Özür dilerim, haklısınız. Daha anlamlı ve akıcı bir çeviri yapmaya çalışayım. İşte güncellenmiş çeviri:
**3. Prompt IDE**:
Komut istemlerini oluşturmak, model performansını karşılaştırmak ve sohbet tabanlı uygulamalara metin-konuşma gibi ek özellikler eklemek için kullanıcı dostu bir arayüz.
@ -150,8 +151,6 @@ Görsel bir arayüz üzerinde güçlü AI iş akışları oluşturun ve test edi
## Dify'ı Kullanma
- **Cloud </br>**
İşte verdiğiniz metnin Türkçe çevirisi, kod bloğu içinde:
-
Herkesin sıfır kurulumla denemesi için bir [Dify Cloud](https://dify.ai) hizmeti sunuyoruz. Bu hizmet, kendi kendine dağıtılan versiyonun tüm yeteneklerini sağlar ve sandbox planında 200 ücretsiz GPT-4 çağrısı içerir.
- **Dify Topluluk Sürümünü Kendi Sunucunuzda Barındırma</br>**
@ -177,8 +176,6 @@ GitHub'da Dify'a yıldız verin ve yeni sürümlerden anında haberdar olun.
>- RAM >= 4GB
</br>
İşte verdiğiniz metnin Türkçe çevirisi, kod bloğu içinde:
Dify sunucusunu başlatmanın en kolay yolu, [docker-compose.yml](docker/docker-compose.yaml) dosyamızı çalıştırmaktır. Kurulum komutunu çalıştırmadan önce, makinenizde [Docker](https://docs.docker.com/get-docker/) ve [Docker Compose](https://docs.docker.com/compose/install/)'un kurulu olduğundan emin olun:
zh_Hans:采样温度,控制输出的随机性,必须为正数取值范围是:(0.0,1.0],不能等于 0,默认值为 0.95 值越大,会使输出更随机,更具创造性;值越小,输出会更加稳定或确定建议您根据应用场景调整 top_p 或 temperature 参数,但不要同时调整两个参数。
en_US:Sampling temperature, controls the randomness of the output, must be a positive number. The value range is (0.0,1.0], which cannot be equal to 0. The default value is 0.95. The larger the value, the more random and creative the output will be; the smaller the value, The output will be more stable or certain. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
en_US:Another method of temperature sampling is called kernel sampling. The value range is (0.0, 1.0) open interval, which cannot be equal to 0 or 1. The default value is 0.7. The model considers the results with top_p probability mass tokens. For example 0.1 means The model decoder only considers tokens from the candidate set with the top 10% probability. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
en_US:When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
en_US:The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic.
- name:response_format
label:
zh_Hans:回复格式
en_US:Response Format
type:string
help:
zh_Hans:指定模型必须输出的格式
en_US:specifying the format that the model must output
Document metadata (required if doc_type is provided). Fields vary by doc_type:
For <code>book</code>:
- <code>title</code> Book title
- <code>language</code> Book language
- <code>author</code> Book author
- <code>publisher</code> Publisher name
- <code>publication_date</code> Publication date
- <code>isbn</code> ISBN number
- <code>category</code> Book category
For <code>web_page</code>:
- <code>title</code> Page title
- <code>url</code> Page URL
- <code>language</code> Page language
- <code>publish_date</code> Publish date
- <code>author/publisher</code> Author or publisher
- <code>topic/keywords</code> Topic or keywords
- <code>description</code> Page description
Please check [api/services/dataset_service.py](https://github.com/langgenius/dify/blob/main/api/services/dataset_service.py#L475) for more details on the fields required for each doc_type.
