Vector Retrieval Service for Milvus:Ekstraksi kata kunci

REST API

REST API

Kirim permintaan POST ke titik akhir AI keywords:

POST /v2/vectordb/ai/keywords
Content-Type: application/json

{
  "model_name": "<model_name>",
  "texts": ["<text_or_media_url>"],
  "params": {"max_keywords": 10, "target_language": "zh"}
}

Collection Function (Python SDK)

Collection Function (Python SDK)

Tentukan ekstraksi kata kunci sebagai Collection Function dalam skema. Fungsi ini dipicu secara otomatis saat penyisipan data dan menulis hasil ke bidang output yang ditentukan:

schema = MilvusClient.create_schema(auto_id=True, enable_dynamic_field=False)
schema.add_field("id", DataType.INT64, is_primary=True)
schema.add_field("content", DataType.VARCHAR, max_length=8192)
schema.add_field("keywords", DataType.JSON)
schema.add_field("dummy_vector", DataType.FLOAT_VECTOR, dim=2)
schema.add_function(
    Function(
        name="extract_keywords",
        function_type=texttransform_function_type(),
        input_field_names=["content"],
        output_field_names=["keywords"],
        params={
            "provider": "aliyun_milvus",
            "model_name": "<model_name>",
            "task": "ai_keywords",
            "max_keywords": "5",
            "target_language": "en",
            "temperature": "0",
        },
    )
)

REST API

REST API

#!/usr/bin/env bash
set -euo pipefail

MILVUS_REST_BASE_URL="http://c-xxxx.milvus.aliyuncs.com:19530"
MILVUS_AUTH_TOKEN="<yourUsername>:<yourPassword>"

post_json() {
  local path="$1"
  local body="$2"
  curl -X POST \
    "$MILVUS_REST_BASE_URL$path" \
    -H "Authorization: Bearer $MILVUS_AUTH_TOKEN" \
    -H "Content-Type: application/json" \
    -d "$body"
}

BODY=$(cat <<JSON
{
  "model_name": "qwen3.7-max",
  "texts": ["Milvus is an open-source vector database for high-performance approximate nearest neighbor search in RAG, recommendation, and image search."],
  "params": {"max_keywords": 5, "target_language": "en", "temperature": 0}
}
JSON
)
RESPONSE_BODY="$(post_json "/v2/vectordb/ai/keywords" "$BODY")"
if command -v jq >/dev/null 2>&1; then
  echo "$RESPONSE_BODY" | jq .
  [ "$(echo "$RESPONSE_BODY" | jq -r '.code // -1')" = "0" ] || exit 1
else
  echo "$RESPONSE_BODY"
fi

Python (Collection Function)

Python (Collection Function)

Contoh berikut menggunakan fungsi utilitas bersama (texttransform_function_type dan run_texttransform_example) yang menangani pembuatan skema, pendaftaran fungsi, penyisipan data, dan kueri hasil. Contoh 2 dan 3 menggunakan utilitas yang sama.

from __future__ import annotations

from typing import Any

from pymilvus import DataType, Function, FunctionType, MilvusClient

MILVUS_URI = "http://c-xxxx.milvus.aliyuncs.com:19530"
MILVUS_TOKEN = "<yourUsername>:<yourPassword>"

DUMMY_VECTOR_DIM = 2
TEXTTRANSFORM_FUNCTION_TYPE = 9

def texttransform_function_type() -> Any:
    for type_name in ("TEXTTRANSFORM", "TEXT_TRANSFORM", "TextTransform"):
        function_type = getattr(FunctionType, type_name, None)
        if function_type is not None:
            return function_type
    # Alibaba Cloud Milvus provides TEXTTRANSFORM as a managed extension (function type value 9);
    # some pymilvus versions do not have this enum member built in, while Function(...) validates through FunctionType(...).
    existing = getattr(FunctionType, "_value2member_map_", {}).get(TEXTTRANSFORM_FUNCTION_TYPE)
    if existing is not None:
        return existing
    extension = int.__new__(FunctionType, TEXTTRANSFORM_FUNCTION_TYPE)
    extension._name_ = "TEXTTRANSFORM"
    extension._value_ = TEXTTRANSFORM_FUNCTION_TYPE
    FunctionType._value2member_map_[TEXTTRANSFORM_FUNCTION_TYPE] = extension
    FunctionType._member_map_["TEXTTRANSFORM"] = extension
    return extension

