Data Characteristics in this Domain
Infectious disease R&D documents come from diverse sources. These include clinical trial reports, pathogen genome sequences, drug mechanism of action studies, epidemiological survey data, and related medical literature. This data updates frequently, especially with new infectious diseases or drug resistance mutations, where research progresses rapidly. Document structures vary, including PDF clinical reports, Word experimental protocols, plain text gene sequence files, and structured database exports. Beyond standard patient information, medication records, and experimental results, fields also include unique indicators like pathogen strains, infection sites, drug resistance genotypes, and antibody titers. Units involve genome base pairs, microbial counts (CFU/mL), and minimum inhibitory concentration (MIC) of drugs, requiring precise parsing.
Constraints on Deployment and Upgrade from These Characteristics
High update frequency requires FastGPT to configure efficient data synchronization and incremental parsing mechanisms during deployment. This ensures rapid integration of the latest research. Diverse document formats and structures, especially gene sequences and tabular data, demand higher robustness and accuracy from the parser. This may necessitate customized preprocessing modules. The complexity of infectious disease-specific fields and units means more refined entity recognition and relation extraction configurations are needed during model training and knowledge base construction. This prevents unit confusion or data misinterpretation. Furthermore, data volume can grow rapidly, challenging storage and computing resource scalability. The deployment solution must support elastic expansion. Naming conventions for specific pathogens or drugs also require reinforcement through dictionaries or rules to ensure accurate structured parsing.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Clinical trial reports or gene sequence files can be large. This ensures full document uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing complex PDFs or large text files takes longer. This prevents parsing timeouts. |
maxContext | 3000 Tokens | Infectious disease research has rich background information. This context length covers key details. |
Chunk size | 800–1200 characters | Balances semantic completeness and segment recall efficiency, adapting to various document structures. |
Similarity threshold | 0.75 | Domain terminology has high similarity. Raising the threshold reduces recall of irrelevant results. |
Recall count | Top 10 entries | Ensures more potentially relevant research results are covered in complex queries. |
Three Common Pitfalls
- When configuring Nginx reverse proxy to the FastGPT container on the server, a 502 Bad Gateway error often occurs. This is typically due to
proxy_passin the Nginx configuration pointing to the wrong container port or IP address. - During a FastGPT upgrade, logs showing
systemEnv.por similar parameters not found usually indicate changes in the new version'sconfig.jsonfile structure or parameter names. The configuration needs updating according to the new version's documentation. - Frequent 404 Not Found errors during Vercel automated deployment of FastGPT may relate to incorrect Vercel build scripts or environment variable configurations. This can cause some frontend resources or API paths to fail parsing correctly.
How to Verify Correct Configuration
- Upload a comprehensive PDF document containing pathogen gene sequences, clinical data tables, and drug mechanism of action descriptions. Check if it can be fully parsed and if key entities, such as pathogen names, gene loci, and MIC values, are extracted.
- Use a query statement including specific drug resistance genotypes. Verify if the knowledge base can accurately recall relevant research literature or clinical trial reports and if key fields in the recalled results are correctly structured.
- Simulate high-concurrency query scenarios. Observe FastGPT's response time and resource usage. Confirm that the system remains stable under high load and that parsing and recall efficiency meet expectations.
The values given 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.