Data Characteristics for This Category
Academic promotion data sources primarily include regulatory documents, Standard Operating Procedures (SOPs), training materials, and Frequently Asked Questions (FAQs) issued by a pharmaceutical company's medical affairs or compliance departments. These documents typically exist as PDFs, Word files, or internal knowledge base pages. Update frequency for regulatory documents and SOPs varies from quarterly to annually, depending on regulatory changes, company policy adjustments, or periodic reviews. However, some highly dynamic detailed guidelines may update monthly. Document structures often feature clear chapter headings, clause numbers, definitions, scope, operational procedures, and approval processes. Fields and units involve drug names, indications, medical terminology, approval levels, timeframes (e.g., "reply within 24 hours"), monetary limits (e.g., "not exceeding 500 RMB"), and specific compliance requirements (e.g., "must be reviewed by the medical department").
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The strict document structure of academic promotion regulations demands high precision in segmentation and semantic parsing. This ensures tools accurately identify critical clauses and limiting conditions during calls. The uncertain update frequency requires the knowledge base to support efficient incremental updates and version management, preventing compliance risks from tools using outdated regulations. Documents contain extensive medical terminology and specialized processes. This necessitates a higher level of specialized knowledge for the large language model to understand query intent and select tools, reducing misjudgment probability. Monetary and time-based numerical restrictions common in regulations require tools to precisely extract and logically evaluate these values during calls, for instance, determining if an activity exceeds budget or time limits. If documents are accessed via API and content cannot be read, issues may stem from permissions, format, or encoding. Ensure the API returns parseable text or structured data.
Configuration Recommendations
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8000 tokens | Regulatory documents are often long, requiring a larger context window to understand complete clauses and logical relationships. |
Chunk size (Segment Length) | 800–1200 characters | Balances the completeness of regulatory clauses with retrieval efficiency, avoiding excessive fragmentation or overly long irrelevant content. |
Recall count (Retrieval Count) | top 8 entries | Ensures coverage of multiple relevant regulatory clauses, improving the comprehensiveness and accuracy of answers. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Regulatory Q&A demands high accuracy, requiring a balance between retrieval relevance and quantity. |
Rerank result count (Reranked Return Count) | top 3 entries | After reranking, a few most relevant entries are sufficient to support the answer, reducing the model's processing burden. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF or Word documents can be time-consuming; sufficient time is allocated to prevent parsing interruptions. |
Three Common Pitfalls
- Concurrent calls or unexpected results during tool selection: This occurs when tool definitions are too broad, or the model's understanding of query intent is imprecise, leading to multiple tools activating simultaneously or an inappropriate tool being chosen.
- Chatbot responses via API calls do not include knowledge base information: This usually happens when the knowledge base ID or relevant parameters are not correctly passed during the API call, or the knowledge base index is not successfully loaded into the application.
- AI answers questions after reading a document, but appears not to have read the content: This could be due to unsupported document formats, improper parser configuration preventing correct content extraction and embedding, or a
connection errorduring API calls preventing data transmission.
How to Confirm Proper Configuration
- Test core regulatory clauses with various phrasings. Verify the model consistently calls the correct tools and references knowledge base content.
- Simulate a regulatory update scenario. Upload a new version of a document and test if tool calls prioritize the latest information for answers. Compare the differences in responses.
- Check system logs. Confirm no
connection errorortimeoutmessages appear during tool calls, and API calls return a 200 status code.
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.