def add_id(schema: Any) -> None:
    schema.add_field("id", DataType.INT64, is_primary=True)

def add_dummy_vector(schema: Any) -> None:
    schema.add_field("dummy_vector", DataType.FLOAT_VECTOR, dim=DUMMY_VECTOR_DIM)

def run_texttransform_example(*, client, collection_name, input_fields, output_field, function_name, function_params, rows) -> None:
    if client.has_collection(collection_name):
        client.drop_collection(collection_name)
    schema = MilvusClient.create_schema(auto_id=True, enable_dynamic_field=False)
    add_id(schema)
    for name, data_type, max_length in input_fields:
        field_params = {"max_length": max_length} if max_length is not None else {}
        schema.add_field(name, data_type, **field_params)
    output_name, output_data_type, output_max_length = output_field
    output_params = {"max_length": output_max_length} if output_max_length is not None else {}
    schema.add_field(output_name, output_data_type, **output_params)
    add_dummy_vector(schema)
    schema.add_function(
        Function(
            name=function_name,
            function_type=texttransform_function_type(),
            input_field_names=[name for name, _, _ in input_fields],
            output_field_names=[output_name],
            params=function_params,
        )
    )
    index_params = client.prepare_index_params()
    index_params.add_index(field_name="dummy_vector", index_type="AUTOINDEX", metric_type="COSINE")
    client.create_collection(collection_name=collection_name, schema=schema, index_params=index_params)
    client.insert(collection_name, rows)
    client.flush(collection_name)
    fields = [name for name, _, _ in input_fields] + [output_name]
    for row in client.query(collection_name, filter="", output_fields=fields, limit=len(rows)):
        print(row)

client = MilvusClient(uri=MILVUS_URI, token=MILVUS_TOKEN)

run_texttransform_example(
    client=client,
    collection_name="simple_ai_keywords_text",
    input_fields=[("content", DataType.VARCHAR, 8192)],
    output_field=("keywords", DataType.JSON, None),
    function_name="extract_keywords",
    function_params={"provider": "aliyun_milvus", "model_name": "qwen3.7-max", "task": "ai_keywords", "max_keywords": "5", "target_language": "en", "temperature": "0"},
    rows=[{"content": "Milvus is an open-source vector database for high-performance approximate nearest neighbor search in RAG, recommendation, and image search.", "dummy_vector": [0.1, 0.2]}],
)

Hasil yang diharapkan: Bidang keywords mengembalikan {"keywords":["Milvus","vector database","approximate nearest neighbor search","RAG","open-source"]} (kata kunci dalam bahasa Inggris ketika target_language=en).

REST API

REST API

#!/usr/bin/env bash
set -euo pipefail

MILVUS_REST_BASE_URL="http://c-xxxx.milvus.aliyuncs.com:19530"
MILVUS_AUTH_TOKEN="<yourUsername>:<yourPassword>"

post_json() {
  local path="$1"
  local body="$2"
  curl -X POST \
    "$MILVUS_REST_BASE_URL$path" \
    -H "Authorization: Bearer $MILVUS_AUTH_TOKEN" \
    -H "Content-Type: application/json" \
    -d "$body"
}

