Add text embedding function

examples/example_bulkinsert_withfunction.py

@@ -0,0 +1,360 @@
+import random
+import json
+import csv
+import time
+import os
+
+from pymilvus import (
+    connections,
+    FieldSchema, CollectionSchema, DataType,
+    Collection,
+    utility,
+    BulkInsertState,
+    Function, FunctionType,
+)
+
+
+LOCAL_FILES_PATH = "/tmp/milvus_bulkinsert"
+
+# Milvus service address
+_HOST = '127.0.0.1'
+_PORT = '19530'
+
+# Const names
+_COLLECTION_NAME = 'demo_bulk_insert_csv'
+_ID_FIELD_NAME = 'id_field'
+_VECTOR_FIELD_NAME = 'float_vector_field'
+_JSON_FIELD_NAME = "json_field"
+_VARCHAR_FIELD_NAME = "varchar_field"
+_DYNAMIC_FIELD_NAME = "$meta"     # dynamic field, the internal name is "$meta", enable_dynamic_field=True
+
+
+# Vector field parameter
+_DIM = 1536
+
+# to generate increment ID
+id_start = 1
+
+# Create a Milvus connection
+def create_connection():
+    retry = True
+    while retry:
+        try:
+            print(f"\nCreate connection...")
+            connections.connect(host=_HOST, port=_PORT)
+            retry = False
+        except Exception as e:
+            print("Cannot connect to Milvus. Error: " + str(e))
+            print(f"Cannot connect to Milvus. Trying to connect Again. Sleeping for: 1")
+            time.sleep(1)
+
+    print(f"\nList connections:")
+    print(connections.list_connections())
+
+# Create a collection
+def create_collection(has_partition_key: bool):
+    field1 = FieldSchema(name=_ID_FIELD_NAME, dtype=DataType.INT64, description="int64", is_primary=True, auto_id=False)
+    field2 = FieldSchema(name=_VECTOR_FIELD_NAME, dtype=DataType.FLOAT_VECTOR, description="float vector", dim=_DIM,
+                         is_primary=False)
+    field3 = FieldSchema(name=_JSON_FIELD_NAME, dtype=DataType.JSON)
+    # if has partition key, we use this varchar field as partition key field
+    field4 = FieldSchema(name=_VARCHAR_FIELD_NAME, dtype=DataType.VARCHAR, max_length=256, is_partition_key=has_partition_key)
+    schema = CollectionSchema(fields=[field1, field2, field3, field4], enable_dynamic_field=True)
+    text_embedding_function = Function(
+        name="openai",
+        function_type=FunctionType.TEXTEMBEDDING,
+        input_field_names=[_VARCHAR_FIELD_NAME],
+        output_field_names=_VECTOR_FIELD_NAME,
+        params={
+        "provider": "openai",
+        "model_name": "text-embedding-3-small",
+        }
+    )
+    schema.add_function(text_embedding_function)
+    if has_partition_key:
+        collection = Collection(name=_COLLECTION_NAME, schema=schema, num_partitions=10)
+    else:
+        collection = Collection(name=_COLLECTION_NAME, schema=schema)
+    print("\nCollection created:", _COLLECTION_NAME)
+    return collection
+
+# Test existence of a collection
+def has_collection():
+    return utility.has_collection(_COLLECTION_NAME)
+
+# Drop a collection in Milvus
+def drop_collection():
+    collection = Collection(_COLLECTION_NAME)
+    collection.drop()
+    print("\nDrop collection:", _COLLECTION_NAME)
+
+
+# List all collections in Milvus
+def list_collections():
+    print("\nList collections:")
+    print(utility.list_collections())
+
+# Create a partition
+def create_partition(collection, partition_name):
+    collection.create_partition(partition_name=partition_name)
+    print("\nPartition created:", partition_name)
+    return collection.partition(partition_name)
+
+def gen_csv_rowbased(num, path, partition_name, sep=","):
+    global id_start
+    header = [_ID_FIELD_NAME, _JSON_FIELD_NAME, _VARCHAR_FIELD_NAME, _DYNAMIC_FIELD_NAME]
+    rows = []
+    for i in range(num):
+        rows.append([
+            id_start, # id field
+            json.dumps({"Number": id_start, "Name": "book_"+str(id_start)}), # json field
+            "{}_{}".format(partition_name, id_start) if partition_name is not None else "description_{}".format(id_start), # varchar field
+            json.dumps({"dynamic_field": id_start}), # no field matches this value, this value will be put into dynamic field
+        ])
+        id_start = id_start + 1
+    data = [header] + rows
+    with open(path, "w") as f:
+        writer = csv.writer(f, delimiter=sep)
+        for row in data:
+            writer.writerow(row)
+
+
+def bulk_insert_rowbased(row_count_per_file, file_count, partition_name = None):
+    # make sure the files folder is created
+    os.makedirs(name=LOCAL_FILES_PATH, exist_ok=True)
+
+    task_ids = []
+    for i in range(file_count):
+        data_folder = os.path.join(LOCAL_FILES_PATH, "csv_{}".format(i))
+        os.makedirs(name=data_folder, exist_ok=True)
+        file_path = os.path.join(data_folder, "csv_{}.csv".format(i))
+        print("Generate csv file:", file_path)
+        sep ="\t"
