HTTP Interface and External Systems for Small Molecule Drug

Data Characteristics for This Category

Small molecule drug data centers on chemical structure, physicochemical properties, pharmacodynamics, pharmacokinetics, and toxicology. Data sources are diverse, including public databases (e.g., PubChem, ChEMBL), internal lab data, clinical trial reports, and patent literature. Update frequencies vary; public databases typically update quarterly or annually, while internal data is generated in real-time. Data document structures are complex, often including structural formulas (SMILES, InChI), CAS numbers, molecular weight, LogP, solubility, IC50/EC50 values, and ADMET prediction parameters. Units are highly standardized, such as molecular weight in Da, concentration in nM or μM, and solubility in mg/mL. This data typically exists in structured or semi-structured formats, such as CSV, JSON, or specialized chemical information file formats (e.g., SDF).

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

The complexity of small molecule drug data requires HTTP interface designs that flexibly handle various data formats and nested structures. Varying data update frequencies mean external systems need to support periodic or event-driven data synchronization mechanisms to ensure information timeliness. The richness and specialized nature of fields, especially chemical structure representation, demand high standards for data parsing and validation. Examples include validating SMILES strings or standardizing InChI keys. Additionally, pharmacodynamic and toxicological data often involve numerical ranges and units, requiring interfaces to explicitly transmit this metadata during data transfer and for receiving systems to correctly parse and convert it. Since data may contain sensitive internal research and development information, interface security, including authentication and authorization mechanisms, is crucial.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`requestTimeout`	`60000 ms`	Addresses extended processing times due to slow external services or large data volumes.
`maxConnections`	`10`	Balances concurrent requests with external system load capacity to prevent overload.
`headers`	`Content-Type: application/json`	Ensures clear data transfer format, facilitating correct JSON structure parsing by the server.
`retryCount`	`3`	Handles temporary network fluctuations or transient external service failures, improving data retrieval success rates.
`dataFieldPath`	`$.results[*].properties`	Precisely extracts core attribute fields of small molecule drugs from typical JSON response structures.
`pollingInterval`	`3600 seconds`	Matches public database update frequencies, reducing unnecessary requests.

Common Pitfalls

HTTP interface returns a 500 error code, but FastGPT fails to process it correctly: This typically occurs when the external system's error message format is unexpected, preventing FastGPT from parsing error details and consequently hindering effective retries or error notifications.
After knowledge base content updates, AI responses still rely on old data: This happens when the external data source's update mechanism is not effectively integrated with FastGPT's data synchronization strategy, such as failing to trigger knowledge base re-indexing or incremental updates.
When calling an external model API, the returned result fields are empty or incomplete: This often indicates an inaccurate dataFieldPath configuration, failing to correctly point to the specific path within the external API response that contains the required small molecule drug information.

Verification Steps

Check the status of the most recent HTTP interface synchronization task in FastGPT's data source management interface. Confirm it shows "success" with no abnormal warnings.
Manually trigger a knowledge base update. Then, in FastGPT's test chat interface, ask questions related to the new data to verify that AI responses accurately cite the latest information.
Examine FastGPT's internal logs or external system access logs. Confirm that HTTP request headers like Content-Type and User-Agent are as expected, and that the external system returned a 200 status code.
Select typical small molecule drug data records returned by the external system. Compare the corresponding field values in the FastGPT knowledge base to ensure the completeness and accuracy of data parsing and import.

Note: The values provided are common starting points. Measure against your own samples to determine optimal configurations.

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.