Data Characteristics for this Category
Bispecific antibodies are an emerging drug modality. Related regulatory and SOP documentation data primarily originates from technical guidance published by regulatory agencies, internal quality management system documents from pharmaceutical companies, clinical trial protocols, and manufacturing process specifications. These documents are typically in PDF, Word, or structured XML formats. Update frequency is relatively low, occurring mainly when regulatory policies change, new drug development progresses, or manufacturing processes are optimized. Document structures are complex, containing extensive specialized terminology, charts, flowcharts, and cross-references. Fields and units involve antibody domains, target affinity, production batches, purity, potency, stability, and dosage. These often include biomedical-specific units such as International Units (IU), molar concentration (nM), milligrams per milliliter (mg/mL), and percentages (%).
Constraints Imposed by these Characteristics on Tool Calling and Plugins
The complexity and specialized nature of bispecific antibody regulatory documents demand high accuracy for tool calling and robust plugin processing capabilities. Embedded charts and flowcharts in documents are difficult for text models to understand directly, requiring image recognition or specialized structured extraction plugins for preprocessing. Extensive specialized terminology and cross-references require tools to identify and link concepts across different documents during semantic understanding, preventing information silos. The characteristic of infrequent but large-volume updates means knowledge base construction needs to support version management and trigger comprehensive document parsing and index rebuilding upon updates. Furthermore, specific units and numerical values require plugins to correctly parse units and unify dimensions during calculations or data comparisons, avoiding misjudgments due to unit discrepancies. For critical fields like production batch and purity, tools need to extract data precisely and support real-time comparison with external databases, for example, by integrating with manufacturing management systems or quality control systems.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 500–800 characters | Balances semantic completeness with recall efficiency, preventing long texts from diluting key information. |
overlapSize | 100 characters | Ensures contextual continuity, reducing semantic breaks caused by chunk boundaries. |
embeddingModel | text-embedding-ada-002 or higher | Provides good understanding of biomedical terminology and high-dimensional vectors. |
maxContext | 16384 tokens | Accommodates long text inputs from complex regulatory documents, avoiding truncation of critical information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accounts for potentially long parsing times for large PDF or Word documents. |
similarityThreshold | 0.75 | Ensures high relevance of recall results to regulatory content, reducing the risk of misjudgment. |
Three Common Mistakes
- External model calls return empty results, appearing as
response body is emptyornull. This can happen if the model poorly handles complex document structures or special character encoding, leading to parsing failure. - After calling an API for file parsing, the call shows success but no parsed results are returned. This might be due to an incorrectly configured
callback URL, preventing the parsing service from returning results to the FastGPT platform. - Numerical discrepancies occur during data comparison, for example,
100 mgis incorrectly identified as100 g. This happens when the unit parsing plugin is not correctly configured or does not cover all commonly used biomedical units.
How to Verify Configuration
- Upload a bispecific antibody SOP document containing charts and specialized terminology. Check if the knowledge base correctly extracts text content and can semantically retrieve key concepts from the document.
- Configure a tool call to extract purity or potency data for a specific batch from the document. Compare this with an external database and verify that the numerical values and units in the returned results are consistent.
- Simulate a regulatory update by replacing an old document version. Verify that the knowledge base can identify version differences and accurately answer questions based on the new content after the update.
The values provided are common starting points and should be measured against specific 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.