Tool Use and Plugins for CSO Registration and Declaration

Data Characteristics in this Category

CSOs (Contract Sales Organizations) handle diverse data types during registration and declaration document preparation. These primarily include clinical trial reports, pharmaceutical research data, non-clinical research reports, regulatory documents, and various administrative licenses. Data originates from sponsors, CROs (Contract Research Organizations), or internal R&D departments. Data update frequencies vary; clinical data may update incrementally with trial progress, while regulatory documents are revised based on national drug regulatory authority publication cycles. Document formats are mainly PDF, Word, and Excel, with some structured data in CSV or XML. Field and unit specificity demands high standardization and rigor for drug names, active ingredients, dosages, batch numbers, manufacturing process parameters, stability data, pharmacokinetic parameters, and toxicology indicators. For example, AUC (Area Under the Curve) units are ng·h/mL, and Cmax (peak plasma concentration) units are ng/mL.

Constraints on Tool Use and Plugins from These Characteristics

CSO registration and declaration data characteristics impose several constraints on tool use and plugins. First, the timeliness requirement for regulatory documents necessitates frequent external API calls to retrieve the latest regulatory updates, ensuring compliance of declaration materials. Second, clinical trial reports and pharmaceutical research data are often lengthy, dense, unstructured documents. This challenges the LLM's context window size and information extraction capabilities, requiring preprocessing mechanisms like segmentation and summarization. Third, diverse data formats demand robust file parsing capabilities from tools, especially for structured extraction of tables and charts from PDFs. Finally, the strictness required for key drug fields and units means plugins must achieve high accuracy during data extraction and transformation. They must also identify and handle potential unit inconsistencies or data format errors, for instance, ensuring pH values are reported as unitless numbers and solubility as mg/mL.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8192 token`	Balances long document processing with response speed and cost.
`Chunk size` (Segment Length)	`500 characters` (characters)	Adapts to paragraph structures in regulatory texts and clinical reports, minimizing information loss.
`Recall count` (Recall Count)	`Top 10 entries` (top 10)	Ensures coverage of highly relevant regulatory clauses and experimental data.
`Similarity threshold` (Similarity Threshold)	`0.75`	Improves recall precision, filtering out irrelevant auxiliary information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Handles complex parsing of large PDF and Word documents.
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Supports uploading declaration documents containing numerous charts and attachments.

Three Common Mistakes

When calling an external regulatory database API, messages is empty returns. This indicates an incorrect API key configuration or an empty query request parameter.
The model responds slowly. After deploying FastGPT locally, curl calls to the model API are fast. This usually results from local deployment environment network configuration or insufficient GPU resources causing model inference delays.
When extracting clinical data, the AUC field value is empty. This occurs because table parsing within the PDF file failed, preventing correct identification of field-value mappings.

How to Verify Correct Configuration

Use an external regulatory query tool plugin. Input a specific regulatory clause number and verify that the returned regulatory text content matches the official publication.
Upload a multi-page clinical trial report PDF file. Observe the file parsing progress and retrieve key data points (e.g., drug dosage, administration route) from the report within the knowledge base. Check their accuracy and completeness.
Configure a tool to extract specific pharmaceutical parameters. Input a drug instruction manual and verify that the model's output for fields like active ingredient, specifications, and indications is correct. Also, confirm that units like mg and ml are correctly identified.

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.