What the data for this category looks like
Engineering consulting supervision data comes from project site supervision logs, progress control documents, contract attachments, acceptance reports, rectification notices, and similar sources. Updates follow project progress, with submissions made daily or weekly. Document structures include project number, supervision section, problem description, rectification deadline, acceptance result, responsible unit, and other fields. Units include workdays, square meters, ten thousand yuan, and similar units. Primary document formats are PDF, Word, and Excel, with some structured Excel ledger data.
What constraints these characteristics impose on deployment and upgrade
Supervision project data mixes structured ledgers and unstructured documents. During deployment, configure multi-format parsing and storage and retrieval for mixed data. High data update frequency requires caching mechanisms to reduce database query pressure. Document fields carry project-specific identifiers, so retain field mapping rules during deployment. Some project data involves internal permission isolation. During upgrade, maintain compatibility with original data formats to avoid data parsing exceptions. Private deployment storage is required for confidential project documents to ensure data security.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:password@host1:27017,host2:27017/fastgpt?authSource=admin&replicaSet=rs0 | Large volume of supervision project data and high concurrent access; sharded cluster configuration ensures stable connections and supports parallel processing of multiple projects |
REDIS_URL | redis://:redis_pass@redis-host:6379/0?maxmemory=2048 MB&maxmemory-policy=allkeys-lru | Cache supervision project progress data and document indexes to reduce database query frequency, suitable for high-frequency update business scenarios |
STORAGE_S3_ENDPOINT | https://s3-cn-beijing.volces.com | Adapt to domestic compliant cloud storage nodes, improve upload and download speed of supervision documents, ensure storage stability for internal office scenarios |
DB_MAX_LINK | 600 | Match the concurrent connection requirements of parallel processing of multiple projects by the supervision team, avoid connection pool exhaustion |
FILE_TOKEN_KEY | Random 32-bit string | Protect upload and download tokens for supervision documents, prevent unauthorized access to confidential project documents |
FE_DOMAIN | https://fastgpt.internal | Configure the access domain name of the internal office system, match the access path of enterprise intranet deployment |
Three Common Mistakes
- Phenomenon: After Docker deployment, when attempting to add a domestic cloud service provider's DeepSeep-R1 model, the interface prompts model access failure with a 401 unauthorized error. Cause: The API key of the domestic cloud service provider was not configured in the system environment variables, and the correct model access address was not filled in the model access configuration.
- Phenomenon: After local deployment update, upload a PDF document of a supervision project to the knowledge base, the model cannot generate a response, and the logs show that the parsed content of the file is empty. Cause: After the update, the dependency package version of the file parsing module is incompatible with the original configuration, or the
FILE_TOKEN_KEYconfiguration is not correctly matched. - Phenomenon: When creating a new knowledge base in a locally deployed 4.9.0 version, there is no image indexing option. Cause: This function can only be enabled after upgrading to FastGPT v4.17.0 in the open source version, and the module is not integrated by default in earlier open source versions.
How to Confirm the Configuration is Complete
- Run a database connection test, check the system logs, confirm that the sharded cluster connection configured by
MONGODB_URIis normal. - Upload a supervision document containing project numbers and rectification items, check that the corresponding fields can be extracted after the knowledge base parses the document, confirm that the data mapping is correct.
- Access the configured
REDIS_URL, check the cache monitoring panel, confirm that the cache validity period of project progress data meets business requirements. - Test calling the model to generate responses related to supervision projects, confirm that the model can normally receive the uploaded document content and generate results.
- Check the
FILE_TOKEN_KEYconfiguration, attempt to upload and download documents, confirm that token verification is normal, and there are no unauthorized access errors.
The values provided are common starting points and should be evaluated against local 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.