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93 lines
2.7 KiB
Python
93 lines
2.7 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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from abc import ABC, abstractmethod
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from typing import Dict, List, Optional
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from pilot.configs.config import Config
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from pilot.vector_store.connector import VectorStoreConnector
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registered_methods = []
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CFG = Config()
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def register(method):
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registered_methods.append(method.__name__)
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return method
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class SourceEmbedding(ABC):
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"""base class for read data source embedding pipeline.
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include data read, data process, data split, data to vector, data index vector store
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Implementations should implement the method
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"""
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def __init__(
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self,
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file_path,
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vector_store_config,
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embedding_args: Optional[Dict] = None,
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):
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"""Initialize with Loader url, model_name, vector_store_config"""
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self.file_path = file_path
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self.vector_store_config = vector_store_config
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self.embedding_args = embedding_args
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self.embeddings = vector_store_config["embeddings"]
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self.vector_client = VectorStoreConnector(
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CFG.VECTOR_STORE_TYPE, vector_store_config
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)
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@abstractmethod
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@register
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def read(self) -> List[ABC]:
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"""read datasource into document objects."""
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@register
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def data_process(self, text):
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"""pre process data."""
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@register
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def text_split(self, text):
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"""text split chunk"""
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pass
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@register
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def text_to_vector(self, docs):
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"""transform vector"""
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pass
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@register
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def index_to_store(self, docs):
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"""index to vector store"""
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self.vector_client.load_document(docs)
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@register
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def similar_search(self, doc, topk):
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"""vector store similarity_search"""
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return self.vector_client.similar_search(doc, topk)
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def vector_name_exist(self):
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return self.vector_client.vector_name_exists()
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def source_embedding(self):
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if "read" in registered_methods:
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text = self.read()
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if "data_process" in registered_methods:
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text = self.data_process(text)
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if "text_split" in registered_methods:
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self.text_split(text)
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if "text_to_vector" in registered_methods:
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self.text_to_vector(text)
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if "index_to_store" in registered_methods:
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self.index_to_store(text)
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def batch_embedding(self):
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if "read_batch" in registered_methods:
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text = self.read_batch()
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if "data_process" in registered_methods:
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text = self.data_process(text)
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if "text_split" in registered_methods:
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self.text_split(text)
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if "text_to_vector" in registered_methods:
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self.text_to_vector(text)
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if "index_to_store" in registered_methods:
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self.index_to_store(text)
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