HTTP Interface and External Systems for Hemato-Oncology

Data Characteristics for This Category

Hemato-oncology quality documents primarily source data from clinical trial reports, drug manufacturing batch records, adverse event monitoring reports, and regulatory compliance audit files. These documents are updated frequently, especially clinical trial data and adverse event reports, which may update weekly or monthly. Document structures often combine structured tables (e.g., CSV, Excel) with semi-structured text (e.g., PDF reports). Specific fields include ICD-O-3 tumor morphology codes, WHO disease classification, RECIST assessment criteria, drug batch number, patient ID, and dosage units (e.g., mg/kg). Precision and consistency of units are critical in quality documents; for example, drug concentrations are typically precise to ng/mL, and time points to days or hours.

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

The mixed data structure of hemato-oncology quality documents requires specific interface design. Structured data needs direct mapping to JSON or XML fields. Semi-structured text requires preprocessing to extract key information. High update frequency means external systems must support periodic or event-driven interface calls; for instance, triggering a synchronization when new clinical trial data is imported. The presence of specific fields requires interfaces to flexibly handle custom field mapping and validation, especially for hierarchical fields like ICD-O-3 codes. Additionally, unit precision requires interfaces to maintain unit integrity during data transfer, avoiding data quality issues from lost or misunderstood units. For example, the dosage field must be transmitted with its associated unit field. For HTTP interfaces, this means defining clear data models and validation rules to ensure data accuracy and completeness.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`maxContext`	`800–1200 characters`	Captures key diagnostic descriptions and treatment plan details in hemato-oncology reports.
`segmentLength`	`300 characters`	Balances semantic integrity with recall efficiency, avoiding excessive truncation of critical medical terms.
`recallCount`	`top 5`	Considers the specialized nature and relevance of hemato-oncology documents, increasing recall to cover more related information.
`similarityThreshold`	`0.75`	Ensures recalled results are highly relevant to hemato-oncology query intent, reducing interference from irrelevant information.
`HTTP_REQUEST_TIMEOUT`	`600 seconds`	Addresses potential delays in external systems when processing large volumes of clinical data or generating complex reports.
`webhookUrl`	`Calibrate based on actual measurements`	Requires configuration and validation based on the callback address provided by the actual business system.

Common Pitfalls

External systems return an HTTP 500 error or an empty response body. This often occurs because the ICD-O-3 encoding format in the request body does not meet the target system's data validation rules.
After calling the FastGPT API, the output treatmentPlan field contains partial text from the previous diagnosisConclusion field. This results from improper context management configuration, leading to variable contamination.
A configured Webhook fails to trigger. This is often due to the Content-Type header not being set to application/json, preventing the external system from correctly parsing the request body.

Verification Steps

Perform a knowledge base import for core hemato-oncology documents (e.g., diagnostic reports, medication records). Check for parsing errors or missing fields.
Simulate an external system data update. Trigger a knowledge base synchronization via the HTTP interface. Verify that the updateTime field correctly reflects the change.
Conduct at least 5 question-and-answer tests for typical hemato-oncology questions (e.g., "treatment plan for patients with XX gene mutation"). Evaluate the relevance and accuracy of the results, ensuring key information like RECIST assessment criteria is correctly recalled.
Check external system logs to confirm that HTTP requests sent by FastGPT were successfully received and returned an HTTP 200 OK response code.

The values provided are common starting points and should be measured against specific 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.