Tool Calling and Plugins for Solid Tumor Quality Documents

Data Characteristics

Solid tumor quality documents primarily source data from clinical trial reports, pathology diagnostic reports, treatment guidelines, drug inserts, and relevant regulatory files. Update frequencies vary; clinical trial data and drug inserts update regularly with research progress and post-market surveillance, while regulatory documents typically revise after policy promulgation. Document structures often contain extensive unstructured text but also include structured or semi-structured data, such as tabular clinical indicators, dosage instructions, and adverse event lists. Fields and units involve tumor size (millimeters or centimeters), pathological grade (e.g., G1-G3), molecular marker expression (e.g., HER2 positive/negative, PD-L1 TPS percentage), drug dosage (milligrams, milligrams/kilogram), and treatment cycles (days, weeks), demanding high precision and unit consistency.

Constraints on Tool Calling and Plugins

The data characteristics of solid tumor quality documents impose specific constraints on tool calling and plugin functionalities. First, diverse document sources and varying update frequencies require tool calling to flexibly integrate with multiple data sources and possess version management capabilities. This ensures retrieval and analysis of the latest, most authoritative information. Second, the mix of structured and unstructured data in documents necessitates that plugins handle both text comprehension and table parsing to extract key information, preventing omissions or misinterpretations. For example, extracting the objective response rate (ORR) of a specific drug in a particular solid tumor type from a clinical trial report may require parsing multiple tables and paragraphs. Third, strict field and unit requirements demand that plugins standardize units during numerical comparisons or calculations to prevent result deviations due to inconsistent units. Finally, sensitive information like drug dosages and treatment cycles places extremely high demands on plugin accuracy and reliability; any parsing error could lead to severe consequences.

Configuration Parameters

Configuration Item	Suggested Value	Rationale
`maxContext`	`3000-4000 characters`	Balances context length with model processing efficiency, ensuring coverage of most solid tumor-related paragraphs.
`Similarity Threshold`	`0.75-0.85`	Increases the relevance of recall results, reducing interference from irrelevant regulations or non-core treatment plans.
`Recall Count`	`8-12 items`	Given the complexity of solid tumor documents, increasing the recall count appropriately covers more potential information points.
`Reranked Return Count`	`3-5 items`	Filters out the most core and directly relevant document snippets, improving user efficiency in acquiring information.
`PARSE_FILE_TIMEOUT_SECONDS`	`300-600 seconds`	Solid tumor documents may contain numerous charts and complex layouts, requiring ample time for parsing.
`API_KEY_ROTATION_INTERVAL`	`7 days`	Given the data security sensitivity in the biomedical field, regular rotation of third-party API keys is recommended.

Common Pitfalls

401 Unauthorized error when calling a third-party API. This occurs when the plugin's authentication key expires or is misconfigured, preventing access to external services.
Knowledge base Q&A training status remains "training" for an extended period. This happens when the data volume is too large or contains abnormal characters, causing the parser or model training task to stall.
Numerical results returned by tool calls do not match expectations, such as inconsistent tumor size units. This indicates the plugin did not standardize units during document parsing, or the API return data format was not correctly mapped.

Verification Steps

Simulate requests to call external tool APIs related to solid tumor treatment plans. Check if the returned status code is 200 OK and verify that the returned data structure matches expectations.
Upload solid tumor clinical trial reports containing complex tables to the knowledge base and trigger parsing. Check parsing logs for WARN or ERROR level errors and verify that key fields like drug dosage and PFS (progression-free survival) are correctly extracted.
In a FastGPT workflow, configure a plugin that calls an external drug database API. Input a known solid tumor drug name and verify that the drug information returned by the plugin (e.g., mechanism of action, adverse reactions) completely matches the official data in the database.
Use FastGPT's debugging tools to perform an end-to-end test on a solid tumor diagnosis and treatment pathway involving multiple tool calls. Observe the input parameters and output results of each plugin call to ensure data flow and processing logic meet business requirements.

The values provided 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.