Data Characteristics
Stability study regulation data sources typically include internal quality management system documents, Standard Operating Procedures (SOPs), guidelines, registration and declaration materials, and relevant regulatory documents. These documents exist as PDFs, Word files, or internal knowledge base pages. Update frequency is low, usually annually or in response to regulatory changes. Document structure is rigorous, containing numerous tables, charts, and cross-references. Fields include batch number, production date, expiration date, storage conditions, test items, test methods, and result judgment criteria. Units involve temperature (℃), humidity (%RH), time (months/years), and concentration (mg/mL). Data accuracy requirements are high.
Constraints from "Workflow Orchestration"
The low update frequency of stability study regulation documents means knowledge base indexing does not require frequent updates. Periodic full updates are feasible, reducing the complexity of incremental updates. The rigorous document structure and numerous tables require robust table recognition and structured extraction capabilities during the document parsing stage. This ensures critical fields like storage conditions and judgment criteria are accurately identified. High accuracy requirements may necessitate additional post-processing steps after information retrieval to verify the correctness of numerical values and units, preventing misinterpretation. Furthermore, cross-references between documents require retrieval nodes in the workflow to effectively handle multi-document associated queries, providing complete context.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Policy documents often have long paragraphs and strong contextual relevance. This prevents context loss after splitting. |
Recall count (Recall Count) | Top 5 | Ensures coverage of multiple relevant policy points while controlling result redundancy. |
Similarity threshold (Similarity Threshold) | 0.75 | Policy Q&A demands high accuracy. A lower threshold may introduce irrelevant content. |
Rerank result count (Rerank Return Count) | 3 | Reranking further improves the sorting of the most relevant information, reducing the model's processing burden. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Stability study documents can contain many pages and complex tables, requiring longer parsing times. |
HTTP_REQUEST_TIMEOUT | 30 seconds | Ensures timely responses when calling external systems for data validation or associated queries. |
Common Pitfalls
- Symptom: Model responses contain incorrect key numerical values or units, such as inconsistent storage temperature or humidity. Reason: Document parsing failed to correctly identify numerical values and units in tables, or deviations occurred during information extraction.
- Symptom: When user questions involve multiple policy intersection points, the model fails to provide comprehensive answers. Reason: The retrieval node in the workflow did not effectively handle cross-references between documents, leading to the recall of only partial relevant information.
- Symptom: After workflow configuration, calling it from the business system results in a
401 Unauthorizederror. Reason: The business system user identity and FastGPT platform association configuration is incorrect, or the API key or authentication token is invalid.
Verification Steps
- Conduct multi-turn dialogue tests using typical stability study regulation questions. Verify whether key information in model responses (e.g., storage conditions, testing cycles, judgment criteria) aligns with original documents.
- Check workflow logs to confirm that the document parsing node successfully processed all uploaded policy files. Look for error messages like
PARSE_FILE_ERRORorTABLE_EXTRACT_FAIL. - Test with complex questions involving cross-references. Confirm that the retrieval node in the workflow accurately recalls all relevant document segments.
- Simulate business system calls to verify that authentication and execution processes in the external environment are smooth. Check HTTP request return status codes.
The values provided are common starting points. Measure performance 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.