Deployment and Upgrade for Respiratory System Quality

Data Characteristics

Quality documentation for respiratory system diseases primarily originates from clinical guidelines, drug instructions, diagnostic and treatment norms, medical record templates, research literature, and regulatory documents issued by pharmaceutical authorities. Data update frequencies vary. Clinical guidelines and drug instructions typically revise annually or biennially, while research literature continuously publishes. Document structures often include extensive medical terminology, abbreviations, dosage units, laboratory reference ranges, and diagnostic criteria. Common fields include disease diagnosis (ICD-10 codes), drug names (generic, brand), dosage (mg/kg, ml/h), administration routes, frequency, duration, adverse reactions, contraindications, treatment flowcharts, and risk assessment forms. Units involve various international standard units and specific medical units, such as L/min, mmHg, and mmol/L.

Constraints Imposed by These Characteristics on Deployment and Upgrade

The data characteristics of respiratory system quality documentation impose specific deployment and upgrade requirements. Varied document update frequencies necessitate a flexible incremental update mechanism to handle frequent revisions of some documents. Dense medical terminology and abbreviations require text processing modules with high-precision word segmentation and entity recognition capabilities to prevent semantic loss. Documents contain significant structured and semi-structured data (e.g., dosage tables, flowcharts). Deployment must consider effective parsing and storage of this information for subsequent retrieval and question answering. Additionally, diverse measurement units and complex field associations challenge knowledge base construction and query understanding. This requires configuring appropriate vectorization models and similarity algorithms to ensure retrieval accuracy. During upgrades, key considerations include compatibility between old and new data, model fine-tuning to adapt to new knowledge, and multi-model API management in private deployment environments.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Respiratory-related literature and guidelines often contain numerous images and charts, leading to larger individual file sizes.
`maxContext`	`800–1200 characters`	Ensures sufficient contextual information retention when processing complex medical concepts or long sentences.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF documents or guidelines with complex tables can be time-consuming.
`Chunk size` (Segment Length)	`300 characters`	Balances semantic completeness and fragment recall efficiency, preventing the splitting of critical medical concepts.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	The medical field demands high retrieval precision, ensuring recalled document fragments are highly relevant to the query.
`Recall count` (Number of Retrieved Items)	`Top 10`	Expands the recall scope to cover more potentially relevant medical information, improving question-answering accuracy.

Common Pitfalls

After a knowledge base update, some query results show misunderstandings of medical terminology or missing key information. This occurs due to inconsistent terminology definitions between old and new document versions, or an incremental update mechanism that did not fully cover all related documents.
After system restart, previously configured model API credentials become invalid, causing model call failures. Logs show 401 Unauthorized errors. This happens when CHAT_API_KEY is not persistently stored during private deployment or the configuration method is incompatible with the post-restart environment.
Uploading large diagnostic and treatment guideline files results in prolonged unresponsiveness or 504 Gateway Timeout errors. This is typically due to a PARSE_FILE_TIMEOUT_SECONDS configuration that is too low, not allowing sufficient time for file parsing.

Verification Steps

Upload a respiratory system diagnostic and treatment guideline containing complex medical terminology and tables. Verify successful parsing and retrieval, and confirm correct extraction of key fields and units.
Perform incremental updates using different versions of respiratory system disease guidelines. Then, verify the system can retrieve information from both old and new versions and distinguish their version differences.
Call FastGPT via API, using multiple model combinations configured with different CHAT_API_KEYs for question-answering tests. Ensure all models respond normally, without 401 or 403 error codes.
Simulate a system restart. Verify that all configurations (including model API credentials) remain unchanged in the private deployment environment, the workbench functions normally, and shared links are accessible.

The values given 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.