feature:add markdown embedding

pilot/source_embedding/search_milvus.py

@@ -1,61 +1,61 @@
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import Milvus
-from pymilvus import Collection,utility
-from pymilvus import connections, DataType, FieldSchema, CollectionSchema
-
-# milvus = connections.connect(
-#   alias="default",
-#   host='localhost',
-#   port="19530"
-# )
-# collection = Collection("book")
-
-
-# Get an existing collection.
-# collection.load()
+# from langchain.embeddings import HuggingFaceEmbeddings
+# from langchain.vectorstores import Milvus
+# from pymilvus import Collection,utility
+# from pymilvus import connections, DataType, FieldSchema, CollectionSchema
 #
-# search_params = {"metric_type": "L2", "params": {}, "offset": 5}
+# # milvus = connections.connect(
+# #   alias="default",
+# #   host='localhost',
+# #   port="19530"
+# # )
+# # collection = Collection("book")
 #
-# results = collection.search(
-# 	data=[[0.1, 0.2]],
-# 	anns_field="book_intro",
-# 	param=search_params,
-# 	limit=10,
-# 	expr=None,
-# 	output_fields=['book_id'],
-# 	consistency_level="Strong"
-# )
 #
-# # get the IDs of all returned hits
-# results[0].ids
+# # Get an existing collection.
+# # collection.load()
+# #
+# # search_params = {"metric_type": "L2", "params": {}, "offset": 5}
+# #
+# # results = collection.search(
+# # 	data=[[0.1, 0.2]],
+# # 	anns_field="book_intro",
+# # 	param=search_params,
+# # 	limit=10,
+# # 	expr=None,
+# # 	output_fields=['book_id'],
+# # 	consistency_level="Strong"
+# # )
+# #
+# # # get the IDs of all returned hits
+# # results[0].ids
+# #
+# # # get the distances to the query vector from all returned hits
+# # results[0].distances
+# #
+# # # get the value of an output field specified in the search request.
+# # # vector fields are not supported yet.
+# # hit = results[0][0]
+# # hit.entity.get('title')
 #
-# # get the distances to the query vector from all returned hits
-# results[0].distances
+# # milvus = connections.connect(
+# #   alias="default",
+# #   host='localhost',
+# #   port="19530"
+# # )
+# from pilot.vector_store.milvus_store import MilvusStore
 #
-# # get the value of an output field specified in the search request.
-# # vector fields are not supported yet.
-# hit = results[0][0]
-# hit.entity.get('title')
-
-# milvus = connections.connect(
-#   alias="default",
-#   host='localhost',
-#   port="19530"
-# )
-from pilot.vector_store.milvus_store import MilvusStore
-
-data = ["aaa", "bbb"]
-model_name = "/Users/chenketing/Desktop/project/all-MiniLM-L6-v2"
-embeddings = HuggingFaceEmbeddings(model_name=model_name)
-
-# text_embeddings = Text2Vectors()
-mivuls = MilvusStore(cfg={"url": "127.0.0.1", "port": "19530", "alias": "default", "table_name": "test_k"})
-
-mivuls.insert(["textc","tezt2"])
-print("success")
-
-# mivuls.from_texts(texts=data, embedding=embeddings)
-#     docs,
-#     embedding=embeddings,
-#     connection_args={"host": "127.0.0.1", "port": "19530", "alias": "default"}
-# )
+# data = ["aaa", "bbb"]
+# model_name = "xx/all-MiniLM-L6-v2"
+# embeddings = HuggingFaceEmbeddings(model_name=model_name)
+#
+# # text_embeddings = Text2Vectors()
+# mivuls = MilvusStore(cfg={"url": "127.0.0.1", "port": "19530", "alias": "default", "table_name": "test_k"})
+# 
+# mivuls.insert(["textc","tezt2"])
+# print("success")
+# ct
+# # mivuls.from_texts(texts=data, embedding=embeddings)
+# #     docs,
+# #     embedding=embeddings,
+# #     connection_args={"host": "127.0.0.1", "port": "19530", "alias": "default"}
+# # )