Data Characteristics in this Category
Data generated during laboratory service registration and declaration document preparation primarily comes from various experiment reports, test data, analysis charts, method validation documents, and quality control records. This data exists in both structured (e.g., LIMS system exports, Excel format test results) and unstructured forms (e.g., PDF experiment reports, image format charts). Data updates frequently; experimental data continuously generates and revises, especially during a project. Document structures are complex, containing numerous technical terms, abbreviations, and specific formatting requirements. Examples include experimental method descriptions, instrument parameters, sample batch information, and units (e.g., ng/mL, %, kPa). Field names often vary across laboratories or test items, requiring standardization.
Constraints Imposed by these Characteristics on "Deployment and Upgrade"
The multi-source nature and high update frequency of laboratory service data require the deployed FastGPT system to have efficient data ingestion and incremental update capabilities. This avoids redundant processing and data lag. The complexity and specialized nature of document structures mean that the RAG (Retrieval-Augmented Generation) system needs more refined text segmentation strategies and entity recognition capabilities. This ensures critical information is not fragmented or overlooked. The presence of many unstructured documents demands high performance from file parsing and OCR (Optical Character Recognition) functions. The specificity of fields and units requires the system to accurately identify and normalize this information during knowledge base construction. This improves retrieval accuracy and answer generation. During system upgrades, ensure compatibility with existing knowledge bases and verify the new version's ability to handle complex data formats.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Experiment reports and chart files are generally large, requiring support for large file uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing complex PDF reports and image OCR requires significant time; this prevents parsing timeouts. |
Chunk size | 800 characters | Ensures semantically complete paragraphs, such as experimental methods and results, are not excessively split, maintaining context. |
Recall count | Top 8 entries | Increases the probability of recalling critical information from a large volume of relevant experimental data. |
Similarity threshold | 0.75 | Balances recall precision and generalization ability, ensuring retrieved results are highly relevant to the query. |
Vector Model | text-embedding-ada-002 | Suitable for semantic understanding of specialized texts in the biomedical field. |
Common Pitfalls
- The system reports out-of-memory errors or database connection errors after running for some time. This occurs because
PG_MAX_CONNECTIONSorCONTAINER_MEMORY_LIMITare set too low, without fully considering laboratory data volume and query concurrency. - Retrieval quality for some historical documents degrades after an upgrade. This happens when the new version's tokenizer or vector model changes, and the existing knowledge base is not re-embedded or compatibility is not verified.
- Uploading large PDF experiment reports results in parsing progress stalling or an
ERR_PARSE_FILE_FAILEDerror. This is due toPARSE_FILE_TIMEOUT_SECONDSbeing too small, preventing the completion of OCR and text extraction for complex documents.
Verification Steps
- Upload typical experiment reports in various formats (PDF, Excel, images). Verify file parsing and knowledge base construction success. Check the completeness of content in the knowledge base.
- For specific experimental projects, use query statements containing specialized terms. Verify the system's ability to accurately recall relevant experimental data and methods. Compare the number of recalled items against the expected threshold.
- During peak hours, simulate concurrent access and queries from multiple users. Monitor system resource usage (e.g.,
CPU,Memory) to ensure it remains within reasonable limits. Check if response times meet business requirements.
The values provided are common starting points. Measure against your 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.