Document Parsing and Chunking for Tender Quality Documents

Data Characteristics

Tender quality documents in the biopharmaceutical sector originate from centralized procurement platforms for drugs and consumables, as well as procurement announcements from healthcare institutions. These documents are typically in PDF format, sometimes including scanned images or embedded tables. Updates align with provincial procurement cycles and corporate declaration processes, with new announcements potentially released weekly or even daily. Document structures are highly standardized, usually containing tender project names, procurement numbers, product catalogs, technical parameters, qualification requirements, submission deadlines, and detailed product quality standards and inspection report requirements. Fields include product name, specification model, manufacturer, registration certificate number, quality standard number, test items, methods, results, and judgment criteria. Units involve measurement units, percentages, and concentrations.

Constraints on Document Parsing and Chunking

The standardized structure of tender documents enables template-based parsing, but requires handling scanned images and embedded tables. High-frequency updates and large document volumes necessitate efficient automated parsing to avoid manual processing bottlenecks. The detail in quality standards and technical parameters dictates that chunking must preserve the integrity of critical information, avoiding truncation within key fields. Specifically, unique identifiers like registration certificate numbers and quality standard numbers, as well as logical units formed by test items, methods, results, and judgment criteria, must be retained as a whole during chunking. Failure to do so impacts subsequent accurate retrieval and question answering. Accurate recognition of numerical information, such as measurement units and percentages, is crucial for comparing technical parameters of different products, requiring high precision in text extraction.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size` (Chunk Size)	`500–800 characters`	Ensures the completeness of logical units like quality standards and technical parameters, preventing key information truncation while maintaining an appropriate amount of information per chunk.
`Chunk Overlap Length` (Overlap Size)	`100–150 characters`	Increases contextual continuity and addresses information loss at chunk boundaries, especially for table data spanning pages or paragraphs.
`Enabled OCR` (Enable OCR)	`Yes`	Processes common scanned PDFs and embedded tables in tender documents, ensuring comprehensive content extraction.
`OCR Language`	`zh`	Tender documents are primarily in Chinese; selecting a Chinese OCR engine yields higher recognition accuracy.
`maxContext`	`4096`	Matches the context window limit of mainstream LLMs, ensuring retrieved information is fully passed to the model for analysis.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles large PDF files containing numerous images or complex tables, preventing task interruption due to excessively long parsing times.

Common Mistakes

"Offset out of range" or network errors during file upload, typically occurring when large PDF files fail around 90% completion. This may be due to insufficient client_max_body_size in server configuration or UPLOAD_FILE_MAX_SIZE parameter limits.
Inaccurate question-answering results after document parsing, with specific fields like "registration certificate number" or "test method" missing. This usually results from a chunk size set too short, leading to incomplete segmentation of critical information blocks.
Inconsistent chunking between files uploaded to the knowledge base and files uploaded directly via the platform. This occurs when parameters like chunk_size or overlap_size are not specified during API uploads, leading to default configurations that differ from manual adjustments made through the platform interface.

Verification

Upload typical tender PDF documents. Examine the content of each chunk in the knowledge base to confirm that key technical parameters and quality standards are not truncated.
Conduct question-answering tests using specific product names, registration certificate numbers, or quality standard numbers from the document. Verify that relevant information is accurately retrieved and answered.
Check system logs to confirm that no timeout or out-of-memory errors occur during the upload and parsing of large PDF files.
Compare the total character count before and after parsing. Through random sampling, confirm that OCR recognition accuracy meets expectations, especially for table content.

The values provided are common starting points and should be measured against specific 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.