update docs

Signed-off-by: AnthonyTsu1984 <[email protected]>

API_Reference/pymilvus/v2.4.x/MilvusClient/Collections/DataType.md

@@ -28,9 +28,6 @@ Sets the data type to **Float**.
 - DOUBLE = 11
 Sets the data type to **Double**.
 
-- STRING = 20
-Sets the data type to **String**.
-
 - VARCHAR = 21
 Sets the data type to **Varchar**.
 
@@ -59,5 +56,6 @@ Sets the data type to **Float Vector**.
     Sets the data type to **Sparse Vector**.
 
 - UNKNOWN = 999
-Sets the data type to **Unknown**.
 
+  Sets the data type to **Unknown**.
+

API_Reference/pymilvus/v2.4.x/Rerankers/JinaRerankFunction/JinaRerankFunction.md

@@ -12,7 +12,7 @@ Constructs a JinaRerankFunction for common use cases.
 
 ```python
 JinaRerankFunction(
-    model_name: str = "jina-reranker-v1-base-en",
+    model_name: str = "jina-reranker-v2-base-multilingual",
     api_key: Optional[str] = None
 )
 ```
@@ -21,7 +21,7 @@ JinaRerankFunction(
 
 - **model_name** (*string*)
 
-    The name of the Jina AI reranker model to use for encoding. If you leave this parameter unspecified, `jina-reranker-v1-base-en` will be used. For a list of available models, refer to [Jina AI Rerankers](https://jina.ai/reranker/).
+    The name of the Jina AI reranker model to use for encoding. If you leave this parameter unspecified, `jina-reranker-v2-base-multilingual` will be used. For a list of available models, refer to [Jina AI Rerankers](https://jina.ai/reranker/).
 
 - **api_key** (*string*)
 
@@ -33,9 +33,9 @@ JinaRerankFunction(
 from pymilvus.model.reranker import JinaRerankFunction
 
