Data Characteristics for this Product Category
Stability study data primarily originates from various experimental reports, analysis certificates, product technical specifications, and quality standard documents. Document update frequency typically correlates with product lifecycles and regulatory requirements, such as annual reviews, batch releases, or significant changes. Document structures are often highly standardized, including clear fields like batch number, production date, expiration date, storage conditions, test items, test methods, test results, and judgment criteria. Key fields, such as "degradation product content," are usually expressed as a percentage. "Activity" is expressed in units per milligram or international units, and "storage temperature" is in Celsius or Kelvin.
Constraints Imposed by these Characteristics on Knowledge Base Retrieval and Recall
The standardized structure and specific fields of stability study data introduce unique constraints for knowledge base retrieval and recall. High-frequency updates require the knowledge base to have an efficient incremental update mechanism to ensure information timeliness. Documents contain numerous tables and structured data, posing challenges for chunking strategies. Critical related information must not be split. Specific fields like batch number, expiration date, and test results often serve as conditions for exact matches or range queries during retrieval; standard semantic retrieval might not handle these effectively. Furthermore, queries on numerical data (e.g., degradation product content, activity values) require the system to support numerical range filtering and unit conversion to ensure the accuracy and relevance of recall results.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 500–800 characters (characters) | Balances the completeness of information in a single chunk with retrieval granularity, preventing critical information from being truncated. |
Chunk Overlap Length (Chunk Overlap Length) | 100 characters (characters) | Ensures contextual continuity and reduces information loss due to chunking. |
Recall count (Recall Count) | 8–12 entries (items) | Ensures comprehensive recall while minimizing unnecessary computational overhead. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Balances recall precision and recall rate, reducing noise interference. |
Rerank result count (Reranked Return Count) | 3–5 entries (items) | Focuses on the most relevant results, improving the quality of the final presentation. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Accommodates parsing time for large experimental reports or documents containing complex tables. |
Three Common Pitfalls
- Knowledge base file upload fails, with logs showing
HTTP 504 Gateway Timeout. This occurs because stability study reports often have large document sizes, and file parsing or transfer time exceeds the default timeout settings of the gateway or server. - Retrieval results contain a large amount of irrelevant or outdated product information. This may happen if the knowledge base update mechanism does not effectively handle document version iterations or expired batch data, leading to a mix of old and new data.
- The conversational agent cannot accurately answer questions about activity values or degradation rates for specific batch products. This indicates that the knowledge base, during vectorization, failed to effectively identify and distinguish numerical fields and their associated batch information within documents, preventing precise matching or numerical range filtering during retrieval.
How to Verify Correct Configuration
- Upload a batch of typical stability study report documents. Check the knowledge base file upload status to ensure no timeout errors and that document content is parsed correctly.
- Query the expiration date or storage conditions for a specific product batch via API or interface. Verify that the returned results match the original document, ensuring data timeliness and accuracy.
- For queries involving numerical ranges, such as "batches with degradation product content less than 0.5%," verify that the recall results correctly filter and present relevant document snippets.
- Delete a specific Q&A pair or document from the knowledge base, then query again to confirm that the old information is no longer recalled.
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.