Data Characteristics in This Category
Molecular diagnostics product data comes from various sources. These include official product manuals, clinical validation reports, technical documentation, user manuals, and journal articles. Document updates are relatively stable, typically occurring during initial product launches or significant version iterations, averaging 1-2 times per year. Document structures are usually highly standardized. They contain sections such as product overview, detection principles, reagent components, operating procedures, performance indicators (e.g., sensitivity, specificity), applicable sample types, storage conditions, result interpretation, and precautions. Fields often involve gene loci, nucleic acid sequences, detection targets, cycle threshold (Ct values), concentration units (e.g., nM, μg/mL), reaction temperatures (e.g., 95℃), and time (e.g., min, s). File formats are predominantly PDF and structured XML or JSON.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The standardization and stability of molecular diagnostics data demand high precision in the data extraction and processing stages of a workflow. For example, accurately extracting the storage temperature (2-8℃) and expiration date for a specific reagent from a PDF product manual requires robust document parsing capabilities. The low frequency of document updates means less pressure on knowledge base synchronization. However, each update requires accurate identification and integration of incremental information into the existing knowledge system. Specialized fields, such as gene loci or Ct values, require the AI model to understand professional vocabulary to avoid ambiguity when users inquire about "a certain gene mutation." Additionally, the numerical nature of performance indicators (e.g., sensitivity 99.5%) makes numerical comparisons and logical judgments common within workflows. This necessitates workflow orchestration tools that support flexible conditional branching and data validation nodes.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
Segment Length | 500-800 characters | Molecular diagnostics document paragraphs have strong logical integrity. This length helps retain context and reduces information fragmentation. |
Recall Count | Top 8-12 | Ensures coverage of more relevant technical details in complex queries, improving accuracy. |
Similarity Threshold | 0.75-0.85 | Ensures precision of recalled content, filtering out irrelevant information, especially for technical detail inquiries. |
maxContext | 6000-8000 Tokens | Addresses complex scenarios in user queries that may include multiple product parameters and experimental steps. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Accounts for the parsing time of large technical documents and reports (e.g., clinical validation reports). |
Rerank Return Count | Top 5 | After reranking, the top few most relevant pieces of information are sufficient for most product inquiry needs. |
Three Common Pitfalls
- The AI model's response contains incorrect threshold judgments for specific detection indicators (e.g., Ct value
>35). This occurs because numerical comparison nodes are not configured correctly in the workflow, or the model misunderstands professional terminology. - When a user queries "storage conditions for a certain reagent kit," the system returns a null value. This can happen if the document parser fails to accurately extract the field from the
PDF, or if the corresponding field is missing during knowledge base indexing. - When using an external database connection plugin to query product batch information, the workflow validation fails with a message like "missing, null value, is connection normal?" This usually indicates incorrect database connection parameters (e.g.,
host,port,dbname) or a syntax error in the SQL query.
How to Verify Configuration
- For core product manuals, design a set of test questions that include professional terminology, numerical queries, and multi-conditional judgments. Verify the accuracy and completeness of the AI's responses.
- Randomly select 10 documents of different types (manuals, reports). Check their indexing status in the knowledge base to ensure all key fields (e.g.,
Product Name,Batch Number,Expiration Date) are correctly identified and stored. - Use the workflow's logging feature to track the execution status of key nodes (e.g., document parsing, external tool calls, model inference). Confirm no abnormal errors occur and data flow aligns with expectations.
- Simulate user inquiries. Test the system's response to complex questions like product batch queries and clinical data interpretation. Evaluate the success and accuracy of external tool calls (e.g., database query plugins).
The values provided are common starting points. They 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.