseModel):
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"""Class for storing a piece of text and associated metadata."""
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page_content: str
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"""Arbitrary metadata about the page content (e.g., source, relationships to other
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documents, etc.).
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"""
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metadata: Optional[dict] = Field(default_factory=dict)
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class BaseDocumentTransformer(ABC):
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"""Abstract base class for document transformation systems.
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A document transformation system takes a sequence of Documents and returns a
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sequence of transformed Documents.
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Example:
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.. code-block:: python
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class EmbeddingsRedundantFilter(BaseDocumentTransformer, BaseModel):
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embeddings: Embeddings
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similarity_fn: Callable = cosine_similarity
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similarity_threshold: float = 0.95
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class Config:
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arbitrary_types_allowed = True
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def transform_documents(
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self, documents: Sequence[Document], **kwargs: Any
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) -> Sequence[Document]:
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stateful_documents = get_stateful_documents(documents)
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embedded_documents = _get_embeddings_from_stateful_docs(
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self.embeddings, stateful_documents
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)
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included_idxs = _filter_similar_embeddings(
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embedded_documents, self.similarity_fn, self.similarity_threshold
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)
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return [stateful_documents[i] for i in sorted(included_idxs)]
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async def atransform_documents(
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self, documents: Sequence[Document], **kwargs: Any
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) -> Sequence[Document]:
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raise NotImplementedError
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"""
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@abstractmethod
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def transform_documents(
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self, documents: Sequence[Document], **kwargs: Any
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) -> Sequence[Document]:
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"""Transform a list of documents.
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Args:
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documents: A sequence of Documents to be transformed.
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Returns:
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A list of transformed Documents.
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"""
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@abstractmethod
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async def atransform_documents(
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self, documents: Sequence[Document], **kwargs: Any
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) -> Sequence[Document]:
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"""Asynchronously transform a list of documents.
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Args:
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documents: A sequence of Documents to be transformed.
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Returns:
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A list of transformed Documents.
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"""
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