+        gen_csv_rowbased(row_count_per_file, file_path, partition_name, sep)
+
+        print("Import csv file:", file_path)
+        task_id = utility.do_bulk_insert(collection_name=_COLLECTION_NAME,
+                                         partition_name=partition_name,
+                                         files=[file_path],
+                                         sep=sep)
+        task_ids.append(task_id)
+
+    return wait_tasks_competed(task_ids)
+
+# Wait all bulkinsert tasks to be a certain state
+# return the states of all the tasks, including failed task
+def wait_tasks_to_state(task_ids, state_code):
+    wait_ids = task_ids
+    states = []
+    while True:
+        time.sleep(2)
+        temp_ids = []
+        for id in wait_ids:
+            state = utility.get_bulk_insert_state(task_id=id)
+            if state.state == BulkInsertState.ImportFailed or state.state == BulkInsertState.ImportFailedAndCleaned:
+                print(state)
+                print("The task", state.task_id, "failed, reason:", state.failed_reason)
+                continue
+
+            if state.state >= state_code:
+                states.append(state)
+                continue
+
+            temp_ids.append(id)
+
+        wait_ids = temp_ids
+        if len(wait_ids) == 0:
+            break
+        print("Wait {} tasks to be state: {}. Next round check".format(len(wait_ids), BulkInsertState.state_2_name.get(state_code, "unknown")))
+
+    return states
+
+# If the state of bulkinsert task is BulkInsertState.ImportCompleted, that means the data file has been parsed and data has been persisted,
+# some segments have been created and waiting for index.
+# ImportCompleted state doesn't mean the data is queryable, to query the data, you need to wait until the segment is
+# indexed successfully and loaded into memory.
+def wait_tasks_competed(task_ids):
+    print("=========================================================================================================")
+    states = wait_tasks_to_state(task_ids, BulkInsertState.ImportCompleted)
+    complete_count = 0
+    for state in states:
+        if state.state == BulkInsertState.ImportCompleted:
+            complete_count = complete_count + 1
+        # print(state)
+        # if you want to get the auto-generated primary keys, use state.ids to fetch
+        # print("Auto-generated ids:", state.ids)
+
+    print("{} of {} tasks have successfully generated segments, able to be compacted and indexed as normal".format(complete_count, len(task_ids)))
+    print("=========================================================================================================\n")
+    return states
+
+# List all bulkinsert tasks, including pending tasks, working tasks and finished tasks.
+# the parameter 'limit' is: how many latest tasks should be returned, if the limit<=0, all the tasks will be returned
+def list_all_bulk_insert_tasks(collection_name=_COLLECTION_NAME, limit=0):
+    tasks = utility.list_bulk_insert_tasks(limit=limit, collection_name=collection_name)
+    print("=========================================================================================================")
+    print("List bulkinsert tasks with limit", limit)
+    pending = 0
+    started = 0
+    persisted = 0
+    completed = 0
+    failed = 0
+    for task in tasks:
+        print(task)
+        if task.state == BulkInsertState.ImportPending:
+            pending = pending + 1
+        elif task.state == BulkInsertState.ImportStarted:
+            started = started + 1
+        elif task.state == BulkInsertState.ImportPersisted:
+            persisted = persisted + 1
+        elif task.state == BulkInsertState.ImportCompleted:
+            completed = completed + 1
+        elif task.state == BulkInsertState.ImportFailed:
+            failed = failed + 1
+    print("There are {} bulkinsert tasks: {} pending, {} started, {} persisted, {} completed, {} failed"
+          .format(len(tasks), pending, started, persisted, completed, failed))
+    print("=========================================================================================================\n")
+
+# Get collection row count.
+def get_entity_num(collection):
+    print("=========================================================================================================")
+    print("The number of entity:", collection.num_entities)
+
+# Specify an index type
+def create_index(collection):
+    print("Start Creating index IVF_FLAT")
+    index = {
+        "index_type": "IVF_FLAT",
+        "metric_type": "L2",
+        "params": {"nlist": 128},
+    }
+    collection.create_index(_VECTOR_FIELD_NAME, index)
+
+# Load collection data into memory. If collection is not loaded, the search() and query() methods will return error.
