Data Characteristics
CDMO (Contract Development and Manufacturing Organization) regulation and SOP documents are typically in PDF, Word, or internal knowledge base HTML formats. These documents are highly structured, containing extensive standard operating procedures, quality management systems, regulatory compliance requirements, and technical guidelines. Data update frequency is relatively low, primarily occurring quarterly or semi-annually after regulatory changes, process improvements, or audit requirements are released. Documents include numerous tables, flowcharts, and specialized terminology. Fields may involve batch numbers, inspection standards, equipment parameters, and operation step numbers. Units cover mass (mg, g), volume (mL, L), and time (min, h), often with specific abbreviations and internal codes.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The structured nature of CDMO regulation documents demands high precision in parameter extraction and result parsing for tool calling and plugins. Due to the low document update frequency, stable knowledge graphs or structured data indexes can be pre-built, reducing the need for real-time data retrieval. Specialized terminology and internal codes in documents require plugins to have accurate entity recognition capabilities to avoid ambiguity when converting natural language queries into structured queries (e.g., SQL). Extensive table and flowchart information requires plugins to parse complex layouts, ensuring data completeness. Furthermore, the accuracy of critical fields like batch numbers and inspection standards directly impacts production compliance, making result validation after tool calling particularly important and requiring additional verification mechanisms.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 4000 | Regulation documents are often long, requiring a larger context window to capture complete information. |
Chunk size | 800–1200 characters | Balances semantic completeness and segment recall efficiency, preventing loss of key information in long paragraphs. |
Similarity threshold | 0.75 | Ensures recalled regulation entries are highly relevant to the query, filtering out irrelevant content. |
Recall count | 5 | Regulation Q&A often requires a small number of highly precise entries to support the answer. |
Rerank result count | 3 | Re-sorts recalled results to further prioritize the presentation of the most relevant content. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | CDMO documents may contain complex charts and large amounts of text, potentially requiring longer parsing times. |
Three Common Pitfalls
- Symptom: A database query tool returns an SQL statement that fails to execute, indicating a non-existent table name or field. Reason: The AI failed to accurately identify the agreed-upon database table names and field mappings from the regulation documents when generating SQL, or the plugin incorrectly handled case sensitivity when passing parameters.
- Symptom: When a user queries an operation step, the tool call returns results missing critical equipment parameters or inspection standards. Reason: The plugin failed to effectively parse associated information from tables or flowcharts in the regulation documents when extracting specific entities, leading to incomplete parameters.
- Symptom: When querying compliance requirements for a specific batch number, the returned result is empty or inaccurate. Reason: Batch number formats in regulation documents vary, and the plugin failed to cover all possible regular expressions or matching patterns, preventing correct batch information identification from the text.
How to Confirm Proper Configuration
- For core regulation documents, perform a series of Q&A sessions involving key entities and processes. Compare whether the AI-generated tool call parameters are accurate and complete.
- Simulate database query scenarios to verify if AI-generated SQL statements can execute correctly in a real database and return expected data.
- Select regulation sections with complex tables and flowcharts. Test whether the plugin can precisely extract all associated fields and values.
Note: 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.