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59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
import sys
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sys.path.append(".")
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from concurrent.futures import ThreadPoolExecutor
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from memory_scope.models.llama_index_embedding_model import LlamaIndexEmbeddingModel
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from memory_scope.storage.llama_index_es_memory_store_sync import \
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LlamaIndexEsMemoryStoreSync as SyncLlamaIndexEsMemoryStore
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from memory_scope.utils.logger import Logger
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logger = Logger.get_logger("default")
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# Define the class as a Ray actor
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class ThreadTest(object):
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def __init__(self):
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self.task_list = []
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config = {
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"module_name": "dashscope_embedding",
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"model_name": "text-embedding-v2",
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"clazz": "models.llama_index_embedding_model",
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}
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emb = LlamaIndexEmbeddingModel(**config)
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config = {
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"index_name": "0708_2",
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"es_url": "http://localhost:9200",
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"embedding_model": emb,
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"use_hybrid": True
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}
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self.es_store = SyncLlamaIndexEsMemoryStore(**config) # 不能在async中初始化
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self.logger = logger
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def major_func(self, i: int):
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result = self.es_store.retrieve_memories("_", top_k=10, filter_dict={})
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print(result)
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return result
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def run(self):
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while True:
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executor_internal = ThreadPoolExecutor(max_workers=5)
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f1 = executor_internal.submit(self.major_func, i=1)
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f2 = executor_internal.submit(self.major_func, i=2)
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f2 = executor_internal.submit(self.major_func, i=3)
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f2 = executor_internal.submit(self.major_func, i=4)
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f2 = executor_internal.submit(self.major_func, i=5)
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executor_internal.shutdown()
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f1.result()
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f2.result()
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executor = ThreadPoolExecutor(max_workers=5)
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t1 = executor.submit(ThreadTest().run)
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executor.shutdown()
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