HTTP Interface and External Systems for Target Discovery

Data Characteristics

Data related to target discovery regulations primarily originates from bioinformatics databases, clinical trial reports, internal research documents, and regulatory files. Update frequencies vary. Foundational databases (e.g., GenBank, UniProt) update monthly or quarterly. Clinical trial data and regulatory documents update in real-time based on project progress or policy releases. Document structures are largely semi-structured, containing extensive text descriptions, charts, and tabular data. Common fields include gene ID, protein sequence, disease association, drug mechanism of action, toxicity data, and approval status. Units frequently used include concentration (nM), dosage (mg/kg), time (h, day), and effect values (e.g., IC50, EC50), which are standard biochemical and pharmacological units. Some data exists in PDF or Word formats, including complex diagrams and experimental procedure descriptions.

Constraints from "HTTP Interface and External Systems"

The diversity of data sources requires HTTP interfaces to be highly flexible. They must integrate with various data formats, such as retrieving structured data via RESTful APIs or processing unstructured documents via file upload interfaces. Inconsistent update frequencies challenge caching strategies and data synchronization mechanisms. Appropriate synchronization cycles or event-triggered mechanisms must be selected based on data source characteristics. Complex charts and tables in semi-structured documents require robust parsing capabilities, potentially necessitating external document parsing services. The specificity of fields and units means data cleaning and standardization might be needed before ingestion into FastGPT. For example, drug concentration units from different sources might need unification. Additionally, when processing large volumes of biological sequence and structural data, interface transmission efficiency and security become critical considerations.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Target discovery documents often contain high-resolution images and complex charts, leading to large file sizes.
`Chunk size`	`800–1200 characters`	Ensures each text segment contains sufficient context while preventing excessively long segments that reduce recall efficiency.
`Similarity threshold`	`0.75`	Target discovery regulation Q&A demands high accuracy. A high threshold helps exclude irrelevant results.
`Recall count`	`Top 10 entries`	Increases the number of recalled items to cover more potentially relevant information, addressing the complexity of regulatory texts.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large files is time-consuming. This allows ample time to prevent parsing interruptions.
`Rerank result count`	`Top 5 entries`	Re-ranks recalled items to ensure the most relevant answers are presented.

Common Pitfalls

Calling an external system results in an HTTP 400 Bad Request error. This often occurs when biological sequence data in the request body does not conform to the external system's expected format.
Uploading a PDF file with complex diagrams leads to a lack of diagram-related information in the knowledge base's answers. This usually indicates the document parser failed to correctly extract text or tabular data from images.
API calls to external tools return empty JSON fields. This might be due to the external tool timing out or its internal logic failing to process the input data successfully.

Verification Steps

Upload a PDF file containing a complex experimental workflow diagram. Ask questions about the steps involved and verify if the answer accurately references key information from the diagram.
Use FastGPT's HTTP interface to call an external target prediction service. Check if the gene_id and target_protein fields in the returned data match expectations.
Simulate different data update frequencies. Observe if relevant regulatory document versions in the FastGPT knowledge base are synchronized promptly.

Note: The values provided are common starting points. Measure them 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.