Document Parsing and Chunking for Clinical Trial

Data Characteristics for This Category

Clinical trial information from tender bidding primarily originates from drug procurement platforms, medical institution official websites, and government regulatory documents. This data updates frequently, typically changing with tender batches or policy adjustments. Document formats vary, including PDF tender announcements, procurement documents, technical requirements, contract templates, and Word or Excel attachments. Structurally, documents usually contain project names, sponsor information, trial drugs, indications, inclusion/exclusion criteria, research centers, budget details, and timelines. Fields include extensive unstructured descriptions and structured table data. Units are complex; for example, monetary values might involve "million CNY" or "USD," and time might involve "days," "months," or "years," with varying expressions across documents.

Constraints Imposed by These Characteristics on Document Parsing and Chunking

The high update frequency of tender bidding documents requires the knowledge base to support rapid incremental updates and version management, ensuring the timeliness of pre-screening information. Diverse document formats, especially PDFs and scanned copies, challenge the robustness of parsing engines, necessitating support for multiple parsing strategies. The mix of unstructured descriptions and structured table data within documents makes a single text chunking method ineffective for extracting all key information. For instance, inclusion/exclusion criteria often appear as long paragraphs, while research center lists are presented in tables. Inconsistent units for fields like monetary values and time require identification and standardization during chunking to prevent inaccurate pre-screening results due to unit confusion. Key information scattered across long documents also complicates determining optimal chunk granularity.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Tender bidding documents are often large, containing numerous charts and attachments. This limit covers most cases.
`Chunk Size`	`500–800 characters`	Balances semantic completeness for long paragraphs and recall efficiency for short paragraphs, suitable for mixed text and table content.
`Chunk Overlap Size`	`50 characters`	Ensures contextual continuity and prevents critical information from being truncated at chunk boundaries.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing complex PDFs or large Excel files may require more time, preventing parsing failures due to timeouts.
`Custom Parsing Rules`	Enabled, with table parsing configured	Tailored for the table structures in tender documents, ensuring accurate extraction of structured data from tables.
`Max Concurrent Parsing Tasks`	Calibrate based on actual measurements	Adjust according to system resources and file parsing load to avoid resource exhaustion or parsing delays.

Three Common Mistakes

After uploading a large PDF file, an ERR_FILE_PARSE_FAILED error occurs because PARSE_FILE_TIMEOUT_SECONDS is set too short, preventing parsing completion within the allotted time.
The number of knowledge base chunks increases abnormally, or some key information is missing, because custom parsing rules are not enabled, causing table content to be incorrectly chunked as plain text.
Retrieval for certain specific fields (e.g., "budget amount," "trial duration") is inaccurate, showing irrelevant values in results, because units for these fields were not effectively identified and standardized during chunking, leading to semantic confusion during vectorization.

How to Confirm Proper Configuration

Upload typical tender announcements and procurement documents. Review the chunk preview to confirm that table content is correctly identified and chunked independently, and that text paragraphs are semantically complete.
For fields with complex units, perform keyword searches. Verify that the numerical values and units of these fields in the recall results match the original text. Experiment with different unit search terms.
Randomly select multiple chunks. Manually read or use the knowledge base Q&A function to check if chunk content includes complete contextual information without obvious semantic breaks.

Note: The values provided are common starting points. Measure them against your own samples for optimal performance.

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.