What the data for this category looks like
Supervision documents and materials for engineering consulting supervision mainly include supervision logs, on-site inspection records, subdivision project acceptance reports, change visas, project payment certificates, and similar items. Data sources include on-site supervision ledgers, inspection materials submitted by construction parties, and instruction documents issued by the client. Update cadence: Supervision logs are updated daily, acceptance reports are generated with subdivision project milestones, and change visas are created in real time with project changes. Document structures typically include a header (project name, supervision section, date), body (reason, inspection status, handling opinions), and signature block (supervision engineer signature, date). Fields include supervision engineer certificate number, measured deviation value, number of accepted qualified items, and similar details.
What constraints these characteristics impose during deployment and upgrade
Since supervision documents include both high-frequency daily short documents and long-cycle structured reports, deployment must support switching between incremental synchronization and full updates. Since document sources include on-site scanned documents and non-standard Word documents submitted by construction parties, deployment must configure adaptation rules for multi-format parsing. Since documents contain specialized fields and units, deployment must retain field-level metadata to avoid loss of specialized semantics during retrieval. Since some documents need to be associated with project section information, deployment must configure unified project metadata association rules to prevent mixing of documents from different sections.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://nj_admin:secure_pass@192.168.1.100:27017/fastgpt_supervise | Stores metadata for supervision documents (section, signature status, associated project ID), specifies a dedicated database name to isolate business data |
PG_URL | postgresql://nj_db_user:db_pass@192.168.1.101:5432/fastgpt_supervise?schema=supervise_docs | Stores structured supervision document field data (such as measured deviation value, acceptance date), specifies a schema to distinguish business types |
REDIS_URL | redis://:redis_pass@192.168.1.102:6379/2 | Caches frequently accessed supervision logs and acceptance reports, uses database 2 as a dedicated cache store |
DB_MAX_LINK | 80–150 connections | Adapts to concurrent retrieval and incremental sync needs for supervision documents, prevents database connection exhaustion |
FE_DOMAIN | https://supervise-doc.fastgpt.internal | Configures the frontend access domain, adapts to internal private domain rules for supervision teams |
FILE_TOKEN_KEY | 32-character randomly generated string | Generates temporary access tokens for supervision document downloads, ensures access security for sensitive documents |
Three common mistakes
- Phenomenon: The frontend page fails to load normally, or displays a blank screen after loading, with
502 Bad Gatewayin the console. Cause:FE_DOMAINand backend service port mapping are not correctly configured, causing frontend requests to fail to forward to the FastGPT backend service. - Phenomenon: A
connection refusederror is returned when calling document retrieval or model generation. Cause: The address of the external model or storage service is not correctly filled in the corresponding configuration item, or the internal network access permission for the corresponding port of the service is not enabled, causing FastGPT to fail to establish a connection. - Phenomenon: The number of retrieval results after uploading supervision documents is far less than the actual number of uploaded documents, or professional field information is lost. Cause: A reasonable upper limit for
DB_MAX_LINKis not set, causing the database connection pool to be exhausted and some requests to fail to obtain document data normally, or field parsing rules are not configured, causing professional information to not be extracted.
How to confirm the configuration is correct
- Access the configured
FE_DOMAINaddress, confirm that the frontend page loads normally with no cross-domain or connection errors. - Upload a standard supervision log document, confirm that the parsed document fields (such as supervision section, record date) can be viewed normally in the backend management interface.
- Initiate a retrieval request for supervision logs, confirm that the number of returned results matches the actual number of uploaded documents, with no obvious missing items.
- Test the call of the external language model, confirm that draft content conforming to the supervision document format can be generated, with no connection timeouts or parameter errors.
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 2026-09-28.