Data Characteristics
Stability study data primarily originates from long-term retention sample observations, accelerated tests, and forced degradation studies. Data updates typically occur monthly, quarterly, or annually, as specified by drug registration approvals or research protocols. Document structures commonly include research protocols, raw records, analysis reports, and trend charts, often in PDF or scanned formats. Fields include batch number, manufacturing date, expiration date, test item, test result, storage conditions, and sampling time point. Test result units vary, such as content percentage (%), solubility (mg/mL), pH value, and microbial limits (CFU/g), and are often accompanied by upper and lower limits.
Constraints on Multi-Turn Conversations and Prompts
The multi-source nature and infrequent updates of stability study data necessitate robust document version management and data timeliness during knowledge base construction. In multi-turn conversations, users may need to trace long-term stability trends for specific batches, requiring the system to accurately link test reports from different time points. Complex table and chart structures within documents challenge information extraction capabilities; parsing tools must accurately identify fields and units. The extensive use of specialized terminology and abbreviations, along with inquiries into reasons for result fluctuations, demands prompt designs that embed domain knowledge to avoid semantic ambiguity. For anomalous data exceeding standard limits, the conversational system needs to guide users toward further investigation or risk assessment.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500 characters (500 characters) | Stability reports contain extensive continuous experimental descriptions and results. Segments that are too short may break critical information, while segments that are too long increase irrelevant noise. |
Recall count (Recall Count) | Top 8 entries (Top 8 items) | Stability studies often involve multiple time points and test items, requiring more context to support complex queries, such as comparisons under different storage conditions. |
Similarity threshold (Similarity Threshold) | 0.78 | Specialized terminology and numerical differences in stability data can lead to lower similarity scores. Slightly relaxing the threshold helps recall more relevant documents. |
maxContext | 4096 tokens | Ensures the ability to carry longer historical conversation records and recalled document content in multi-turn conversations to understand user intent and trace context. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (600 seconds) | Stability reports, especially annual summaries, can be large documents, requiring more time for parsing. |
Rerank result count (Reranked Return Count) | Top 3 entries (Top 3 items) | Multiple documents may be recalled. Reranking places the most relevant core reports or data points first, improving user efficiency in obtaining information. |
Common Pitfalls
- Missing test data for a specific batch in the conversation's returned results. This can occur if document parsing fails to correctly identify the correspondence between batch and data in tables, or if the knowledge base index does not include all batch data.
- After clicking a link provided in the conversation, the page fails to navigate to the correct internal document location. This is typically due to issues in link handling logic during API streaming output, or the frontend failing to correctly parse the returned URL format.
- The model cannot understand deep user questions regarding the "correlation between accelerated testing and long-term stability." This happens when prompts do not sufficiently guide the model to use domain knowledge for reasoning, remaining at an information retrieval level.
Verification Steps
- Conduct multi-turn conversation tests for typical stability study questions, confirming the system accurately understands intent and provides relevant document snippets.
- Cross-reference returned test results, ensuring numerical values, units, and batch information precisely match original documents, especially for critical values or anomalous data.
- Verify the system's ability to correctly associate and extract information when querying stability data under different storage conditions and at different time points.
- Check the system's capability to identify and reference data from charts or complex tables within documents, confirming it can provide key conclusions or data points.
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.