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61 lines
1.9 KiB
Python
61 lines
1.9 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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VECTOR_SEARCH_TOP_K,
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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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model_name = LLM_MODEL_CONFIG["text2vec"]
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top_k: int = VECTOR_SEARCH_TOP_K
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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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def knowledge_persist(self, file_path, append_mode):
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"""knowledge persist"""
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kv = KnowledgeEmbedding(
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file_path=file_path,
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model_name=LLM_MODEL_CONFIG["text2vec"],
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vector_store_config=self.vector_store_config,
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)
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vector_store = kv.knowledge_persist_initialization(append_mode)
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return vector_store
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def query(self, q):
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"""Query similar doc from Vector"""
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vector_store = self.init_vector_store()
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docs = vector_store.similarity_search_with_score(q, k=self.top_k)
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for doc in docs:
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dc, s = doc
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yield s, dc
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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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parser.add_argument("--store_type", type=str, default="Chroma")
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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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vector_store = kv.knowledge_persist(file_path=DATASETS_DIR, append_mode=append_mode)
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print("your knowledge embedding success...")
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