BODY=$(cat <<JSON
{"model_name":"qwen3.7-plus","texts":["https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20260415/hynnff/wan-video-edit-clothes.webp"],"params":{"media_type":"image","max_keywords":5,"target_language":"en","temperature":0,"timeout_sec":120,"enable_thinking":false}}
JSON
)
RESPONSE_BODY="$(post_json "/v2/vectordb/ai/keywords" "$BODY")"
if command -v jq >/dev/null 2>&1; then
  echo "$RESPONSE_BODY" | jq .
  [ "$(echo "$RESPONSE_BODY" | jq -r '.code // -1')" = "0" ] || exit 1
else
  echo "$RESPONSE_BODY"
fi

Python (Collection Function)

Python (Collection Function)

Contoh ini menggunakan fungsi utilitas yang sama seperti pada Contoh 1. Hanya bagian pemanggilan unik yang ditampilkan di bawah.

client = MilvusClient(uri=MILVUS_URI, token=MILVUS_TOKEN)

run_texttransform_example(
    client=client,
    collection_name="simple_ai_keywords_image",
    input_fields=[("image_url", DataType.VARCHAR, 4096)],
    output_field=("keywords", DataType.JSON, None),
    function_name="extract_image_keywords",
    function_params={"provider": "aliyun_milvus", "model_name": "qwen3.7-plus", "task": "ai_keywords", "media_type": "image", "max_keywords": "5", "target_language": "en", "temperature": "0"},
    rows=[{"image_url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20260415/hynnff/wan-video-edit-clothes.webp", "dummy_vector": [0.1, 0.2]}],
)

Hasil yang diharapkan: Bidang keywords mengembalikan hingga 5 kata kunci bahasa Inggris yang diekstraksi dari gambar dalam format {"keywords":[...]}.

REST API

REST API

#!/usr/bin/env bash
set -euo pipefail

MILVUS_REST_BASE_URL="http://c-xxxx.milvus.aliyuncs.com:19530"
MILVUS_AUTH_TOKEN="<yourUsername>:<yourPassword>"

post_json() {
  local path="$1"
  local body="$2"
  curl -X POST \
    "$MILVUS_REST_BASE_URL$path" \
    -H "Authorization: Bearer $MILVUS_AUTH_TOKEN" \
    -H "Content-Type: application/json" \
    -d "$body"
}

BODY=$(cat <<JSON
{"model_name":"qwen3.7-plus","texts":["https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20260409/dozxak/Wan_Video_Edit_33_1.mp4"],"params":{"media_type":"video","max_keywords":5,"target_language":"en","temperature":0}}
JSON
)
RESPONSE_BODY="$(post_json "/v2/vectordb/ai/keywords" "$BODY")"
if command -v jq >/dev/null 2>&1; then
  echo "$RESPONSE_BODY" | jq .
  [ "$(echo "$RESPONSE_BODY" | jq -r '.code // -1')" = "0" ] || exit 1
else
  echo "$RESPONSE_BODY"
fi

Python (Collection Function)

Python (Collection Function)

Contoh ini menggunakan fungsi utilitas yang sama seperti pada Contoh 1. Hanya bagian pemanggilan unik yang ditampilkan di bawah.

client = MilvusClient(uri=MILVUS_URI, token=MILVUS_TOKEN)

run_texttransform_example(
    client=client,
    collection_name="simple_ai_keywords_video",
    input_fields=[("video_url", DataType.VARCHAR, 4096)],
    output_field=("keywords", DataType.JSON, None),
    function_name="extract_video_keywords",
    function_params={"provider": "aliyun_milvus", "model_name": "qwen3.7-plus", "task": "ai_keywords", "media_type": "video", "max_keywords": "5", "target_language": "en", "temperature": "0"},
    rows=[{"video_url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20260409/dozxak/Wan_Video_Edit_33_1.mp4", "dummy_vector": [0.1, 0.2]}],
)

Hasil yang diharapkan: Bidang keywords mengembalikan hingga 5 kata kunci bahasa Inggris yang terkait dengan topik video, yang dapat ditulis ke bidang pengambilan video.