Tool Calling and Plugins for CSO Regulations

Data Characteristics

CSO (Contract Sales Organization) regulations and SOP documents in the biopharmaceutical industry are typically stored in PDF, Word, or Excel formats. Content includes sales conduct guidelines, compliance procedures, data reporting requirements, incentive mechanisms, and training materials. These documents have a relatively low update frequency, usually quarterly or annually, revised primarily due to regulatory changes, company strategy adjustments, or product line expansions. Document structures are rigorous, containing numerous nested chapter titles, definitions, flowcharts, and tables. Fields involve sales region codes, product batch numbers, compliance review results, training completion status, and commission calculation rules. Units include currency (Yuan), time (days), and percentages (%).

Constraints on Tool Calling and Plugins

The low update frequency of CSO regulation documents means that model training and knowledge base construction do not require frequent refreshes. However, initial construction must ensure data completeness. Diverse document formats and rigorous structures necessitate detailed parsing and structuring during data preprocessing, especially for flowcharts and tables. This content needs conversion into text or structured data that the model can understand. The specialized nature of fields and the standardization of units require tool calls to accurately identify and transmit this information. For example, when querying commission calculation rules for a specific sales region, the tool needs to precisely extract and process region codes and percentage units. For compliance queries, the tool also needs the ability to call external compliance databases or APIs to verify the real-time validity of regulatory terms.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`2000 characters`	CSO regulation documents have high information density per segment; sufficient context is needed to capture complete semantics.
`Chunk size` (Segment Length)	`800–1000 characters`	Balances semantic completeness and recall efficiency, avoiding excessive fragmentation or information overload in a single segment.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures high-precision matching of regulatory terms and reduces false positives.
`API_TIMEOUT_SECONDS`	`60 seconds`	External compliance databases or business systems may have longer response times; sufficient call duration is reserved.
`TOP_K_RECALL`	`Top 5 entries`	The first few recall results typically cover user intent, reducing the model's processing burden.
`ENABLE_TOOL_FALLBACK`	`true`	Allows the system to attempt alternative solutions or guide the user when the primary tool call fails.

Common Pitfalls

Symptom: The tool call node freezes without response or returns a timeout error. Cause: The external API interface response time exceeds FastGPT's configured API_TIMEOUT_SECONDS limit, or the external service is unavailable.
Symptom: Critical numbers or units are missing from the regulatory terms output by the model. Cause: Structured data in tables or flowcharts was not correctly identified and extracted during document parsing, leading to incomplete context passed to the model.
Symptom: The model cannot effectively answer questions when processing regulatory flowcharts in image format. Cause: Images were not OCR-processed or OCR quality was poor, and Base64 encoded image content was not converted into searchable text.

Verification Steps

Construct a series of questions targeting core regulatory terms. Verify if tool calls accurately return relevant content and check the completeness of the returned information.
Simulate external API errors or timeout scenarios. Check if FastGPT triggers the ENABLE_TOOL_FALLBACK mechanism as expected and provides user-friendly prompts.
Upload regulatory documents containing complex tables and flowcharts. Ask questions related to the data or steps. Check if the model's output accurately includes table data and process descriptions, confirming the effectiveness of document parsing and information extraction.
Review tool call requests and responses through FastGPT's log system. Confirm that parameters are passed correctly and that the external service returns an HTTP 200 status code, indicating a successful call.

The values given 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.