What this category’s data looks like
The data for publishing intelligent due diligence reports originates from internal topic management systems of publishing institutions, deliverables from copyright agencies, and publicly available publication supervision databases. Updates are triggered in line with topic advancement milestones, with no fixed single update cycle. Documents are split into structured metadata blocks and long-text analysis chapters. Fields include topic ID, ISBN number, copyright term, author qualifications, and market performance comparison items. Units include copies and yuan.
What constraints these characteristics impose on deployment and upgrade
Structured metadata makes up a large share of the data and has fixed field formats. Deployed models must support multi-field vector recall to avoid metadata loss caused by configurations that only support plain text parsing. Update cycles are irregular and data increment volumes vary widely. Upgrades must support incremental vector synchronization to avoid full reconstruction and reduce resource consumption. Long-text analysis chapters have significant length, so sufficient context processing parameters must be configured to prevent content truncation. Publishing data has compliance storage requirements, so a data persistence directory that meets regulatory standards must be specified during deployment. Upgrades must retain existing data volumes to prevent data loss.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Publishing due diligence reports include long-text analysis chapters, which take a long time to parse. This value covers the complete parsing process |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Due diligence reports may include multiple copyright documents and market research reports, resulting in large single-package upload volume |
maxContext | 8192–16384 characters | Adapts to the content length of long-text analysis chapters to avoid content truncation during parsing |
embeddingModel | Qwen3-Embedding-8B | This model supports vector recall for multi-field structured data, adapting to the metadata characteristics of publishing due diligence reports |
VLLM_WORKER_NUM | 2–4 workers | Balances the vector recall concurrency requirements of publishing data and server resource usage |
DB_INCREMENT_SYNC_ENABLE | Enabled | Adapts to the irregular update cycle of publishing data, reducing resource consumption during daily operation and upgrades |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Symptom: Embedding model connections return
500 Internal Server Error, with logs showing model loading failure. Cause: The API address and port of the Qwen3-Embedding-8B model deployed via VLLM were not correctly filled in the FastGPT configuration, or network communication permissions between containers were not enabled. - Symptom: The PostgreSQL container continuously reports
FATAL: role "postgres" does not existafter startup. Cause: The database initialization script was not executed during deployment, or the initialization configuration for the default role was accidentally overwritten during an upgrade. - Symptom: Uploaded audio files return empty parsing results with no text output. Cause: The audio parsing module was not enabled in the deployment configuration, or the corresponding speech-to-text large model API was not bound.
How to verify successful configuration
- Run the embedding model test interface, confirm that the returned vector dimensions match the configured embedding model parameters.
- Upload a small sample publishing due diligence report, confirm that parsed metadata fields are complete with no content truncation.
- Run a database incremental synchronization test, confirm that new data can be automatically synchronized to the vector database with no data loss.
- Restart the FastGPT service, confirm that there are no database connection or model loading errors in the container logs.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.