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标书大模型（Proposal-LLM Chinese version )
| Date | Stars |
|---|---|
| 2026-07-31 | 360 |
| 2026-08-05 | 361 |
| 2026-08-06 | 361 |
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# ProposalLLM （Proposal-LLM Chinese version )
If you want the English Version, please pick a issue.
#Features of this Open-Source Software:
Automatically generates a point-by-point response format proposal based on the requirements matrix and product documentation. This includes generating all subheadings and content with automatic formatting for headings 1, 2, and 3, organizing body text, images, and bullet points accordingly.
If the proposal content has a corresponding feature in the product requirements table, it automatically copies the product manual content to the point-by-point response section (preserving images, tables, bullet points, etc.). It can also automatically rewrite product descriptions to suit different proposal requirements. If no matching feature is found, it calls a large model to automatically generate a relevant feature description.
Generates a technical requirements deviation table and fills out point-by-point responses in the requirements matrix, formatted as "Answer: Fully supported, {text generated by the large model based on the requirement}." It also adds corresponding chapter numbers for each section in the proposal.
Breaks down the product manual into multiple reusable detail documents to avoid scanning the entire manual repeatedly when drafting a proposal, thus improving proposal generation performance and enabling customization of features as needed.
#Instructions:
Install a Python environment and the required packages: pip install openpyxl, docx, openai, requests, python-docx
Apply for a ChatGPT or Baidu Qianfan model (I use ERNIE-Speed-8K, a free model), note down the token, and enter it in the appropriate key position in the code.
Copy the product manual to Template.docx. Ensure styles use “Body Text,” “Heading 1,” “Heading 2,” and “Heading 3” to avoid formatting issues.
Run Extract_Word.py to generate documents based on the product manual (supports up to 3 levels of headings). Check the generated file for accuracy; if bullet list formats are incorrect, they can be corrected in the final document.
Fill in columns B, C (to auto-generate level 2 and level 3 headings), and G in the requirements_table.xlsx (G corresponds to the product manual's feature section, use X if there is no section available. If X is entered or if no matching chapter is found, the large model will generate content automatically).
Review "Proposal Content.docx," retaining sections to be used in the proposal with styles for body text, headings 1, 2, and 3. Adjust style formatting as needed but avoid changing style names to prevent errors.
Configure parameters in Generate.py:
openai.api_key: OpenAI API Key
API_KEY, SECRET_KEY: Baidu API Key
MAX_WIDTH_CM: Maximum image width (images exceeding this width will automatically resize)
Customize the prompts used to generate point-by-point responses and content for large datasets as per product type. The response format can also be customized.
MoreSection=1 reads column C to generate detailed level 3 headings (default is 1, enabled).
ReGenerateText=0 sets automatic regeneration of all product document text content for different proposal needs (default is 0, disabled).
DDDAnswer=1 enables automatic generation of point-by-point response content at the beginning of each feature section (default is 1, enabled).
key_flag=1 includes the importance level in the proposal subheading (default is enabled).
last_heading_1=2 sets the beginning chapter number for the technical solution in "Proposal Content.docx" (in the template, it is chapter 2) so that chapter numbers are automatically filled in the requirements matrix.
Run Generate.py.
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cb102013193a161b, llm:Repository name and description: 'ProposalLLM' and '标书大模型（Proposal-LLM Chinese version )' indicating a large language model focused on proposals/bid documents in Chinese.
matched fp:cb102013193a161b, llm:Repository name and description: 'ProposalLLM' and '标书大模型（Proposal-LLM Chinese version )' indicating a large language model focused on proposals/bid documents in Chinese.
matched fp:cb102013193a161b, llm:Repository name and description: 'ProposalLLM' and '标书大模型（Proposal-LLM Chinese version )' indicating a large language model focused on proposals/bid documents in Chinese.