HTTP Interface and External Systems for Lead Optimization

Data Characteristics in this Category

Lead optimization data primarily originates from high-throughput screening, in vitro activity testing, in vivo pharmacodynamic studies, and ADME/Tox predictions. This data typically includes both structured data (e.g., compound structures, IC50 values, PK parameters) and unstructured data (e.g., experimental reports, spectra, literature). Data updates are frequent, especially during compound iteration and activity validation phases, with new experimental results potentially generated daily. Document structures are complex, often containing fields like compound ID, batch information, test methods, result values, units, and statistical analysis. For example, IC50 values are usually expressed in nanomolar (nM) or micromolar (µM), and solubility might be in µg/mL or mg/L. These units require standardization during data integration.

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

Frequent data updates require HTTP interfaces to support efficient data synchronization. This ensures the timeliness and accuracy of registration and submission materials. The heterogeneous nature of multi-source data means interfaces must parse and convert various data formats. This includes handling structured data in JSON and experimental reports in PDF or TIFF. Complex document structures and diverse field units demand high standards for data cleaning and standardization. Interface design needs flexibility for field mapping and unit conversion. Given the sensitivity of biomedical data, interface security and access control are critical. Data transmission must be encrypted, and only authorized users should access it. For large volumes of unstructured documents, file upload interfaces must support large file transfers and resume capabilities. Stability of file parsing services is also a consideration.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`API_TIMEOUT_SECONDS`	`120 seconds`	Accommodates response times for large-scale data uploads or complex analysis tasks, preventing timeouts.
`MAX_FILE_UPLOAD_SIZE_MB`	`500 MB`	Meets the requirements for uploading PDF files containing high-resolution spectra or multi-page scanned reports.
`DATA_SYNC_INTERVAL_MINUTES`	`15 minutes`	Balances data real-time requirements with system load, ensuring timely synchronization of lead optimization data.
`CONCURRENT_REQUEST_LIMIT`	`20`	Limits the number of concurrent requests, preventing backend service overload and maintaining system stability.
`METADATA_FIELD_MAPPING`	Map `compound_id` to `CID` based on actual business system field names	Unifies field names from different data sources, facilitating subsequent knowledge base retrieval and RAG.
`ERROR_RETRY_COUNT`	`3 times`	Addresses network fluctuations or transient service unavailability, improving data transmission success rates.

Common Pitfalls

HTTP requests remain in a long connection waiting state. This can occur if backend services take too long to process complex data or large files, leading to slow interface responses.
Model testing errors, API Key authentication failures, or Ollama image version incompatibilities often result from incorrect API_KEY configuration or local model environments that do not meet interface requirements.
Business systems cannot distinguish between different users' chat records. This happens when user identifiers like user_id or session_id are not correctly passed in the API request headers or body.

Verification Steps

Use an API testing tool (e.g., Postman) to simulate data submission. Check if the HTTP status code is 200 OK or 201 Created and verify if the data in the response body matches the expected structure.
Upload a PDF file containing typical lead optimization data. Observe the file upload progress and the file parsing service logs. Confirm that the PARSE_FILE_TIMEOUT_SECONDS configuration allows for successful file parsing.
In the FastGPT backend knowledge base, retrieve the newly synchronized data. Confirm that key fields like compound ID and activity values are accurately identified and extracted, and that unit conversions are correct.
Initiate conversations using different user_id values. Check if the FastGPT platform correctly creates independent chat sessions for each user and saves their respective chat histories.

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.