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docs: update chat_knowledge.md and wechat.jpg (#1035)

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Aries-ckt 2024-01-05 16:37:33 +08:00 committed by GitHub
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docs/docs/application/started_tutorial/chat_knowledge.md
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@ -56,6 +56,23 @@ and click Process, it will take a few minutes to complete the document segmentat
<img src={'/img/chat_knowledge/doc_segmentation.png'} width="720px" /> <img src={'/img/chat_knowledge/doc_segmentation.png'} width="720px" />
</p> </p>
:::tip
**Automatic: The document is automatically segmented according to the document type.**
**Chunk size: The number of words in each segment of the document. The default is 512 words.**
- chunk size: The number of words in each segment of the document. The default is 512 words.
- chunk overlap: The number of words overlapped between each segment of the document. The default is 50 words.
** Separator:segmentation by separator **
- separator: The separator of the document. The default is `\n`.
- enable_merge: Whether to merge the separator chunks according to chunk_size after splits. The default is `False`.
** Page: page segmentation, only support .pdf and .pptx document.**
** Paragraph: paragraph segmentation, only support .docx document.**
- separator: The paragraph separator of the document. The default is `\n`.
** Markdown header: markdown header segmentation, only support .md document.**
:::
### Waiting for document vectorization ### Waiting for document vectorization

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setup.py
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@ -638,7 +638,7 @@ init_install_requires()
setuptools.setup( setuptools.setup(
name="db-gpt", name="db-gpt",
packages=find_packages(exclude=("tests", "*.tests", "*.tests.*", "examples")), packages=find_packages(exclude=("tests", "*.tests", "*.tests.*", "examples")),
version="0.4.4", version="0.4.5",
author="csunny", author="csunny",
author_email="cfqcsunny@gmail.com", author_email="cfqcsunny@gmail.com",
description="DB-GPT is an experimental open-source project that uses localized GPT large models to interact with your data and environment." description="DB-GPT is an experimental open-source project that uses localized GPT large models to interact with your data and environment."