Data Characteristics
Market access quality documents primarily source data from regulations, guidelines, and technical requirements issued by national or regional drug regulatory agencies. Internal corporate registration applications, clinical trial reports, and manufacturing process documents also contribute. Update cycles typically follow regulatory release schedules, occurring quarterly, semi-annually, or annually. Major policy changes trigger more frequent updates. Document structures are complex, containing specialized terminology, abbreviations, and specific formatting requirements, such as CTD (Common Technical Document) format. Fields and units are highly specialized. For example, drug active ingredient content is often expressed in mg or % (w/w), impurity limits in ppm or ppb, and stability data includes parameters like temperature, humidity, and time with their corresponding units.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The complex and specialized nature of market access documents requires tool calls to accurately parse deeply nested section information and identify specific regulatory clauses. Periodic regulatory updates necessitate regular incremental or full knowledge base updates. Tool calling plugins must support version management and difference comparison. The precision of specialized fields and units demands high accuracy in entity recognition and numerical extraction to prevent unit confusion. For instance, confusing mg with g when extracting drug dosages leads to serious errors. Multi-language documents (e.g., for EU, US, Japan markets) also require plugins with multi-language processing capabilities or pre-processing via external translation tools. For documents containing charts and attachments, tool calls must trigger corresponding image recognition or file parsing plugins.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4000 tokens | Market access document paragraphs are long; sufficient context is needed for understanding. |
Chunk size (Chunk Length) | 800–1200 characters (characters) | Balances semantic completeness and retrieval efficiency, avoiding splitting critical regulatory clauses. |
Recall count (Recall Count) | Top 5–8 entries (top 5–8 entries) | Ensures coverage of relevant regulatory clauses while controlling model input length. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | A high threshold improves matching accuracy for documents with many specialized terms. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Processing large PDF or DOCX documents can take a long time. |
toolCallMaxRetry | 3 times (times) | Addresses occasional network fluctuations or temporary unavailability of external API services. |
Common Pitfalls
- Symptom: The model provides a general answer instead of triggering a tool when an external database query for regulation versions is needed. Reason: The tool's
descriptionis unclear. It does not explicitly state that the tool applies to querying keywords like "regulation version" or "update date," preventing the model from accurately matching the call intent. - Symptom: The
Referencefield is empty or incomplete in the content recalled from the knowledge base. Reason: Document metadata, such as regulation numbers or publication dates, was not correctly configured or extracted during document import and thus not indexed. - Symptom: Calling an external SQL database plugin returns
500 Internal Server Error. Reason: The database connection string is configured incorrectly, or the plugin's database driver does not support the target database type (e.g., attempting to connect to SQL Server when the plugin only supports PostgreSQL).
Confirmation of Correct Configuration
- For typical market access questions, such as "query the latest registration requirements for a certain drug in the EU," observe whether the model successfully calls the regulation query tool and returns information with specific regulation numbers and versions.
- Test multiple queries containing specialized terms and units, for example, "What is the impurity limit for XX drug?" Check if the numerical values and units returned after the tool call are precise.
- Simulate a regulatory update scenario: After uploading a new version of a regulation document, query questions related to the old regulation. Confirm that the model correctly recalls and cites the latest version of the content or indicates that the regulation has been updated.
The values provided are common starting points. Measure them 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.