AI/dify
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enhance: use urllib join instead of fstring (#9549)

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Byron.wang 2024-10-21 19:04:28 +08:00 committed by GitHub
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commit 37fea072bc
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4 changed files with 7 additions and 5 deletions
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2
README.md
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@ -168,7 +168,7 @@ Star Dify on GitHub and be instantly notified of new releases.
> Before installing Dify, make sure your machine meets the following minimum system requirements: > Before installing Dify, make sure your machine meets the following minimum system requirements:
> >
>- CPU >= 2 Core >- CPU >= 2 Core
>- RAM >= 4GB >- RAM >= 4 GiB
</br> </br>

2
README_CN.md
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@ -174,7 +174,7 @@ Dify 是一个开源的 LLM 应用开发平台。其直观的界面结合了 AI
在安装 Dify 之前，请确保您的机器满足以下最低系统要求： 在安装 Dify 之前，请确保您的机器满足以下最低系统要求：
- CPU >= 2 Core - CPU >= 2 Core
- RAM >= 4GB - RAM >= 4 GiB
### 快速启动 ### 快速启动

4
api/services/enterprise/base.py
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@ -1,4 +1,5 @@
import os import os
from urllib.parse import urljoin
import requests import requests
@ -15,8 +16,7 @@ class EnterpriseRequest:
@classmethod @classmethod
def send_request(cls, method, endpoint, json=None, params=None): def send_request(cls, method, endpoint, json=None, params=None):
headers = {"Content-Type": "application/json", "Enterprise-Api-Secret-Key": cls.secret_key} headers = {"Content-Type": "application/json", "Enterprise-Api-Secret-Key": cls.secret_key}
url = urljoin(cls.base_url, endpoint)
url = f"{cls.base_url}{endpoint}"
response = requests.request(method, url, json=json, params=params, headers=headers, proxies=cls.proxies) response = requests.request(method, url, json=json, params=params, headers=headers, proxies=cls.proxies)
return response.json() return response.json()

4
api/tests/unit_tests/core/model_runtime/model_providers/wenxin/test_text_embedding.py
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@ -1,3 +1,5 @@
import string
import numpy as np import numpy as np
from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
@ -31,7 +33,7 @@ def test_max_chunks():
max_chunks = embedding_model._get_max_chunks(model, credentials) max_chunks = embedding_model._get_max_chunks(model, credentials)
embedding_model._create_text_embedding = _create_text_embedding embedding_model._create_text_embedding = _create_text_embedding
texts = ["0123456789" for i in range(0, max_chunks * 2)] texts = [string.digits for i in range(0, max_chunks * 2)]
result: TextEmbeddingResult = embedding_model.invoke(model, credentials, texts, "test") result: TextEmbeddingResult = embedding_model.invoke(model, credentials, texts, "test")
assert len(result.embeddings) == max_chunks * 2 assert len(result.embeddings) == max_chunks * 2