Data Characteristics in this Category
Target discovery data primarily consists of biomolecular sequences, structural information, mechanisms of action, and related literature. Data sources are diverse, including public databases (e.g., NCBI, UniProt, PDB) and internal experimental data. Update cycles depend on public database release schedules, typically quarterly or semi-annually. Internal data generates in real-time based on experimental progress. Document structures are complex, often including JSON for gene/protein annotations, XML for pathway maps, PDF for preclinical research reports, and CSV for high-throughput screening results. Fields and units are specific, such as gene_id, protein_sequence, IC50 (unit nM), and binding_affinity (unit Kd). Data volumes are large; a single request may involve tens of thousands of sequences or thousands of compound data points.
Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"
The wide range of target discovery data sources requires FastGPT's HTTP interface to support flexible authentication mechanisms. This accommodates various public database API Keys or OAuth 2.0 tokens. The non-real-time nature of data updates, especially from public databases, impacts caching strategy. Set reasonable cache expiration times to avoid frequent, redundant data fetching. The complex document structure makes parsing interface return data difficult. The system needs to automatically identify and convert multiple data formats, for example, transforming XML into an easily processable JSON structure. Specific fields and units require strict parameter validation and data type conversion during interface calls. For instance, parse string-formatted IC50 values into floating-point numbers and unify units to ensure accuracy in subsequent knowledge base embedding and retrieval. Large data volumes demand robust concurrent processing capabilities and appropriate timeout settings for the interface. This may require support for paginated queries or asynchronous task processing.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
HTTP_REQUEST_TIMEOUT_SECONDS | 300 seconds | Target discovery data is large; some requests take longer to process. Avoid timeouts. |
MAX_CONCURRENT_REQUESTS | 10 | Balance external system load with FastGPT's processing capacity. Prevent overload. |
API_KEY_ENV_VAR | TARGET_DB_API_KEY | Manage sensitive credentials using environment variables to enhance security. |
JSON_PATH_EXTRACTION_RULES | {"gene_name": "$.gene_info.name"} | Precisely extract deeply nested JSON fields to obtain critical target information. |
DATA_TRANSFORMATION_SCRIPT | Python script, unify IC50 units to nM | Standardize numerical data from different sources for subsequent analysis. |
RETRY_STRATEGY | Exponential backoff, max 5 retries | Handle transient external system failures or network fluctuations. Improve data acquisition success rate. |
Three Common Mistakes
- An external API returns a 401 or 403 status code. This usually indicates the
API_KEY_ENV_VARenvironment variable is incorrectly set or expired. - Interface returns empty data fields or type errors. This often occurs because
JSON_PATH_EXTRACTION_RULESare inaccurately defined and fail to correctly match the deep structure of target data. - Interface calls remain unresponsive for an extended period and eventually time out. This typically happens when
HTTP_REQUEST_TIMEOUT_SECONDSis set too short, not accounting for the complexity of target data retrieval.
How to Confirm Correct Configuration
- Simulate an interface call. Check if the returned HTTP status code is 200. Confirm the response body contains expected target sequences or structural information.
- Review FastGPT's internal logs. Confirm no errors occurred during data extraction and transformation. Verify that key fields (e.g.,
gene_id,IC50) have the expected data types and units. - Conduct end-to-end tests with a small number of representative target data samples. Validate the entire pipeline from external system retrieval to knowledge base embedding. Confirm relevant content is retrievable via keyword search.
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.