HTTP Interface and External Systems for Cardiovascular

Data Characteristics in This Category

Cardiovascular regulatory submission data comes from various sources. These typically include clinical trial reports, non-clinical study reports, manufacturing process and quality control documents, and regulatory compliance statements. Data update frequency varies by project stage. Clinical trial data may update quarterly or annually, while regulatory documents may update immediately with policy changes. Document structures are often hierarchical PDF, Word, or XML formats, containing numerous tables, charts, and text descriptions. Fields involve dose units (e.g., mg/kg, μg/mL), time units (weeks, months), and biostatistical indicators (P-value, confidence interval). Additionally, specialized terms and abbreviations are common, such as NYHA classification, EF value (ejection fraction), and various drug International Nonproprietary Names (INN).

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The complexity of cardiovascular data places specific demands on HTTP interfaces and external system integration. Multi-source data requires support for parsing and importing various data formats, such as structured extraction from PDF and XML files. Periodic updates of clinical trial data necessitate incremental synchronization and version management capabilities in the interface to avoid duplicate imports and track data changes. The abundance of specialized terminology and measurement units makes text parsing and entity recognition accuracy critical, requiring configuration of specialized dictionaries or models. Charts and tabular data within documents challenge image recognition and table parsing services, requiring external systems to accurately identify and extract data points. Furthermore, the timeliness of regulatory compliance documents requires interfaces to quickly respond to update notifications from external regulatory databases and trigger relevant document re-review processes.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Cardiovascular clinical trial reports and pharmaceutical research data often contain many images and tables. Individual file sizes can be large.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing complex charts and nested tables in large PDF files requires longer parsing times. This avoids parsing failures due to timeouts.
`Chunk size`	`800–1200 characters`	Maintain semantic integrity of text segments, especially for pathology descriptions and trial result interpretations. This facilitates subsequent RAG retrieval.
`Similarity threshold`	`0.78–0.85`	Ensure high relevance for recalled cardiovascular professional terms, dosage information, and clinical indicators. This reduces interference from irrelevant information.
`HTTP_REQUEST_TIMEOUT`	`120 seconds`	When connecting to external regulatory databases and specialized terminology libraries, allow sufficient request response time. This accounts for network latency and data volume.
`MAX_RETRIES`	`3 times`	External interfaces may experience temporary failures during peak hours. Setting a retry mechanism improves data synchronization stability.

Three Common Pitfalls

Symptom: Images returned by external systems do not display in FastGPT replies, appearing as broken links or blank spaces. Reason: Image URLs returned by external interfaces require signing or authorization, or the image format is not supported by the FastGPT renderer.
Symptom: After importing a large number of cardiovascular research reports, search results contain many irrelevant general medical terms. Reason: No dictionary configuration or knowledge base pre-training was performed for cardiovascular-specific terminology and entities. This leads to general models failing to accurately identify and weight them.
Symptom: The PDF parser errors out or extracts incomplete data when processing complex drug structure diagrams or electrocardiograms. Reason: The external PDF parsing service has insufficient support for embedded text within images or complex table structures (e.g., multi-level headers, merged cells). An upgrade or replacement of the parsing engine is required.

How to Verify Correct Configuration

Select 5-10 typical cardiovascular regulatory submission documents (including text, tables, images). Upload and import them into the knowledge base via the HTTP interface. Check if the imported file preview is complete and the content is correct, especially if tabular data is structurally extracted.
Perform retrieval tests for key cardiovascular terms (e.g., ACEI, myocardial infarction, NYHA IV). Check the accuracy and relevance of the recalled results. Compare them with manually annotated expected results and adjust Similarity threshold based on the comparison.
Simulate an external regulatory database update. Trigger data synchronization via the HTTP interface. Check if the system correctly identifies updated content and updates corresponding knowledge base entries. Simultaneously, check synchronization logs for any errors or timeouts and adjust HTTP_REQUEST_TIMEOUT accordingly.
Use FastGPT to ask questions involving cardiovascular drug dosages and clinical trial results. Check if the returned results correctly cite numerical values and units from the original documents and verify their accuracy.

The values provided are common starting points. Measure them against your 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.