Tool Calling and Plugins for Monoclonal Antibody Quality

Data Characteristics

Monoclonal antibody quality documentation primarily consists of laboratory analysis reports, production batch records, stability study reports, and quality standard documents. These documents are typically in PDF, Word, or Excel formats and contain extensive structured and unstructured information. Update frequency correlates with the drug's development stage and production batches; early development might see weekly updates, while post-market updates occur per batch or during annual reviews. Documents have complex structures; for example, batch production records include sections on process parameters, test results, and deviation handling. Fields and units are highly specialized, such as protein concentration (mg/mL), purity (%), glycosylation profiles (relative abundance), and endotoxin content (EU/mg), often accompanied by specific detection methods and limit requirements.

Constraints Imposed by Data Characteristics on Tool Calling and Plugins

The complex structure and specialized fields of monoclonal antibody quality documentation demand high precision in parameter parsing and result validation for tool calls. For instance, extracting purity data for a specific batch from a production record requires the tool to accurately identify the batch number and corresponding purity value in the text, distinguishing between different detection methods. Large volumes of unstructured descriptive text, such as deviation analysis reports, require plugins with natural language understanding capabilities to convert them into actionable structured information. The highly specialized fields and units mean that general-purpose tool libraries are often unsuitable, necessitating the development of customized plugins to handle calculations or validations for specific biological indicators. The uncertain update frequency of documents implies that tools must consider data version management during calls to avoid using outdated information.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	4000 characters	Ensures accommodation of common critical information snippets in monoclonal antibody quality documents, such as a process description or a list of test results.
`Chunk size` (Segment Length)	800–1200 characters	Balances semantic completeness with retrieval efficiency, preventing the splitting of critical data while reducing interference from large text blocks on retrieval accuracy.
`Recall count` (Number of Retrieved Items)	Top 5	Covers the most relevant quality control indicators or batch information, reducing interference from irrelevant information and improving tool call efficiency.
`Similarity threshold` (Similarity Threshold)	0.78–0.85	Adapts to the precise matching requirements of specialized terminology, allowing for some semantic flexibility while ensuring relevance.
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Accounts for the parsing time of large PDF batch production records or stability reports, preventing parsing failures due to timeouts.
`Rerank result count` (Number of Reranked Items)	3 items	Further refines retrieval results, focusing on document segments most directly relevant to tool call parameters, improving subsequent processing accuracy.

Common Pitfalls

Tool call failure, log shows parameter parsing error: This occurs when the parameter format expected by the plugin does not match the data format actually extracted from the document, for example, numerical values containing units or special symbols.
Plugin execution returns empty or inaccurate results: The tool is called, but the returned batch information or test results are not the target data. This happens when the retrieved document segment does not contain complete, accurate information required for the call, or the plugin's internal regular expressions do not correctly match key fields.
AI platform repeatedly indicates a plugin call, but it is not actually triggered: During interaction, the AI model's response explicitly states a plugin call, but system logs do not record the actual execution of that plugin. This might be due to unclear description or parameters definitions for the plugin, leading to model misjudgment during the call decision.

Validation Steps

Use the GET /api/v1/app/dataset/search interface to search using specific batch numbers and quality indicators. Check if the returned document segments contain the expected data fields and units, and verify if the score value is within a reasonable range.
In the FastGPT interface, use the "Test" feature to simulate a query that triggers a tool call. Observe the logs to confirm if tool_code and tool_name match the expected plugin, and check if the plugin's input parameters are correctly parsed.
Write a small automation script to run a batch of typical quality queries for a specific monoclonal antibody product. Compare the tool call results against baseline data to evaluate accuracy and completeness.

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.