Database and Operations for Structured Analysis of Health

Data Characteristics

R&D document data in health management primarily originates from clinical trial reports, disease management plans, health assessment questionnaires, nutritional intervention records, exercise prescriptions, and related research papers. These documents typically exist as PDFs, Word documents, Excel spreadsheets, or structured text (e.g., JSON, XML). Data updates frequently, especially during ongoing clinical trials or health management plan iterations. Document structures vary, including highly structured tabular data and extensive unstructured free text. Key fields include patient ID, physiological indicators (e.g., blood pressure, blood glucose, BMI), medication records, diagnostic results, intervention measures, follow-up dates, and various assessment scale scores. Units involve common medical and nutritional units such as mmHg, mmol/L, kg/m², and mg/d.

Constraints Imposed on Database and Operations

The data characteristics of health management R&D documents impose specific requirements on databases and operations. High-frequency updates and diverse document structures necessitate flexible data models and efficient write performance. The presence of large amounts of unstructured text requires databases with robust full-text search capabilities and vector storage for semantic search and similarity matching. The precision and unit consistency of key fields like physiological indicators and medication records demand high standards for data cleaning and validation processes. Additionally, documents may contain sensitive patient information, making data security, access control, and audit logs critical for operations. The database must handle mixed data types, supporting both the transactional consistency of relational databases and the scalability and flexibility of non-relational databases.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`800–1200 characters`	Health management document paragraphs are moderately sized; this range balances information density and model processing efficiency.
`Recall count` (Recall Count)	`top 5`	Ensures initial filtering covers highly relevant information, reducing subsequent processing burden.
`Similarity threshold` (Similarity Threshold)	`0.75`	Balances recall precision and recall rate, reducing irrelevant interference, suitable for medical text.
`Chunk size` (Segment Length)	`500 characters`	Accommodates typical paragraph lengths for research methods and results descriptions in health management documents.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accounts for potentially complex charts and extensive text in large clinical trial reports, which can prolong parsing time.
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Covers the size requirements for most health management research reports and clinical trial documents.

Common Mistakes

When executing workflows in batch, the next step has no data or incomplete data, appearing as "result": "List is empty". This occurs when the output data format from the previous step does not match the expected input of the batch execution component, for example, expecting a list but receiving a single object.
The database connection component returns an "Access denied for user" error. This happens when database user access permissions are not configured correctly, or the provided connection credentials (e.g., username or password) are incorrect.
AI-generated database query statements fail during execution, appearing as "syntax error at or near". This indicates that the SQL statement generated by the AI model may not fully conform to the syntax rules of the target database (e.g., PostgreSQL or MongoDB), or the queried table names and field names do not match the actual database structure.

Verification Steps

Use FastGPT's database connection test feature to verify successful database connection and check for a status code of 200.
Upload a typical health management R&D document (e.g., a clinical trial report PDF) and observe its structured parsing results to confirm that key fields like Patient ID, Physiological Indicators, and Medication Records are extracted correctly.
Execute an AI Agent task involving complex queries. Check if the generated and executed database query statements return the expected results, and compare the Recall count (Recall Count) with the actual number of returned items.
Simulate high-concurrency document uploads and parsing. Monitor the database's CPU, memory, and I/O utilization to ensure stable operation within the configured PARSE_FILE_TIMEOUT_SECONDS.

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.