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76 lines
2.6 KiB
Python
76 lines
2.6 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.embedding_engine.knowledge_type import KnowledgeType
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from pilot.server.knowledge.service import KnowledgeService
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from pilot.server.knowledge.request.request import KnowledgeSpaceRequest
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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.embedding_engine.knowledge_embedding import KnowledgeEmbedding
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knowledge_space_service = KnowledgeService()
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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[CFG.EMBEDDING_MODEL]
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def knowledge_persist(self, file_path):
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"""knowledge persist"""
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docs = []
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embedding_engine = None
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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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ke = KnowledgeEmbedding(
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knowledge_source=filename,
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knowledge_type=KnowledgeType.DOCUMENT.value,
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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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embedding_engine = ke.init_knowledge_embedding()
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doc = ke.read()
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docs.extend(doc)
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embedding_engine.index_to_store(docs)
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print(f"""begin create {self.vector_store_config["vector_store_name"]} space""")
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try:
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space = KnowledgeSpaceRequest
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space.name = self.vector_store_config["vector_store_name"]
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space.desc = "knowledge_init.py"
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space.vector_type = CFG.VECTOR_STORE_TYPE
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space.owner = "DB-GPT"
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knowledge_space_service.create_knowledge_space(space)
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except Exception as e:
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if "have already named" in str(e):
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print(f"Warning: you have already named {space.name}")
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else:
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raise e
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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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args = parser.parse_args()
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vector_name = args.vector_name
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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)
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print("your knowledge embedding success...")
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