Deployment and Upgrade for Intelligent Triage Registration

Data Characteristics for This Category

Intelligent triage systems, when preparing registration and declaration documents, primarily handle regulatory files, guidelines, technical review points, clinical trial data, product manuals, user guides, and various test reports. Data sources include the National Medical Products Administration (NMPA) official website, industry association documents, and internal product R&D and production records. Data updates typically align with regulatory policy release cycles, product lifecycles, and review processes, often occurring quarterly or semi-annually in batches. Documents are mainly in PDF, Word, and Excel formats, containing extensive specialized terminology, medical abbreviations, units of measurement (e.g., mg, ml, mm, ℃), and frequently include complex tables, charts, and formulas.

Constraints Imposed by These Characteristics on Deployment and Upgrade

The large volume and complex structure of intelligent triage registration and declaration data demand significant storage and computing capabilities from the deployment environment. Periodic updates to regulatory policies necessitate regular incremental or full refreshes of knowledge base content, challenging the system's knowledge update mechanisms and version management. Specialized terminology and units of measurement within documents require models with high-precision entity recognition and relationship extraction capabilities to prevent ambiguity during retrieval and generation. The presence of mixed document formats also requires robust file parsing and vectorization processing. Deployment must reserve sufficient disk space for future data growth. Upgrades must ensure smooth transitions between old and new knowledge bases and enable effective rollback for parsing errors.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Individual registration and declaration files can be large; this ensures large documents can be uploaded.
`Chunk size` (Segment Length)	`800–1200 characters` (characters)	Balances semantic completeness and recall accuracy, avoiding overly long or short text segments.
`Recall count` (Recall Count)	`15 entries` (items)	Provides sufficient contextual information to the reranking model, improving relevance.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Adjust using test sets based on actual business scenarios and data characteristics to balance precision and recall.
`Rerank result count` (Reranked Return Count)	`5 entries` (items)	Filters for the most relevant results, reducing the processing burden on subsequent models and improving response speed.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Parsing complex PDF or Word documents can be time-consuming; this prevents timeouts.

Common Pitfalls

After deploying the Reranker model, tests pass, but actual retrieval results show no reranking. The Rerank result count (Reranked Return Count) remains equal to Recall count (Recall Count). This might be due to incorrect configuration of the FastGPT backend service or failure to recognize the custom Reranker service address.
After upgrading FastGPT-MCP-Server, FastGPT fails to start, or the interface displays connection errors upon startup. This often occurs because the new version has changed certain dependent libraries or configuration file formats, preventing a smooth migration of old configurations.
When importing CSV files into the knowledge base, Chinese characters display as garbled text. This is typically a file encoding issue. Even if a character encoding is specified, garbled text can still appear if the file's actual encoding does not match the specified encoding.

Verification Steps

Upload a PDF document containing complex tables and specialized terminology. Check if file parsing is normal and if segmented content is semantically coherent.
Pose a question related to a typical registration and declaration issue. Observe the distribution of similarity scores for the recalled results and verify if the reranked document snippets are highly relevant.
In the system logs, check the Reranker model's call records. Confirm that each retrieval request successfully triggered the reranking process and returned the expected number of results.

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.