Vector Models and Indexing for Regulatory Submission Quality

Data Characteristics

Regulatory submission documents in the biopharmaceutical sector originate from official regulatory guidelines, internal R&D reports, clinical trial data, manufacturing process files, and quality standards. These documents update infrequently, typically during regulatory changes or key product lifecycle milestones. Document structures are highly standardized, adhering to ICH guidelines or specific national regulatory requirements. They contain numerous tables, figures, and specialized terminology. Fields and units are strictly defined, such as dosage units (mg/kg), concentration units (μg/mL), and time units (h, day). Key information like batch numbers, expiry dates, and manufacturing dates often appear in specific formats.

Constraints on Vector Models and Indexing

The standardized structure and specialized terminology of regulatory submission documents require vector models to deeply understand domain-specific knowledge. This ensures accurate semantic capture and avoids recall bias from general models misunderstanding professional vocabulary. The prevalence of tables and figures means pure text indexing may lose critical information. This necessitates considering multimodal or structured data extraction strategies. Low update frequency demands high stability once an index is built, but also requires fast incremental update capabilities for localized revisions to regulations or product data. Strict field and unit definitions require the indexing process to effectively distinguish values from units, handle unit conversions, and recognize specific batch number formats. This impacts tokenization strategies and entity recognition configurations.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	Regulatory documents contain substantial information per segment. Shorter segments lose context; longer segments result in overly coarse vector granularity.
`Recall count` (Recall Count)	`Top 5–8 entries`	Ensures coverage of multiple relevant paragraphs in the initial recall phase for complex queries.
`Similarity threshold` (Similarity Threshold)	Calibrate with actual measurements	Adjust based on actual query performance and false positive rates, typically between `0.75–0.85`.
`Rerank result count` (Rerank Return Count)	`3 entries`	After reranking, select a small number of the most relevant paragraphs for user reference to improve precision.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Regulatory documents are often large, requiring longer parsing times. This prevents parsing failures due to timeouts.
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Ensures successful upload of PDF documents containing numerous figures and data, preventing failures due to excessive file size.

Common Pitfalls

Image content is not indexed after uploading documents to the knowledge base, leading to missing query results for image-based content. This usually occurs if the image indexing model is not enabled or incorrectly configured in a local deployment, or if specific features in the current FastGPT version (e.g., v4.9.0) require a commercial license.
Embedding model connection errors occur when connecting to OneAPI, with logs showing HTTP 500 or Connection refused. This typically indicates an invalid API key configured in OneAPI, an incorrect Endpoint address, or a network firewall blocking communication between FastGPT and the OneAPI server.
Query results contain many irrelevant paragraphs, or important information is missed. This may be due to an improper Chunk size (Segment Length) setting, which fragments semantic units, or a Similarity threshold (Similarity Threshold) set too low, recalling too many low-relevance results.

Verification Steps

Upload representative regulatory submission documents. Check that the parsing status for each document in the knowledge base is "success." Review the segment preview to confirm that key information (e.g., batch numbers, dosages) remains intact within the same segment.
Query specific figures or table data within the documents. Verify if the AI can accurately cite or summarize relevant information to assess whether image indexing or structured data processing is effective.
Use queries containing specialized terminology and specific values (e.g., "What is the Cmax of drug X at 24h in clinical trials?"). Check the similarity scores of the recall results and compare them with expected relevant paragraphs. Confirm that highly relevant paragraphs are recalled and ranked prominently.
Check the system logs for the connection status between the Embedding model and OneAPI. Confirm there are no error messages like Connection refused or Authentication failed to ensure the Embedding service is operating correctly.

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

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.