The English version of this is here.
nlp4j-llm-embeddings-e5
Multilingual-E5-large の Embedding をHTTPサーバー化して使うというのが本コンポーネントの趣旨です。
0. 前提
Docker の利用環境が構築されているのが前提です。
5GB ほどのイメージがダウンロードされます。
1. 導入
docker pull コマンドでイメージを取得します。
docker pull oyahiroki/nlp4j-llm-embeddings-e5:1.0.0.0
結果の例は以下のようになります。
>docker pull oyahiroki/nlp4j-llm-embeddings-e5:1.0.0.0
1.0.0.0: Pulling from oyahiroki/nlp4j-llm-embeddings-e5
e17464c8c9fb: Download complete
f344618db07e: Download complete
fd674058ff8f: Download complete
f7fe7c1564c1: Download complete
4f4fb700ef54: Download complete
4036d1c9d769: Download complete
a1235d039a7d: Download complete
22318a820e1b: Download complete
2ecbc2a20de9: Download complete
Digest: sha256:7d21fbdb572e1b99242179b4e99ff7cde6876febeb4f2ce981fccd39afd98f04
Status: Downloaded newer image for oyahiroki/nlp4j-llm-embeddings-e5:1.0.0.0
docker.io/oyahiroki/nlp4j-llm-embeddings-e5:1.0.0.0
イメージについては以下を参照してください
2. 実行
以下のコマンドで実行します。
お好みでオプションを追加してください。
初期化プロセスのなかで Multilingual-E5-large のモデルもダウンロードされるので、さらに5GB ほどのディスクが消費されます。モデルのダウンロードをともなう初期化プロセスは最初の起動時のみなので、２回目以降の起動時には発生しません。
docker run -d --name nlp4j-llm-embeddings-e5 -p 8888:8888 oyahiroki/nlp4j-llm-embeddings-e5:1.0.0.0
Docker Desktopでは以下のように見えます。
５分ほど待って初期化プロセスが終わると、CPUの使用率が下がります。
3. 動作確認
Curl コマンドで REST API にテキストを送信することでベクトル化が可能です。
curl http://127.0.0.1:8888/?text=%E3%81%93%E3%82%8C%E3%81%AF%E3%83%86%E3%82%B9%E3%83%88%E3%81%A7%E3%81%99%E3%80%82
以下のような結果が返ればOKです。
{"message": "ok", "time": "2025-01-04T05:49:20", "text": "\u3053\u308c\u306f\u30c6\u30b9\u30c8\u3067\u3059\u3002", "embeddings": [0.04231279715895653, -0.0035561532713472843, -0.014567538164556026, -0.05735648050904274, 0.0339919812977314, -0.023742269724607468, 0.006811155471950769, 0.0830383449792862, 0.041998427361249924, -0.009564925916492939, 0.021144241094589233, 0.03838073089718819, -0.03303127363324165, -0.00032863442902453244, -0.03512480482459068, -0.016866829246282578, -0.013489240780472755, 0.01558159664273262, 0.028106780722737312, -0.004634382203221321, 0.01627136766910553, -0.010889444500207901, -0.04045294225215912, -0.02741953916847706, -0.026021577417850494, 0.008194061927497387, -0.05707988515496254, -0.028787249699234962, -0.009535274468362331, -0.04099910333752632, 0.012657918967306614, 0.021215656772255898, -0.046045996248722076, -0.042320773005485535, -0.027079155668616295, 0.032212063670158386, 0.033910397440195084, 0.02527390979230404, -0.011053706519305706, 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4. 利用
Python で利用したい場合は以下のようになります。
import requests
import json
import time
# リクエストを送信するURL
url = "http://127.0.0.1:8888/"
params = {"text": "これはテストです。"}
try:
# リクエスト送信前のタイムスタンプ
start_time = time.time()
# GETリクエストを送信
response = requests.get(url, params=params)
# リクエスト送信後のタイムスタンプ
end_time = time.time()
# レスポンスのステータスコードを確認
response.raise_for_status()
# JSONレスポンスをパース
data = response.json()
# 処理時間を計算
elapsed_time = end_time - start_time
# 結果を表示
print("Message:", data["message"])
print("Time:", data["time"])
print("Text:", data["text"])
print("Embeddings (first 5):", data["embeddings"][:5]) # 埋め込みの最初の5要素を表示
print(f"リクエストからレスポンス受信までの時間: {elapsed_time:.4f} 秒")
except requests.exceptions.RequestException as e:
print(f"HTTPリクエスト中にエラーが発生しました: {e}")
except json.JSONDecodeError as e:
print(f"JSONのパース中にエラーが発生しました: {e}")
REST API なので Java などのほかの言語からも利用可能です。
Java向けには専用のライブラリも用意してあります。
String text = "今日はとてもいい天気です。";
String endPoint = "http://localhost:8888/";
EmbeddingServiceViaHttp nlp = new EmbeddingServiceViaHttp(endPoint);
NlpServiceResponse res = nlp.process(text);
EmbeddingResponse r = (new Gson()).fromJson(res.getOriginalResponseBody(), EmbeddingResponse.class);
System.err.println(Arrays.toString(r.getEmbeddings()));
[EOF]
以上.