Tool Calling and Plugins for Target Discovery Registration

Data Characteristics in this Category

Registration and submission documents in target discovery involve diverse and heterogeneous data sources. Data typically originates from public databases (e.g., GeneCards, OMIM, DrugBank), patent literature, clinical trial reports, research papers, and internal experimental data. Update frequencies vary; public databases may update monthly or quarterly, while patent and clinical trial data update according to their publication cycles. Document structures are diverse, including unstructured text descriptions (e.g., paper abstracts, full patent texts), semi-structured tabular data (e.g., gene expression profiles, compound activity data), and structured database records. Field and unit specificities include biomolecule names, sequence information, mechanism of action descriptions, dosage units (e.g., nM, µg/kg), biological activity values (e.g., IC50, Ki), and pharmacokinetic parameters. These fields often require specialized bioinformatics knowledge for parsing.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

Data heterogeneity in target discovery requires robust multi-source data integration capabilities for tool calling and plugins. The presence of unstructured text necessitates efficient text parsing tools to extract key information, such as target names, pathways, and disease associations. Frequent data updates, especially in public databases, demand strong data synchronization and incremental processing capabilities from plugins to ensure the timeliness of submission documents. The specialized nature of fields and units means general data cleaning tools may not accurately identify and standardize them, requiring customized data preprocessing plugins to handle biomolecular dimensions and activity data. For example, IC50 values are often expressed in different orders of magnitude and need conversion to standard units. Furthermore, the complex structure of patent and clinical trial reports requires tools to process long texts and documents with mixed tables and figures, accurately extracting necessary information. This directly impacts the recognition accuracy and recall rate of the tools.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`8192`	Target discovery documents often contain lengthy descriptions and experimental data, requiring a large context window for information completeness.
`Chunk size`	`500` characters	Ensures each text segment contains sufficient semantic information while avoiding excessive length that could reduce processing efficiency.
`Recall count`	`8` entries	Balances recall rate and response speed by considering information density and model processing capabilities.
`Similarity threshold`	`0.75`	Increases the relevance of recalled results and reduces noise, especially for specialized terminology in biomedicine.
`PARSE_FILE_TIMEOUT_SECONDS`	`600` seconds	Processing large patent documents and clinical reports can be time-consuming; this prevents timeouts.
`stream`	`true`	Enables streaming for model calls that require real-time feedback or processing large amounts of intermediate results.

Common Pitfalls

Call logs show Invalid JSON payload received. Unknown name: This typically occurs when the body parameter passed to an external API during tool invocation does not conform to the target API's JSON specification. This may be due to incorrect field names or data type mismatches.
Data acquisition exceptions and error: This happens when an external data source (e.g., a specific target database API) returns a non-standard status code or the data structure does not match expectations. The API response parsing logic in the tool configuration needs checking.
Text extraction tools return empty or incomplete results: This may be due to complex document formats, such as those containing many tables, images, or special characters, leading to incorrect text content recognition by the document parser during the preprocessing stage. Document parsing strategies need optimization.

Verification Steps

In the FastGPT platform, select a document containing typical target discovery data. Execute a tool call and check if the returned results include all expected key information, comparing them against the original document.
For a document containing biological activity data (e.g., IC50 values), run the tool and verify that the extracted values are correct and that units are standardized to the expected format.
Through the call logs, check if the statusCode for each tool call is 200 or another success status code, and confirm that the duration is within an acceptable range.
Simulate a data source update. Run the relevant data synchronization or processing tool and verify that new or modified data is accurately identified and integrated.

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