HTTP Interface and External Systems for Infectious Disease

Data Characteristics in This Category

Infectious disease quality document data primarily originates from clinical diagnostic reports, laboratory test results, epidemiological survey data, and adverse drug reaction reports. This data updates frequently; some laboratory results can update hourly to reflect dynamic epidemic changes. Document structures commonly include PDF-formatted guidelines, Standard Operating Procedures (SOPs), and structured data storage for case report forms. Core fields include specific pathogen names, infection sites, drug resistance profiles, and host immune status. Units involve microbial load (e.g., copies/mL), antibody titers (e.g., IU/mL), and minimum inhibitory concentration (MIC, unit μg/mL) for drugs. Accurate parsing of these units is essential.

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

The high update frequency of infectious disease data requires HTTP interfaces to have efficient fetching mechanisms. This ensures the timeliness of the FastGPT knowledge base. For example, epidemic dynamics or new pathogen information must synchronize quickly to prevent the provision of outdated information. Documents contain a mix of structured and unstructured data. External systems must effectively identify and process this data during extraction. Examples include extracting key steps from PDF SOPs or parsing specific fields from case reports. Highly specialized fields, such as pathogen names and drug resistance profiles, demand data cleaning and standardization. Pre-processing through external systems is necessary to avoid ambiguity in FastGPT recall. The accuracy of units for numerical data, such as microbial load and antibody titers, is critical for subsequent analysis. Interfaces must retain or correctly convert unit information during transmission. For instance, copies/mL should not be misinterpreted as copies/L.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`FETCH_INTERVAL_SECONDS`	`3600 seconds`	Most quality documents, such as new treatment guidelines, update on an hourly basis.
`MAX_FILE_SIZE_MB`	`100 MB`	Infectious disease guidelines or reports can be large, containing numerous charts and detailed descriptions.
`HTTP_TIMEOUT_SECONDS`	`60 seconds`	Allows sufficient response time, considering external systems may need to perform complex data extraction or transformation.
`CHUNK_SIZE_TOKENS`	`800–1200 characters`	Ensures FastGPT can capture longer contextual segments in infectious disease documents, such as clinical pathway descriptions.
`METADATA_FIELDS_TO_EXTRACT`	`病原体名称, 诊断依据, 耐药情况`	These fields are core tags in infectious disease documents, used for precise retrieval and question answering.
`ERROR_RETRY_COUNT`	`3`	Addresses occasional network fluctuations or transient service unavailability in external systems, improving data synchronization success rates.

Three Common Mistakes

An HTTP interface returns a 404 Not Found error. This often occurs when the external system's document path configuration for a specific pathogen or disease is incorrect.
FastGPT fails to recognize specific microbial names in documents during question answering. This happens when the external system does not correctly extract specialized terms from unstructured documents as retrievable metadata during data extraction.
Numerical fields for specific test results are empty in synchronized data. This is often because the external system fails to correctly match or convert numerical fields from different report templates when parsing original reports, leading to data loss.

How to Verify Configuration

Randomly select 5–10 synchronized infectious disease documents from the FastGPT knowledge base. Check if their metadata fields (e.g., 病原体名称, 耐药情况) are complete and accurate. Compare them against the original documents.
Trigger a manual data synchronization via the FastGPT interface. Observe external system logs to confirm an HTTP status code of 200 OK and no Timeout errors.
Ask FastGPT questions about recently updated infectious disease epidemic information. Verify that the documents cited in the answers are the latest versions. This validates the effectiveness of the FETCH_INTERVAL_SECONDS configuration.
Query FastGPT for documents related to a specific test item (e.g., HIV Viral Load). Check if the numerical values and units (e.g., copies/mL) in the returned results match the original documents. This verifies data parsing accuracy.

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.