Data Characteristics for This Category
Laboratory service data for biomedical registration and declaration document preparation primarily originates from various experimental reports, analysis certificates, methodology validation documents, equipment calibration records, and quality management system files. This data typically exists as PDFs, Word documents, Excel spreadsheets, or scanned images. The update frequency aligns with experimental batches and project progress, with reports potentially updating weekly or monthly. Document structures are complex, containing extensive specialized terminology, charts, structural formulas, and experimental procedure descriptions. Fields and units are highly specific. Examples include compound purity percentages, spectroscopic data (wavelength nm, absorbance AU), chromatographic data (retention time min, peak area mV*s), cell viability percentages, and gene expression fold changes.
Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts
The complexity and diversity of laboratory service data demand high accuracy and robustness from multiturn conversations and prompts. Charts and scanned images within documents can lead to incomplete or inaccurate text extraction, affecting subsequent semantic understanding. High-frequency updates necessitate an efficient incremental update mechanism for the knowledge base to ensure the timeliness of conversation content. The prevalence of specialized terminology and abbreviations requires the model to possess strong domain knowledge understanding capabilities to prevent user confusion or misguidance due to ambiguous terms. Furthermore, the precise numerical values and units in experimental data require the conversation system to accurately reproduce them when cited, as any deviation in numbers or units can lead to serious compliance issues.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Segment Length | 800–1200 characters | Experimental reports and methodology files often contain long logical paragraphs; this ensures contextual completeness. |
Recall Count | Top 8–12 | Ensures coverage of key information from multiple relevant experimental reports to handle cross-references in multiturn conversations. |
Similarity Threshold | 0.75–0.85 | Balances recall precision and generalization ability, avoids irrelevant information interference, and matches semantic similarity of specialized terms. |
Rerank Return Count | Top 5 | Further improves the ranking of the most relevant information after initial recall, enhancing the quality of the first response. |
maxContext | 4000 tokens | Given the dense content of experimental data, a larger context window is needed to handle complex queries and multiturn follow-ups. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large experimental reports and multiple attachments can be time-consuming; this prevents file processing failures due to timeouts. |
Three Common Pitfalls
- Symptom: During a conversation, the Agent cannot correctly cite or understand specific numerical values from experimental reports (e.g., purity 99.5%), instead providing vague descriptions. Reason: During text extraction, the association between numerical values and units is broken, or the knowledge base segmentation strategy splits critical numerical information.
- Symptom: After a user uploads multiple files, the Agent only references one file in its response or indicates a file processing failure. Reason: Incorrect parameter passing in the file upload interface prevents the system from recognizing all files, or the file size exceeds the
UPLOAD_FILE_MAX_SIZElimit. - Symptom: After multiturn conversations, the Agent begins to "hallucinate," generating experimental conclusions or recommendations inconsistent with the knowledge base content. Reason: Insufficient
maxContextsettings lead to truncation of critical information from earlier conversation turns, preventing the model from maintaining long-term memory.
How to Verify Correct Configuration
- Upload typical experimental reports, analysis certificates, methodology documents, and other file formats. Check if the knowledge base indexing is complete, especially whether text near tables and charts is correctly extracted.
- Ask multiturn questions targeting specific specialized terms, abbreviations, and numerical values within the documents. Observe if the Agent can accurately understand and recall relevant passages and data from the knowledge base.
- Simulate complex queries from actual declaration processes, such as asking about differences between data from different experimental batches or requesting the Agent to summarize specific experimental steps. Evaluate the accuracy and coherence of the responses.
- Regularly add the latest batches of experimental reports to the knowledge base. Verify that the Agent's query responses to new data are timely and effective to ensure the incremental update mechanism functions correctly.
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.