 jina_rf = JinaRerankFunction(
-    model_name="jina-reranker-v1-base-en", # Defaults to `jina-reranker-v1-base-en`
+    model_name="jina-reranker-v2-base-multilingual", # Defaults to `jina-reranker-v2-base-multilingual`
     api_key="YOUR_JINAAI_API_KEY"
 )
 ```
 
-<DocCardList />
+<DocCardList />

preview/CONTRIBUTING.md

@@ -2,7 +2,7 @@
 
 The Milvus docs are open-source just like the database itself and welcome contributions from everyone in the Milvus community.
 
-> This repository is for Milvus technical documentation update and maintenance. Visit [Milvus.io](milvus.io) or [Web content repo](https://github.com/milvus-io/web-content) for fully rendered documents.
+> This repository is for Milvus technical documentation update and maintenance. Visit [Milvus.io](https://milvus.io/docs) or [Web content repo](https://github.com/milvus-io/web-content) for fully rendered documents.
 
 ## What contributions can I make?
 
@@ -42,7 +42,7 @@ For detailed information on this workflow, see [Make Your First Contribution](ht
 
 ![Folders](assets/folder-structure.png)
 
-- [Languages](#languages)
+- [Languages](#language)
 - [Pages](#pages)
 - [Sidebar](#sidebar)
 

preview/README.md

@@ -2,7 +2,7 @@
 
 Welcome to Milvus documentation!
 
-This repository contains technical documentation for [Milvus](https://github.com/milvus-io/milvus), the world's most advanced open-source vector database. Visit [Milvus.io](milvus.io) or [Web content repo](https://github.com/milvus-io/web-content) for fully rendered documents.
+This repository contains technical documentation for [Milvus](https://github.com/milvus-io/milvus), the world's most advanced open-source vector database. Visit [Milvus.io](https://milvus.io/docs) or [Web content repo](https://github.com/milvus-io/web-content) for fully rendered documents.
 
 Each branch corresponds to a Milvus release by name. We've set the branch of the latest Milvus release as the default branch. For documentation of a different Milvus release, switch to the corresponding branch.
 

preview/Variables.json

@@ -1,13 +1,24 @@
 {
-  "milvus_release_version": "2.2.3",
-  "milvus_release_tag": "2.2.3",
+  "milvus_release_version": "2.4.5",
+  "milvus_release_tag": "2.4.5",
   "milvus_deb_name": "milvus_2.2.0-1_amd64",
   "milvus_rpm_name": "milvus-2.2.0-1.el7.x86_64",
-  "milvus_python_sdk_version": "2.2.2",
-  "milvus_node_sdk_version": "2.2.x",
-  "milvus_go_sdk_version": "2.2.0",
-  "milvus_java_sdk_version": "2.2.1",
-  "milvus_operator_version": "0.7.7",
-  "milvus_image": "2.2.2",
-  "attu_release": "2.1.1"
+  "milvus_python_sdk_version": "2.4.x",
+  "milvus_python_sdk_real_version": "2.4.4",
+  "milvus_node_sdk_version": "2.4.x",
+  "milvus_node_sdk_real_version": "v2.4.3",
+  "milvus_go_sdk_version": "2.3.x",
+  "milvus_go_sdk_real_version": "2.4.0",
+  "milvus_java_sdk_version": "2.4.x",
+  "milvus_java_sdk_real_version": "2.4.1",
+  "milvus_csharp_sdk_version": "2.2.x",
+  "milvus_csharp_sdk_real_version": "2.2.14", 
+  "milvus_restful_sdk_version": "2.4.x",
+  "milvus_restful_sdk_real_version": "2.4.1",
+  "milvus_operator_version": "0.9.17",
+  "milvus_helm_chart_version": "4.1.24",
+  "milvus_image": "2.4.1",
+  "attu_release": "2.3.10",
+  "milvus_backup_release": "0.4.12",
+  "birdwatcher_release": "1.0.3"
 }

preview/assets/hello_milvus.py

@@ -0,0 +1,246 @@
+# hello_milvus.py demonstrates the basic operations of PyMilvus, a Python SDK of Milvus.
+# 1. connect to Milvus
+# 2. create collection
+# 3. insert data
+# 4. create index
+# 5. search, query, and hybrid search on entities
+# 6. delete entities by PK
+# 7. drop collection
+import time
+
+import numpy as np
+import string
+import random
+
+from pymilvus import MilvusClient, DataType
+
+fmt = "\n=== {:30} ===\n"
+search_latency_fmt = "search latency = {:.4f}s"
+num_entities, dim = 3000, 8
+
+#################################################################################
+# 1. connect to Milvus
+# Add a new connection alias `default` for Milvus server in `localhost:19530`
+# Actually the "default" alias is a buildin in PyMilvus.
+# If the address of Milvus is the same as `localhost:19530`, you can omit all
+# parameters and call the method as: `connections.connect()`.
+#
+# Note: the `using` parameter of the following methods is default to "default".
+print(fmt.format("start connecting to Milvus"))
+client = MilvusClient(uri="http://localhost:19530") # Replace with your Milvus server address
+
+has = client.has_collection("hello_milvus")
+print(f"Does collection hello_milvus exist in Milvus: {has}")
+
+#################################################################################
+# 2. create collection
+# We're going to create a collection with 3 fields.
+# +-+------------+------------+------------------+------------------------------+
+# | | field name | field type | other attributes |       field description      |
+# +-+------------+------------+------------------+------------------------------+
+# |1|    "pk"    |   VarChar  |  is_primary=True |      "primary field"         |
+# | |            |            |   auto_id=False  |                              |
+# +-+------------+------------+------------------+------------------------------+
+# |2|  "random"  |    Double  |                  |      "a double field"        |
+# +-+------------+------------+------------------+------------------------------+
+# |3|"embeddings"| FloatVector|     dim=8        |  "float vector with dim 8"   |
+# +-+------------+------------+------------------+------------------------------+
+
+schema = client.create_schema(
+    auto_id=False,
+    enable_dynamic_fields=True,
+    description="hello_milvus is the simplest demo to introduce the APIs",
+)
+
+schema.add_field(field_name="pk", datatype=DataType.VARCHAR, is_primary=True, max_length=100)
+schema.add_field(field_name="random", datatype=DataType.DOUBLE)
+schema.add_field(field_name="embeddings", datatype=DataType.FLOAT_VECTOR, dim=dim)
+
+print(fmt.format("Create collection `hello_milvus`"))
+client.create_collection(
+    collection_name="hello_milvus", 