+def load_collection(collection):
+    collection.load()
+
+# Release collection data to free memory.
+def release_collection(collection):
+    collection.release()
+
+# ANN search
+def search(collection, search_vector, expr = None, consistency_level = "Eventually"):
+    search_param = {
+        "expr": expr,
+        "data": [search_vector],
+        "anns_field": _VECTOR_FIELD_NAME,
+        "param": {"metric_type": "L2", "params": {"nprobe": 10}},
+        "limit": 5,
+        "output_fields": [_JSON_FIELD_NAME, _VARCHAR_FIELD_NAME, _DYNAMIC_FIELD_NAME],
+        "consistency_level": consistency_level,
+    }
+    print("search..." if expr is None else "hybrid search...")
+    results = collection.search(**search_param)
+    print("=========================================================================================================")
+    result = results[0]
+    for j, res in enumerate(result):
+        print(f"\ttop{j}: {res}")
+    print("\thits count:", len(result))
+    print("=========================================================================================================\n")
+
+# Delete entities
+def delete(collection, ids):
+    print("=========================================================================================================\n")
+    print("Delete these entities:", ids)
+    expr = _ID_FIELD_NAME + " in " + str(ids)
+    collection.delete(expr=expr)
+    print("=========================================================================================================\n")
+
+# Retrieve entities
+def retrieve(collection, ids):
+    print("=========================================================================================================")
+    print("Retrieve these entities:", ids)
+    expr = _ID_FIELD_NAME + " in " + str(ids)
+    result = collection.query(expr=expr, output_fields=[_JSON_FIELD_NAME, _VARCHAR_FIELD_NAME, _VECTOR_FIELD_NAME, _DYNAMIC_FIELD_NAME])
+    for item in result:
+        print(item)
+    print("=========================================================================================================\n")
+    return result
+
+
+def main(has_partition_key: bool):
+    # create a connection
+    create_connection()
+
+    # drop collection if the collection exists
+    if has_collection():
+        drop_collection()
+
+    # create collection
+    collection = create_collection(has_partition_key)
+
+    # specify an index type
+    create_index(collection)
+
+    print("Load data to memory")
+    # load data to memory
+    load_collection(collection)
+    print("Load data to memory completed")
+    # show collections
+    print("Show collections")
+    list_collections()
+
+    # do bulk_insert, wait all tasks finish persisting
+    row_count_per_file = 10
+    if has_partition_key:
+        # automatically partitioning
+        bulk_insert_rowbased(row_count_per_file=row_count_per_file, file_count=2)
+    else:
+        # bulklinsert into default partition
+        bulk_insert_rowbased(row_count_per_file=row_count_per_file, file_count=1)
+    print("Bulk insert completed")
+
+    # list all tasks
+    list_all_bulk_insert_tasks()
+
+    # get the number of entities
+    get_entity_num(collection)
+
+    print("Waiting index complete and refresh segments list to load...")
+    utility.wait_for_index_building_complete(_COLLECTION_NAME)
+    collection.load(_refresh = True)
+
+    # pick some entities
+    pick_ids = [50, row_count_per_file + 99]
+    id_vectors = retrieve(collection, pick_ids)
+
+    # search the picked entities, they are in result at the top0
+    for id_vector in id_vectors:
+        id = id_vector[_ID_FIELD_NAME]
+        vector = id_vector[_VECTOR_FIELD_NAME]
+        print("Search id:", id, ", compare this id to the top0 of search result, they are equal")
+        search(collection, vector)
+
+    # delete the picked entities
+    delete(collection, pick_ids)
+
+    # search the deleted entities, they are not in result anymore
+    for id_vector in id_vectors:
+        id = id_vector[_ID_FIELD_NAME]
+        vector = id_vector[_VECTOR_FIELD_NAME]
+        print("Search id:", id, ", compare this id to the top0 result, they are not equal since the id has been deleted")
+        # here we use Strong consistency level to do search, because we need to make sure the delete operation is applied
+        search(collection, vector, consistency_level="Strong")
+
+    # search by filtering the varchar field
+    vector = [round(random.random(), 6) for _ in range(_DIM)]
+    ret = search(collection, vector)
+    print(ret)
+    # release memory
+    # release_collection(collection)
+
+    # drop collection
+    # drop_collection()
+
+
+if __name__ == '__main__':
+    # change this value if you want to test bulkinert with partition key
+    # Note: bulkinsert supports partition key from Milvus v2.2.12
+    has_partition_key = False
+    main(has_partition_key)