Tool Calling and Plugins for Target Discovery Policies

Data Characteristics in This Category

Target discovery data is highly specialized. It primarily originates from biomedical literature, clinical trial reports, patent information, public databases (e.g., NCBI, UniProt, DrugBank), and internal experimental data. Update frequencies vary; public databases might update weekly or monthly, while internal experimental data is generated in real-time. Document structures are complex, often containing extensive unstructured text, images, tables, and chemical formulas. Field types are diverse, covering gene sequences, protein structures, compound properties, biological pathways, mechanisms of action, clinical symptoms, and toxicology data. Units include nanomolar (nM), micromolar (µM), Kelvin (K), and Dalton (Da). Data is highly interconnected, typically requiring complex ontologies and knowledge graphs for organization.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The characteristics of target discovery data impose specific constraints on tool calling and plugins. First, the massive and continuously updated heterogeneous data sources require tools to flexibly integrate with various APIs and data formats. Examples include parsing PubMed XML data or UniProt FASTA format. Second, identifying biological entities and extracting relationships from unstructured text is critical. This requires integrating Named Entity Recognition (NER) and Relationship Extraction (RE) plugins to convert text into structured knowledge for subsequent queries. Third, the accuracy of specialized terminology and units is crucial. Tool calls must ensure consistent unit parameters to avoid errors caused by incorrect unit conversions. Finally, the high data interconnectivity means a single tool is often insufficient. Complex queries require orchestrating multiple plugins. For instance, one plugin might retrieve gene information, and its results are then passed to another plugin to query related compounds.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`external_api_timeout`	`600 seconds`	Target discovery data queries often involve multiple external API calls and large data volumes, requiring longer response times.
`max_tokens`	`4096`	Target discovery policies and SOP documents are rich in detail, requiring the large model to generate longer responses for comprehensive information.
`chunk_size`	`800–1200 characters`	Ensures each text chunk contains sufficient contextual information for the large model to understand specialized terminology and complex concepts.
`recall_top_k`	`top 5`	Knowledge base recall for target discovery requires high relevance. The top 5 balances breadth and precision.
`similarity_threshold`	`0.75`	Increases the similarity threshold to ensure recalled documents are highly relevant to the query content, reducing noise.
`plugin_retry_limit`	`3 times`	External data source APIs occasionally experience transient failures. Increasing retries improves success rates.

Three Common Pitfalls

Tool call returns HTTP 500 or Gateway Timeout: This usually indicates the external data source API response time exceeded FastGPT's external_api_timeout setting.
AI response contains "citation mark: [1]" or similar, or cited content does not match the answer: This occurs when knowledge base recall text snippets are not effectively processed and are directly fed into the large model as context, causing the model to misinterpret them as citation format.
Key fields are empty or data types are incorrect: When data returned by an external tool is missing expected fields like target names, gene sequences, or compound IDs, or returns unexpected data types (e.g., a string for a numeric field), it indicates an error in the tool's parameter mapping or data parsing configuration.

How to Verify Configuration

For typical target discovery policy or SOP questions, observe if the AI response accurately cites knowledge base content and seamlessly integrates real-time data from external tools.
Check tool call logs to confirm all external API calls return an HTTP 200 status code and response times are within expectations.
Verify specialized terms such as targets, genes, and compounds in the AI response against actual data returned by external tools, ensuring no spelling or unit errors.

Note: The values provided are common starting points and should be measured 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.