Data Characteristics in This Category
Target discovery involves diverse data sources. These include genomics, proteomics, metabolomics, phenomics data, public literature databases (e.g., PubMed, DrugBank), clinical trial data, and patent information. Data update frequencies vary. Basic research data might update monthly, while clinical trial and patent data update weekly or in real-time. Document structures are often complex, containing text descriptions, structured tables (e.g., gene sequences, protein domains, pathway information), images (e.g., Western Blot, immunohistochemistry), and bioinformatics analysis reports. Fields and units are highly specialized. Examples include gene IDs (e.g., HGNC:1097), protein sequences (FASTA format), expression levels (e.g., FPKM, TPM), and affinity constants (e.g., Kd, in nM). Different data sources may use varying naming conventions or versions.
Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"
The diversity and complexity of target discovery data place higher demands on HTTP interfaces. Varied data source update frequencies require external systems to support periodic or event-driven data synchronization mechanisms to ensure real-time information. Complex document structures necessitate HTTP interfaces capable of handling multiple data formats, such as JSON, XML, and binary files (e.g., images, sequence files). Specialized fields and units mean that data transmission and parsing require strict type validation and unit conversion logic to prevent data confusion or misinterpretation. For example, gene ID mapping might require calling additional conversion services. Furthermore, data volumes are typically large, challenging interface transmission efficiency and stability. This may require support for chunked transfer or asynchronous processing to prevent request timeouts.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Request timeout (Request Timeout) | 300 seconds (300 seconds) | Target discovery data queries are complex and require longer processing times. This prevents interruptions due to excessively long query execution. |
Max Concurrent Requests | 5 | External databases have protection mechanisms against high concurrency to prevent service denial from too many requests and maintain system stability. |
Chunk size (Chunk Length) | 800–1200 characters (800–1200 characters) | Literature abstracts and report texts are long. Chunking improves model processing efficiency and reduces the load of a single request. |
Data Parser | JSON/XML/FASTA | Target data involves multiple formats. The appropriate parser must be selected based on the interface return type. |
Retry Strategy | Exponential backoff, max 5 retries | External systems experience occasional failures. A retry mechanism improves data acquisition success rates and prevents failures due to transient errors. |
Authentication Method | API Key/OAuth2 | Ensures secure data access. Select the appropriate authentication protocol based on external system requirements. |
Three Common Mistakes
- Symptom: HTTP request returns a 504 Gateway Timeout error. Reason: Complex queries and external system processing times exceed FastGPT's or the proxy layer's default timeout settings.
- Symptom: Specific fields (e.g.,
gene_symbol) are empty or malformed in SQL query results returned from the database. Reason: The SQL statement generated by the large language model did not account for database version or dialect differences when extracting fields, leading to mismatched field names or incompatible data types. - Symptom: The HTTP interface returns a large amount of data, but subsequent processing nodes cannot effectively parse it or encounter memory overflow. Reason: Long text or large data volumes were not effectively chunked, leading to an excessively high load for single-pass processing.
How to Confirm Correct Configuration
- Simulate complex target discovery queries to check if the HTTP interface consistently returns data within the
Request timeout(Request Timeout). - Verify that the data parser correctly identifies and extracts key fields like gene IDs (e.g.,
HGNC:1097) and protein sequences, ensuring field values match expectations. - Check external system logs to confirm that the
Max Concurrent Requestssetting does not lead to frequent connection rejections or rate limiting messages. - Test data transmission involving long texts (e.g., literature abstracts) to confirm that after setting the
Chunk size(Chunk Length), the text is correctly segmented and processed in batches.
Note: The values provided are common starting points. They 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.