Deployment and Upgrade for Engineering Consulting Financial

What the data for this category looks like

Financial report data for engineering consulting enterprises comes from structured reports exported from internal project management systems, annual settlement audit reports, material purchase ledgers, and industry cost reference data released by construction decoration industry associations. Update rhythm follows project execution milestones: progress data is updated monthly, and full cost and revenue data is updated during annual final accounts. Most documents are structured Excel or multi-page PDF formats, containing fields such as project number, contracted contract amount, completed output value, labor cost, material unit price, management fee rate, and others. Units include ten thousand yuan, man-days, square meters, yuan/square meter, and similar units.

What constraints these characteristics impose on deployment and upgrade

Multiple heterogeneous data sources require supporting multi-format file uploads and structured parsing adaptation during deployment, to prevent data access failures caused by only supporting a single format. Large-volume, long documents require configuring longer file parsing timeouts. During upgrade, the old version's large file parsing logic must be compatible, to avoid parsing interruptions after upgrade. Rich fields and diverse unit structures require configuring custom field mapping rules during deployment. During upgrade, user-configured mapping relationships must be retained, to prevent data cleaning from failing. Incremental updates based on project milestones require supporting incremental index synchronization. During upgrade, the incremental synchronization logic must be ensured not to conflict with full indexing, to avoid wasted resources from repeated calculations.

How to set the configurations

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`2000 MB`	Engineering consulting financial reports are mostly multi-page PDFs or structured Excel files, with single-file volume often exceeding 500 MB. This setting reserves sufficient upload space
`PARSE_FILE_TIMEOUT_SECONDS`	`1200 seconds`	Large-volume financial report parsing requires longer processing time, to avoid mid-process timeout interruptions
`CHUNK_SIZE`	`1000–1500 characters`	Financial reports contain multi-field paragraphs. Chunk length is set to adapt to the semantic integrity of structured text
`RECALL_TOP_N`	`Top 8 entries`	A single project's financial report is associated with multiple historical project datasets. Sufficient associated entries are needed to support analysis
`SIMILARITY_THRESHOLD`	`0.75–0.85`	Filters low-relevance cross-project data, retaining highly matched historical reference information
`INDEX_SYNC_INTERVAL`	`Hourly`	Engineering consulting financial reports are updated based on project milestones. Incremental synchronization frequency matches the business update rhythm

The parameter values provided on this page are all common starting points for configuration. Actual values are affected by document 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 mistakes

Symptom: After entering the FastGPT container, executing npm list mongoose returns no results, while the mongoose dependency declaration is visible in the project root directory's package.json. Cause: Dependencies are only installed within the project directory in the container, the npm command was not executed after switching to the project directory, or the npm global path was not configured.
Symptom: After deploying via docker-compose, the knowledge base management interface fails to display uploaded financial report indexes, and search results are empty. Cause: No index synchronization-related environment variables were added to the environment field in docker-compose.yml, and no local index configuration files were mounted to the corresponding paths in the container.
Symptom: An error prompt pops up when viewing session details. The backend log returns 500 Internal Server Error with the prompt aiproxy connection failed. Cause: Versions 4.9 and above enable aiproxy by default. The AIPROXY_URL environment variable was not configured, or the aiproxy service was not started.

How to confirm the configuration is correct

Upload a test engineering consulting financial report file. After the upload progress bar completes, the file is displayed in the knowledge base list, with no parsing failure prompts.
Execute docker exec -it [container name or ID] npm list mongoose. The returned dependency version matches the version declared in the project root directory's package.json.
Access the backend log interface of the deployment address, and check the most recent session logs. No aiproxy connection failed type errors are present.
Initiate a financial report analysis request. After waiting for the result to return, verify that the project fields included in the result match the fields in the uploaded file.

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.