+    schema=schema,
+    consistency_level="Strong"
+)
+
+################################################################################
+# 3. insert data
+# We are going to insert 3000 rows of data into `hello_milvus`
+# Data to be inserted must be organized in fields.
+#
+# The insert() method returns:
+# - either automatically generated primary keys by Milvus if auto_id=True in the schema;
+# - or the existing primary key field from the entities if auto_id=False in the schema.
+
+print(fmt.format("Start inserting entities"))
+
+def generate_random_string(length):
+    return ''.join(random.choice(string.ascii_letters + string.digits) for _ in range(length))
+
+def generate_random_entities(num_entities, dim):
+    entities = []
+    for _ in range(num_entities):
+        pk = generate_random_string(10)  # Generate a random primary key string of length 10
+        random_value = random.random()  # Generate a random double value
+        embeddings = np.random.rand(dim).tolist()  # Generate a random float vector of dimension 'dim'
+        entities.append({"pk": pk, "random": random_value, "embeddings": embeddings})
+    return entities
+
+entities = generate_random_entities(num_entities, dim)
+
+insert_result = client.insert(
+    collection_name="hello_milvus",
+    data=entities,
+)
+
+print(f"Number of entities in Milvus: {insert_result['insert_count']}")  # check the num_entities
+
+################################################################################
+# 4. create index
+# We are going to create an IVF_FLAT index for hello_milvus collection.
+# create_index() can only be applied to `FloatVector` and `BinaryVector` fields.
+print(fmt.format("Start Creating index IVF_FLAT"))
+
+index_params = client.prepare_index_params()
+
+index_params.add_index(
+    field_name="pk"
+)
+
+index_params.add_index(
+    field_name="embeddings", 
+    index_type="IVF_FLAT",
+    metric_type="L2",
+    params={"nlist": 128}
+)
+
+client.create_index(
+    collection_name="hello_milvus",
+    index_params=index_params
+)
+
+################################################################################
+# 5. search, query, and hybrid search
+# After data were inserted into Milvus and indexed, you can perform:
+# - search based on vector similarity
+# - query based on scalar filtering(boolean, int, etc.)
+# - hybrid search based on vector similarity and scalar filtering.
+#
+
+# Before conducting a search or a query, you need to load the data in `hello_milvus` into memory.
+print(fmt.format("Start loading"))
+client.load_collection("hello_milvus")
+
+# -----------------------------------------------------------------------------
+# search based on vector similarity
+print(fmt.format("Start searching based on vector similarity"))
+last_entity = entities[-1]  # Get the last entity
+vectors_to_search = [last_entity["embeddings"]]  # Extract the embeddings vector and put it in a list
+search_params = {
+    "metric_type": "L2",
+    "params": {"nprobe": 10},
+}
+
+start_time = time.time()
+result = client.search(
+    collection_name="hello_milvus",
+    data=vectors_to_search, 
+    anns_field="embeddings", 
+    search_params=search_params, 
+    limit=3, 
+    output_fields=["random"]
+)
+end_time = time.time()
+
+for hits in result:
+    for hit in hits:
+        print(f"hit: {hit}, random field: {hit.get('entity').get('random')}")
+print(search_latency_fmt.format(end_time - start_time))
+
+# -----------------------------------------------------------------------------
+# query based on scalar filtering(boolean, int, etc.)
+print(fmt.format("Start querying with `random > 0.5`"))
+
+start_time = time.time()
+result = client.query(
+    collection_name="hello_milvus",
+    filter="random > 0.5", 
+    output_fields=["random", "embeddings"]
+)
+end_time = time.time()
+
+print(f"query result:\n-{result[0]}")
+print(search_latency_fmt.format(end_time - start_time))
+
+# -----------------------------------------------------------------------------
+# pagination
+r1 = client.query(
+    collection_name="hello_milvus",
+    filter="random > 0.5", 
+    limit=4, 
+    output_fields=["random"]
+)
+r2 = client.query(
+    collection_name="hello_milvus",
+    filter="random > 0.5", 
+    offset=1, 
+    limit=3, 
+    output_fields=["random"]
+)
+print(f"query pagination(limit=4):\n\t{r1}")
+print(f"query pagination(offset=1, limit=3):\n\t{r2}")
+
+
+# -----------------------------------------------------------------------------
+# filtered search
+print(fmt.format("Start filtered searching with `random > 0.5`"))
+
+start_time = time.time()
+result = client.search(
+    collection_name="hello_milvus",
+    data=vectors_to_search, 
+    anns_field="embeddings", 
+    search_params=search_params, 
+    limit=3, 
+    filter="random > 0.5", 
+    output_fields=["random"]
+)
+end_time = time.time()
+
+for hits in result:
+    for hit in hits:
+        print(f"hit: {hit}, random field: {hit.get('entity').get('random')}")
+print(search_latency_fmt.format(end_time - start_time))
+
+###############################################################################
+# 6. delete entities by PK
+# You can delete entities by their PK values using boolean expressions.
+ids = [entity["pk"] for entity in entities]
+
+expr = f'pk in ["{ids[0]}", "{ids[1]}"]'
+print(fmt.format(f"Start deleting with expr `{expr}`"))
+
+result = client.query(
+    collection_name="hello_milvus",
+    filter=expr, 
+    output_fields=["random", "embeddings"]
+)
+print(f"query before delete by expr=`{expr}` -> result: \n-{result[0]}\n-{result[1]}\n")
+
+client.delete(
+    collection_name="hello_milvus",
+    filter=expr
+)
+
+result = client.query(
+    collection_name="hello_milvus",
+    filter=expr, 
+    output_fields=["random", "embeddings"]
+)
+print(f"query after delete by expr=`{expr}` -> result: {result}\n")
+
+###############################################################################
+# 7. drop collection
+# Finally, drop the hello_milvus collection
+print(fmt.format("Drop collection `hello_milvus`"))
+client.drop_collection("hello_milvus")