For doc_type "others", any valid JSON object is accepted
- <code>qa_model</code> Q&A Mode: Generates Q&A pairs for segmented documents and then embeds the questions
- <code>doc_type</code> Type of document (optional)
- <code>book</code> Book
Document records a book or publication
- <code>web_page</code> Web page
Document records web page content
- <code>paper</code> Academic paper/article
Document records academic paper or research article
- <code>social_media_post</code> Social media post
Content from social media posts
- <code>wikipedia_entry</code> Wikipedia entry
Content from Wikipedia entries
- <code>personal_document</code> Personal document
Documents related to personal content
- <code>business_document</code> Business document
Documents related to business content
- <code>im_chat_log</code> Chat log
Records of instant messaging chats
- <code>synced_from_notion</code> Notion document
Documents synchronized from Notion
- <code>synced_from_github</code> GitHub document
Documents synchronized from GitHub
- <code>others</code> Other document types
Other document types not listed above
- <code>doc_metadata</code> Document metadata (required if doc_type is provided)
Fields vary by doc_type:
For <code>book</code>:
- <code>title</code> Book title
Title of the book
- <code>language</code> Book language
Language of the book
- <code>author</code> Book author
Author of the book
- <code>publisher</code> Publisher name
Name of the publishing house
- <code>publication_date</code> Publication date
Date when the book was published
- <code>isbn</code> ISBN number
International Standard Book Number
- <code>category</code> Book category
Category or genre of the book
For <code>web_page</code>:
- <code>title</code> Page title
Title of the web page
- <code>url</code> Page URL
URL address of the web page
- <code>language</code> Page language
Language of the web page
- <code>publish_date</code> Publish date
Date when the web page was published
- <code>author/publisher</code> Author or publisher
Author or publisher of the web page
- <code>topic/keywords</code> Topic or keywords
Topics or keywords of the web page
- <code>description</code> Page description
Description of the web page content
Please check [api/services/dataset_service.py](https://github.com/langgenius/dify/blob/main/api/services/dataset_service.py#L475) for more details on the fields required for each doc_type.
For doc_type "others", any valid JSON object is accepted
- <code>doc_language</code> In Q&A mode, specify the language of the document, for example: <code>English</code>, <code>Chinese</code>
Document metadata (required if doc_type is provided). Fields vary by doc_type:
For <code>book</code>:
- <code>title</code> Book title
- <code>language</code> Book language
- <code>author</code> Book author
- <code>publisher</code> Publisher name
- <code>publication_date</code> Publication date
- <code>isbn</code> ISBN number
- <code>category</code> Book category
For <code>web_page</code>:
- <code>title</code> Page title
- <code>url</code> Page URL
- <code>language</code> Page language
- <code>publish_date</code> Publish date
- <code>author/publisher</code> Author or publisher
- <code>topic/keywords</code> Topic or keywords
- <code>description</code> Page description
Please check [api/services/dataset_service.py](https://github.com/langgenius/dify/blob/main/api/services/dataset_service.py#L475) for more details on the fields required for each doc_type.
For doc_type "others", any valid JSON object is accepted
- <code>separator</code> Segmentation identifier. Currently, only one delimiter is allowed. The default is <code>***</code>
- <code>max_tokens</code> The maximum length (tokens) must be validated to be shorter than the length of the parent chunk
- <code>chunk_overlap</code> Define the overlap between adjacent chunks (optional)
- <code>doc_type</code> Type of document (optional)
- <code>book</code> Book
Document records a book or publication
- <code>web_page</code> Web page
Document records web page content
- <code>paper</code> Academic paper/article
Document records academic paper or research article
- <code>social_media_post</code> Social media post
Content from social media posts
- <code>wikipedia_entry</code> Wikipedia entry
Content from Wikipedia entries
- <code>personal_document</code> Personal document
Documents related to personal content
- <code>business_document</code> Business document
Documents related to business content
- <code>im_chat_log</code> Chat log
Records of instant messaging chats
- <code>synced_from_notion</code> Notion document
Documents synchronized from Notion
- <code>synced_from_github</code> GitHub document
Documents synchronized from GitHub
- <code>others</code> Other document types
Other document types not listed above
- <code>doc_metadata</code> Document metadata (required if doc_type is provided)
Fields vary by doc_type:
For <code>book</code>:
- <code>title</code> Book title
Title of the book
- <code>language</code> Book language
Language of the book
- <code>author</code> Book author
Author of the book
- <code>publisher</code> Publisher name
Name of the publishing house
- <code>publication_date</code> Publication date
Date when the book was published
- <code>isbn</code> ISBN number
International Standard Book Number
- <code>category</code> Book category
Category or genre of the book
For <code>web_page</code>:
- <code>title</code> Page title
Title of the web page
- <code>url</code> Page URL
URL address of the web page
- <code>language</code> Page language
Language of the web page
- <code>publish_date</code> Publish date
Date when the web page was published
- <code>author/publisher</code> Author or publisher
Author or publisher of the web page
- <code>topic/keywords</code> Topic or keywords
Topics or keywords of the web page
- <code>description</code> Page description
Description of the web page content
Please check [api/services/dataset_service.py](https://github.com/langgenius/dify/blob/main/api/services/dataset_service.py#L475) for more details on the fields required for each doc_type.
For doc_type "others", any valid JSON object is accepted