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91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import os
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from abc import ABC, abstractmethod
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from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.vectorstores import Chroma
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from typing import List, Optional, Dict
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registered_methods = []
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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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persist_dir = os.path.join(self.vector_store_config["vector_store_path"],
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self.vector_store_config["vector_store_name"] + ".vectordb")
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self.vector_store_client = Chroma(persist_directory=persist_dir, embedding_function=self.embeddings)
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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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persist_dir = os.path.join(self.vector_store_config["vector_store_path"],
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self.vector_store_config["vector_store_name"] + ".vectordb")
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self.vector_store = Chroma.from_documents(docs, self.embeddings, persist_directory=persist_dir)
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self.vector_store.persist()
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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_store_client.similarity_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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