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88 lines
2.8 KiB
Python
88 lines
2.8 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 langchain.embeddings import HuggingFaceEmbeddings
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from typing import List, Optional, Dict
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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__(self, file_path, model_name, vector_store_config, embedding_args: Optional[Dict] = None):
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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.model_name = model_name
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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 = HuggingFaceEmbeddings(model_name=self.model_name)
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vector_store_config["embeddings"] = self.embeddings
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self.vector_client = VectorStoreConnector(CFG.VECTOR_STORE_TYPE, vector_store_config)
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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 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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