Data Characteristics in this Domain
R&D documents in metabolism and endocrinology originate from diverse sources. These include clinical trial reports, basic research papers, patent literature, drug instructions, and internal experimental records. Update frequencies vary; clinical trial data might update quarterly, while basic research findings align with academic publication cycles. Document structures typically include standard sections like abstract, introduction, methods, results, discussion, and conclusion. Internal experimental records, however, may be less structured. Specific fields include drug concentration (nM, µM), dosage (mg/kg), biomarker levels (nmol/L, pg/mL), treatment duration (weeks, months), and patient baseline characteristics (BMI, HbA1c). Units are strict and varied. Data often contains extensive charts, chemical structures, and protein sequences, posing challenges for pure text parsing.
Constraints Imposed by These Characteristics on Deployment and Upgrade
The complexity of metabolism and endocrinology documents places specific demands on FastGPT's deployment and upgrade process. Varying document update frequencies require the knowledge base to support incremental updates and version management, preventing duplicate imports. Diverse structures and unique fields necessitate customized text preprocessing and entity extraction rules to ensure critical information is not missed. For example, numerical fields with units, such as drug concentration, must be correctly identified and quantified. Embedding non-textual information, like chart content, requires integrating additional image recognition or OCR components. This increases deployment environment complexity and may require GPU support. Deployment must consider data processing capabilities under high concurrency, as queries for specific metabolic diseases might involve recalling large amounts of data for similar drugs or targets. During upgrades, parsing models need flexible adjustment for new document types or biomarker units, while ensuring compatibility with existing data.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Clinical trial reports or large research papers often contain extensive data and charts, leading to large file sizes. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Metabolism and endocrinology documents have high paragraph density, containing many specialized terms and contextual associations. This range ensures complete semantic capture per segment. |
Recall count (Recall Count) | Top 8 entries (top 8 entries) | Queries for specific diseases or drugs require a broader initial recall range to cover potential associations. |
Similarity threshold (Similarity Threshold) | 0.75 | Domain terminology has high similarity. A relatively high threshold ensures the precision of recall results, avoiding generalized information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Parsing complex PDFs with intricate tables and multi-level structures can be time-consuming. |
maxContext | 3000 Tokens | Understanding metabolic pathways or drug mechanisms requires a longer context window for comprehensive analysis. |
Three Common Mistakes
- Knowledge base query results are empty. This may occur if key fields like drug dosage or biomarkers are not correctly identified during document parsing, leading to incomplete indexing.
- The system frequently experiences timeout errors when processing large documents. This typically happens when the
PARSE_FILE_TIMEOUT_SECONDSparameter is set too low, failing to account for the parsing time of complex PDFs or multimedia documents. - When switching applications, mandatory global variables defined in the old application (e.g.,
disease_area) remain active for the new application and cause errors. This occurs because the variable state is not cleared or reset when switching applications.
How to Verify Configuration
- Upload a typical clinical trial report from the metabolism and endocrinology domain. Verify that unique fields like drug concentration and dosage are correctly extracted and displayed in the knowledge base content preview.
- Execute queries for specific biomarkers or targets. Cross-reference the recall results to ensure they include multiple highly relevant pieces of information from different documents, and check their precision.
- Simulate a high-concurrency scenario by having multiple users query simultaneously. Monitor system logs to observe file parsing and knowledge base retrieval service response times, ensuring service stability meets expectations.
- After upgrading FastGPT's core service or knowledge base model, verify that existing document parsing and query functions operate normally, without data loss or parsing errors. Also, check that new features work as expected.
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.