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run_system.py
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run_system.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import time
import numpy as np
import pandas as pd
from paddle_serving_server.pipeline import PipelineClient
sys.path.append("./recall/milvus") # noqa: E402
from config import collection_name, embedding_name, partition_tag # noqa: E402
from milvus_util import RecallByMilvus # noqa: E402
def recall_result(list_data):
client = PipelineClient()
client.connect(["127.0.0.1:8080"])
feed = {}
for i, item in enumerate(list_data):
feed[str(i)] = item
start_time = time.time()
ret = client.predict(feed_dict=feed)
end_time = time.time()
print("Extract feature time to cost :{} seconds".format(end_time - start_time))
result = np.array(eval(ret.value[0]))
return result
def search_in_milvus(embeddings, query_text):
recall_client = RecallByMilvus()
start_time = time.time()
results = recall_client.search(
embeddings, embedding_name, collection_name, partition_names=[partition_tag], output_fields=["pk", "text"]
)
end_time = time.time()
print("Search milvus time cost is {} seconds ".format(end_time - start_time))
list_data = []
for line in results:
for item in line:
# idx = item.id
distance = item.distance
text = item.entity.get("text")
list_data.append([query_text, text, distance])
df = pd.DataFrame(list_data, columns=["query_text", "text", "distance"])
df.to_csv("recall_result.csv", index=False)
return df
def rerank(df):
client = PipelineClient()
client.connect(["127.0.0.1:8089"])
list_data = []
for index, row in df.iterrows():
example = {"query": row["query_text"], "title": row["text"]}
list_data.append(example)
feed = {}
for i, item in enumerate(list_data):
feed[str(i)] = str(item)
start_time = time.time()
ret = client.predict(feed_dict=feed)
end_time = time.time()
print("time to cost :{} seconds".format(end_time - start_time))
result = np.array(eval(ret.value[0]))
df["distance"] = result
df = df.sort_values(by=["distance"], ascending=False)
df.to_csv("rank_result.csv", index=False)
if __name__ == "__main__":
list_data = ["ไธญ่ฅฟๆน่ฏญ่จไธๆๅ็ๅทฎๅผ"]
result = recall_result(list_data)
df = search_in_milvus(result, list_data[0])
rerank(df)