62 lines
1.3 KiB
Python
62 lines
1.3 KiB
Python
import json
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from pymilvus import (
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connections,
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utility,
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FieldSchema,
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CollectionSchema,
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DataType,
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Collection,
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)
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MILVUS_HOST = "127.0.0.1"
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MILVUS_PORT = "19530"
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connections.connect("default", host=MILVUS_HOST, port=MILVUS_PORT)
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collection = Collection("todos")
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f = open("question_vec.json", "r").read()
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vector_data = json.loads(f)
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search_param = {
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"data": [vector_data],
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"anns_field": "vector",
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"param": {"metric_type": "L2", "ef": 250},
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"limit": 10,
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"expr": "user_id == 2",
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"output_fields": ["todo_id", "title", "source", "todo_description", "language", "text", "user_id"],
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}
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res = collection.search(**search_param)
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# search data
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# json_strings = [
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# '{"page_content": "I love MLflow.", "metadata": {"source": "/path/to/mlflow.txt"}}',
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# '{"page_content": "I love langchain.", "metadata": {"source": "/path/to/langchain.txt"}}',
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# '{"page_content": "I love AI.", "metadata": {"source": "/path/to/ai.txt"}}',
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# ]
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json_string = []
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# get all of the text
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for i in range(len(res[0])):
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data = []
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data.append({"page_content": res[0][i].get("text")})
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data.append({"metadata": {"source": res[0][i].get("source")}})
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json_string.append(data)
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print(json_string)
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#
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# print(res[0][0].get("text"))
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#
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# print("metadata")
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# print(res[0][0].get("source"))
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# get all
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