Data Characteristics
R&D documents for tendering and bidding originate primarily from official institutional websites, such as public resource trading platforms, drug administration agencies, and health commissions, as well as industry media announcements. Data updates are frequent, typically daily, covering new project releases, bid openings, successful bids, and dispute clarifications. Document types vary, including PDF format for tender documents, technical requirements, product specifications, and contract templates, as well as supplementary files in Word or Excel format. Document structures are generally standardized but contain extensive unstructured or semi-structured descriptions. Examples include technical specifications, parameter ranges, and testing methods, often described in natural language. Field names and units can be inconsistent across documents; for instance, "Production Process" (production process) might be described as "Manufacturing Flow" (manufacturing flow), and "milligrams" (milligram) might be abbreviated as "mg".
Constraints Imposed on Document Parsing and Chunking
High update frequency demands efficient, automated crawling and parsing capabilities to handle a large volume of new daily documents. Diverse document types require the parser to support multi-format file processing, especially for complex content in PDFs like mixed text and images, tables, and scanned images. The combination of standardized structure and unstructured descriptions challenges chunking strategies. The system must identify key technical specifications, product parameters, and compliance requirements, separating them from lengthy legal clauses or general descriptions. Inconsistent fields and units necessitate standardization after chunking, for example, unifying "mg" to "milligrams," to facilitate subsequent retrieval and analysis. Furthermore, tendering and bidding documents are often time-sensitive, requiring high parsing speed and accuracy. Errors or delays in parsing can lead to missed business opportunities.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 500–800 characters (characters) | Balances semantic completeness and recall efficiency. Avoids excessively long chunks that lead to information redundancy and excessively short chunks that cause loss of context. |
Overlap Length | 50 characters (characters) | Ensures sufficient contextual overlap between adjacent chunks, reducing semantic discontinuity. This is especially important for complex technical descriptions. |
Parsing Mode | Smart Chunking | Prioritizes identification of structural elements like headings, lists, and tables within documents to improve the semantic accuracy of chunking. |
Image OCR Recognition | Enabled (Enable) | Many tender documents embed technical charts or scanned images. OCR recognition is crucial for extracting information from these parts. |
Table Parsing | Enabled (Enable) | Tender documents often contain detailed parameter tables. Accurate parsing of table structures helps extract key data. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Provides sufficient parsing time for large PDFs or complex documents with many images, preventing timeouts. |
Common Mistakes
- Multiple uploaded files cannot be distinguished, leading to confusion during retrieval. This occurs when metadata is not effectively used to tag file sources or types during document parsing.
- Knowledge base retrieval results fail to recall critical technical parameters from documents, such as "technical specifications of a certain equipment" or "chemical composition of a specific material." This happens when key entities in unstructured text are not identified and independently chunked during document chunking, or when chunks are too long, diluting the information.
- Content from uploaded PDF scans cannot be retrieved. This indicates that the file parser's OCR recognition function is not enabled or incorrectly configured, preventing text extraction from images.
Verification Steps
- Select typical tendering and bidding documents. Upload them to the knowledge base. Review the chunking results via the knowledge base management interface to confirm that key technical descriptions and parameter tables are effectively identified and segmented.
- Use specific technical parameters, product models, or compliance requirements from the documents as query terms. Conduct knowledge base retrieval tests. Observe whether the recall results include relevant document snippets and evaluate the accuracy and completeness of these snippets.
- Check the metadata of parsed documents in the knowledge base. Confirm that information such as file source, upload time, and document type has been automatically extracted and associated to support subsequent refined retrieval and management.
The values provided are common starting points. Measure them against specific samples to determine optimal settings for individual use cases.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.