Document Parsing and Chunking for Tender Bidding Regulations

Data Characteristics

Data related to tender bidding regulations originates from various sources: centralized procurement platforms for pharmaceuticals and consumables, official medical insurance bureau websites, and internal hospital management systems. This data appears as policy documents, announcements, procurement catalogs, technical standards, and operational procedures. Document structures typically include regulatory clauses, policy interpretations, appendix lists, and declaration templates. This data is highly structured and semi-structured. Updates are frequent, especially during policy adjustments or new product approvals, with some documents updating every few weeks. Field content covers product names, registration numbers, manufacturers, listed prices, medical insurance coverage, procurement cycles, and delivery requirements. Prices and technical parameters are often precise to multiple decimal places, demanding high numerical accuracy.

Constraints Imposed by These Characteristics on Document Parsing and Chunking

The highly structured nature of tender bidding regulation documents requires the parser to accurately identify logical units like chapters, clauses, and tables to prevent content misalignment. Frequent updates challenge parsing efficiency and incremental update capabilities, requiring rapid processing of new document versions and knowledge base updates. Documents contain many precise numbers (e.g., prices, specifications) and specialized terms, demanding high accuracy in word segmentation to ensure critical information is fully captured during retrieval. Furthermore, policy interpretation and operational procedure documents often describe multiple responsible parties and complex processes. Chunking must preserve semantic completeness to avoid splitting complete concepts, which would affect subsequent Q&A accuracy.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunk_size`	`500–800 characters`	Ensures each chunk has sufficient semantic context while avoiding excessive length that could lead to information redundancy and reduced retrieval efficiency.
`overlap_size`	`50–100 characters`	Guarantees appropriate overlap between chunks, reducing the risk of critical information being cut off at chunk boundaries.
`parser_mode`	`auto / table_first`	Table data is important in tender bidding documents; prioritizing table structure recognition improves parsing accuracy.
`ocr_enabled`	`true`	Some policy documents may embed scanned images; OCR ensures text content is recognizable.
`max_file_size_mb`	`100 MB`	Balances common policy document sizes with system processing capabilities, preventing parsing failures due to overly large files.
`timeout_seconds`	`300 seconds`	Provides sufficient parsing time for complex documents (e.g., multi-page tables, scanned documents), preventing timeouts.

Common Pitfalls

Log displays ocr error: This usually occurs when documents contain low-quality scanned images or complex layouts, preventing the OCR engine from effectively recognizing text.
Key prices or parameters are missing from Q&A results: Chunks that are too small may truncate sentences containing critical numerical values, or the tokenizer may fail to correctly identify numbers with units.
Feishu Knowledge Base is configured, but PPT/PDF documents fail to synchronize: The file type is not in the parser's whitelist, or the Feishu API returned an unsupported file format error code.

How to Verify Configuration

Upload a typical tender bidding regulation document (including policy clauses, tables, and attachments). Check if the parsed chunks' quantity and content meet expectations, especially if table data is extracted correctly.
Query using specific product names and listed prices from the document. Verify if the AI's answer accurately retrieves relevant data and examine the completeness of its source chunks.
Test documents containing images or scanned content. Confirm OCR functionality works correctly and that text content within images is retrievable and citable.
Continuously monitor parsing logs for timeout or parser_error exceptions. Adjust timeout_seconds or check document format compatibility as needed.

The values provided are common starting points and should be measured against the reader's own samples.

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.