Data Characteristics
Laboratory service quality documents in the biopharmaceutical field primarily source data from experimental protocols, instrument calibration records, reagent batch information, Standard Operating Procedures (SOPs), and experimental reports. Updates typically align with batch experiments or audit cycles. For example, reports update after each batch experiment, or SOPs revise quarterly/annually. Document structures are usually standardized text reports, PDFs, or Word formats, containing extensive structured and semi-structured data. Common fields include batch number, test item, test method, instrument number, calibration date, expiration date, result value, unit (e.g., ng/mL, IU/L, pH value), and signature date. This data is often dispersed across different documents but maintains clear logical connections.
Constraints from Data Characteristics on Multi-Turn Conversations and Prompts
The data characteristics of laboratory service quality documents impose specific requirements on multi-turn conversation and prompt design. First, the standardized document structure and field definitions limit the precision of information extraction. Prompts must accurately locate specific values and units, distinguishing between test value and reference range. Second, the periodic nature of data updates means the knowledge base content needs regular synchronization to ensure timely conversation results. In multi-turn conversations, users might trace a specific test item from one batch to its associated reagent batch or instrument calibration record. This requires the model to perform cross-document linking and maintain context. Additionally, common technical terms and abbreviations (e.g., HPLC, GC-MS) in documents require prompts to parse them effectively, avoiding semantic confusion. For queries involving numerical results, the model must understand the intent behind unit conversions or range comparisons, such as querying samples with pH > 7.0.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 2000 characters | Ensures sufficient contextual information is retained in multi-turn conversations to handle cross-document queries. |
Recall Count | Top 5 | Given the high density of document content, the top few results usually contain the most relevant information, reducing unnecessary recall. |
Similarity Threshold | 0.78 | Quality documents demand high precision. A high threshold helps filter out irrelevant snippets and focuses on core information. |
Rerank Return Count | 3 | Based on high-similarity recall, further refines the most relevant snippets to improve answer accuracy. |
Chunk Length | 400–600 characters | Laboratory documents often contain long descriptive paragraphs. An appropriate chunk length helps maintain semantic integrity. |
UPLOAD_FILE_MAX_SIZE | 50 MB | Considering experimental reports and SOP documents may include charts, file sizes can be large. |
Common Pitfalls
- Query results do not include specific values or units: This occurs when prompt rules for extracting numbers and units are not precise enough, preventing the model from correctly identifying numerical fields in the document.
- Context loss in multi-turn conversations, making it impossible to trace previous queries: This happens when the
maxContextparameter is set too low to accommodate the conversation history of complex linked queries. - API calls for multimodal conversations return a
400 invalid imageerror: This indicates an incompatible image format or corrupted file, preventing the model from correctly parsing the image content.
How to Verify Configuration
- Upload a typical experimental report and query for specific batch test results through conversation. Check if the returned values and units are accurate.
- Conduct a simulated multi-turn conversation, tracing from an experimental batch to its associated instrument calibration records. Observe if the model maintains context and provides relevant information.
- Upload an SOP document containing charts. Attempt to ask about key process steps within the charts. Confirm if the model can parse image content and generate relevant answers.
- Upload documents in various formats (e.g., PDF, DOCX) via the API interface. Check that file uploads and parsing proceed without errors and that conversations can be conducted normally.
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.