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54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import argparse
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import os
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import sys
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sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
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from pilot.configs.config import Config
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from pilot.configs.model_config import (
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DATASETS_DIR,
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LLM_MODEL_CONFIG,
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)
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from pilot.source_embedding.knowledge_embedding import KnowledgeEmbedding
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CFG = Config()
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class LocalKnowledgeInit:
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embeddings: object = None
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def __init__(self, vector_store_config) -> None:
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self.vector_store_config = vector_store_config
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self.model_name = LLM_MODEL_CONFIG["text2vec"]
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def knowledge_persist(self, file_path, append_mode):
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"""knowledge persist"""
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for root, _, files in os.walk(file_path, topdown=False):
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for file in files:
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filename = os.path.join(root, file)
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# docs = self._load_file(filename)
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ke = KnowledgeEmbedding(
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file_path=filename,
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model_name=self.model_name,
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vector_store_config=self.vector_store_config,
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)
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client = ke.init_knowledge_embedding()
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client.source_embedding()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--vector_name", type=str, default="default")
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parser.add_argument("--append", type=bool, default=False)
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args = parser.parse_args()
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vector_name = args.vector_name
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append_mode = args.append
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store_type = CFG.VECTOR_STORE_TYPE
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vector_store_config = {"vector_store_name": vector_name}
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print(vector_store_config)
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kv = LocalKnowledgeInit(vector_store_config=vector_store_config)
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kv.knowledge_persist(file_path=DATASETS_DIR, append_mode=append_mode)
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print("your knowledge embedding success...")
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