Data Characteristics
Antibody-Drug Conjugate (ADC) regulations and Standard Operating Procedure (SOP) documents typically exist as PDFs, Word files, or internal knowledge base pages. These documents are highly specialized, covering drug research and development, manufacturing, quality control, clinical trials, and post-market surveillance. Data update frequency is relatively low, primarily occurring with regulatory changes, new drug development progress, manufacturing process optimization, or quality management system updates. Document structures are rigorous, containing numerous technical terms, acronyms, charts, flowcharts, and cross-references. Fields include batch numbers, product codes, reagent lot numbers, equipment calibration dates, operation step sequences, quality standard limits, and deviation handling procedures. Units strictly adhere to pharmacopoeia or industry standards, such as concentration (mg/mL), temperature (°C), time (hours/days), pH values, and purity percentages, all requiring extremely high precision.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The specialized and rigorous nature of ADC regulations and SOP documents demands high accuracy from tool calling and plugins during data parsing, especially for recognizing numbers, units, and technical terms. Complex cross-references and flowcharts in documents mean that simple text segmentation and retrieval may not capture complete contextual information. Plugins need to understand document structure and perform relational queries. Since data updates are infrequent but impactful, plugins should identify document versions and ensure the latest approved SOP is called. Additionally, the abundance of technical terms and acronyms requires plugins to possess semantic understanding capabilities specific to the biomedical field to avoid misinterpretations due to term ambiguity. For critical areas like quality control, plugin call results must be precise down to specific values and units; any deviation could lead to severe consequences.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4096 tokens | Ensures capacity for key paragraphs and cross-reference information from complex SOP documents. |
chunkSize | 800–1200 characters | Balances the completeness of technical terms and contextual relevance, avoiding excessive fragmentation. |
overlapSize | 100 characters | Ensures semantic continuity between paragraphs and handles professional expressions that span paragraphs. |
Similarity threshold (Similarity Threshold) | 0.85 | Addresses the need for precise matching of technical terms and exact numerical values, improving retrieval accuracy. |
Recall count (Number of Retrieved Items) | Top 8 entries (Top 8) | Increases the probability of retrieving highly relevant SOP steps that may be dispersed across different sections. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large PDF or Word documents, preventing processing failures due to timeouts. |
Three Common Pitfalls
- An
Error: write EPROTwhen calling an external system interface usually indicates network configuration or SSL certificate issues in the FastGPT deployment environment, preventing a secure HTTPS connection. - Tool calling node output does not contain an AI response, returning null or incomplete information. This may be due to an incorrectly defined
Output Content Formatin the tool configuration, or the tool's returned data structure does not match expectations. - Knowledge base query results fail to accurately answer quality standard questions for a specific batch number. This often happens if document chunking granularity is too large, preventing the association of batch numbers with corresponding standards, or if there is a lack of precise matching mechanisms for numbers and units.
How to Verify Correct Configuration
- Upload a typical ADC SOP document. Use the knowledge base testing feature to verify accurate parsing of document content and correct identification of technical terms and numerical values.
- Formulate questions involving complex cross-references. Test whether tool calling can retrieve complete and associated SOP steps or regulatory descriptions via plugins. Compare the retrieved information with the original document for completeness.
- Simulate a query for quality standards of a specific batch product. Check if the numerical values and units returned by tool calling exactly match the limits defined in the document and verify their precision.
Note: The values provided are common starting points. They 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.