Deployment and Upgrade for Autoimmune Quality Documents

Data Characteristics of This Category

Autoimmune disease quality documents primarily originate from clinical trial reports, drug registration applications, post-market surveillance data, and research papers. This data typically exists as structured or semi-structured documents in PDF and DOCX formats, with some images and tables. Update frequency depends on clinical research progress, regulatory approval cycles, and post-market adverse event reports. Updates usually occur quarterly or annually, but some critical safety information may update monthly. Document fields include drug mechanisms of action, indications, contraindications, adverse reactions, dosage and administration, clinical efficacy data, and immunological indicators (e.g., antibody titers, cytokine levels). Units often include milligrams (mg), milliliters (mL), international units (IU), micromoles (µmol), and percentages (%).

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

The data characteristics of autoimmune quality documents impose specific requirements on FastGPT's deployment and upgrade processes. The diverse document formats and semi-structured nature demand robust file parsing modules capable of accurately identifying and extracting key information. The fluctuating document update frequency, especially for critical safety information, means upgrade strategies must support incremental updates and localized index rebuilding. This ensures knowledge base timeliness and avoids resource consumption and downtime from full rebuilds. The presence of specialized fields like immunological indicators requires vector models to consider biomedical semantic relationships during training or fine-tuning to improve recall accuracy. Furthermore, some documents may contain sensitive patient data or trade secrets, necessitating strict data security and compliance for the deployment environment. This includes private deployment solutions and fine-grained access control.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Autoimmune documents often contain numerous images and charts, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Complex PDF and DOCX files require longer parsing times; this prevents parsing timeouts.
`Chunk size`	`800–1200 characters`	Ensures contextual completeness while preventing excessively long segments from degrading recall quality.
`Similarity threshold`	Adjust within `0.70–0.85` based on empirical testing	Fine-tuning is necessary based on the distinctiveness of autoimmune professional terminology to balance recall and precision.
`Recall count`	`Top 8 entries`	Guarantees coverage of relevant information while avoiding the introduction of excessive noise.
`Vector Model`	Select a model pre-trained or fine-tuned for the biomedical domain	Improves understanding and similarity calculation capabilities for specialized terms like immunological indicators and drug mechanisms.

Three Common Mistakes

The file parsing node does not work after file upload, resulting in an empty knowledge base. This typically occurs because the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, causing large or complex autoimmune documents to time out during parsing.
The PostgreSQL database repeatedly restarts after Docker deployment. This might be due to insufficient disk space or incorrect PGDATA directory permissions, preventing the database from starting normally and persisting data.
Key drug adverse reaction information is not effectively recalled in query results. This could be because the vector model does not fully understand the semantic relationships of autoimmune-related terms, or the Similarity threshold is set too high, filtering out slightly less relevant but important information.

How to Confirm Proper Configuration

Upload a typical autoimmune clinical trial report PDF containing immunological indicators and drug mechanisms of action. Verify that segments are successfully generated in the knowledge base, with complete and uncorrupted content.
Execute queries containing key terms (e.g., "TNF-α inhibitor," "lupus nephritis," "adverse event"). Check if relevant document snippets are included in the recall results and evaluate their accuracy. A qualified recall accuracy threshold can be set based on business needs.
Simulate high-concurrency file upload scenarios. Observe system resource utilization and check logs for parsing timeouts or database connection errors to ensure system stability under high load.

Note that 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.