Tool Calling and Plugins for Target Discovery Clinical Trial

Data Characteristics in This Category

Target discovery involves diverse data sources, including public databases (e.g., NCBI Gene, UniProt, PDB, ChEMBL), specialized literature repositories, patent data, and high-throughput experimental data (e.g., genomics, transcriptomics, proteomics). Data update frequencies vary; public databases typically update quarterly or monthly, while experimental data might be generated in real-time. Document structures are complex, often containing semi-structured text (e.g., experimental reports, paper abstracts) and structured data (e.g., gene sequences, protein structures, compound physicochemical properties, drug mechanisms of action). Fields are diverse, covering gene IDs, protein IDs, compound SMILES codes, target sites, IC50 values, KD values, etc. Units include nM, µM, Da, bp, among others, and are often accompanied by experimental conditions and confidence indicators.

Constraints Imposed by These Characteristics on "Tool Calling and Plugins"

The diversity of data sources requires tool calling to flexibly integrate with various API interfaces and handle different authentication methods. Varying update frequencies, especially for experimental data, necessitate plugin designs that consider caching strategies and data freshness validation mechanisms to avoid using outdated information. The complexity of document structures means that data extraction requires a combination of regular expressions, NLP techniques, and even graph parsing capabilities to identify and extract key entities and relationships. The specificity of fields and units, such as the conversion between nanomolar (nM) and micromolar (µM) for IC50 values, and the validation of data range validity, demands that plugins integrate specialized bioinformatics computation and validation logic during the data preprocessing stage. Furthermore, the presence of confidence indicators suggests that plugin design should consider criteria for result ordering or filtering, ensuring that high-confidence information is prioritized.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`tool_api_timeout`	`60 seconds`	Bioinformatics database queries can be time-consuming; allow sufficient time for requests to complete.
`max_tokens_per_response`	`4000`	Ensure the capacity to fully convey complex experimental descriptions, gene sequences, or compound structure information.
`retrieval_top_k`	`Top 5 entries`	Early stages of target discovery require broad exploration to recall more potentially relevant results for screening.
`similarity_threshold`	`0.78`	Balance recall and precision, avoid interference from irrelevant literature, while not missing important clues.
`json_parse_strategy`	`strict`	Ensure structured data returned from external APIs strictly conforms to the expected format, preventing parsing errors.
`cache_ttl_seconds`	`86400 seconds`	For public databases with lower update frequency, set a 24-hour cache to reduce redundant requests.

Three Common Pitfalls

Tool calls return XML or JSON code blocks without rendering charts. This might be due to an incorrect response_parser configuration, failing to properly identify and convert the API's returned data format into the format required by visualization components.
API call results differ significantly from online chat results, with API call results being incorrect. This typically occurs when system_prompt or user_prompt are not fully passed or are incorrectly formatted during the API call, leading to model misinterpretation.
Plugin execution times out or returns empty data. This phenomenon might manifest as a tool_api_timeout error, potentially caused by slow responses from external data sources or missing essential query parameters in the request_body.

How to Confirm Proper Configuration

Use FastGPT's debugging interface to observe the request_body and response_body of each tool call, confirming correct parameter passing and expected response data structure.
For core query scenarios, perform multiple end-to-end tests with varying query complexity. Compare the model's output with expected key information (e.g., target name, mechanism of action, IC50 values).
Check the logging system to confirm no tool call errors with HTTP 4xx or 5xx status codes, and no parsing exceptions like json_parse_error.
Validate data freshness. For data sources known to have updates, query again after the cache period expires to confirm retrieval of the latest information.

The values given 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.