What the data for this category looks like
Advertising and marketing industry financial report data mainly comes from internal enterprise marketing placement ledgers, media resource purchase contracts, performance reports from third-party monitoring platforms, and annual/quarterly marketing final settlement documents. Data update cycles are divided into three categories: weekly (monthly placement details), monthly (quarterly financial report drafts), and annual (full-year final settlement reports). Document structures are primarily structured tables containing fields such as channel_type (placement channel), spend_amount (spending amount), exposure (impressions), and conversion_rate (conversion rate), with units of ten thousand yuan, thousand impressions, and percentage respectively. Unstructured placement strategy descriptions and performance analysis paragraphs are also included.
What constraints these characteristics impose on deployment and upgrade
The multi-frequency update requirement of advertising and marketing financial reports means a scheduled incremental synchronization mechanism must be configured during deployment, to avoid excessive resource consumption from full synchronization. The mixed structured and unstructured document structure requires enabling both structured data extraction and unstructured text parsing models, and writing adapted prompts for dedicated fields. The varying volume of large annual reports requires relaxing the timeout thresholds for file upload and parsing. Minor differences in field units across different channels require configuring normalization rules during deployment. The upgrade process must maintain compatibility with old field extraction logic, to prevent format deviations in historical financial report analysis results caused by model updates.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Advertising and marketing financial reports include large annual media monitoring reports, requiring support for bulk upload of large files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing ultra-large annual reports takes a long time, avoiding interruptions to the parsing process |
MAX_CHUNK_SIZE | 800–1200 characters | Adapted to the mixed document structure of advertising and marketing financial reports, covering short ledger entries and long-form performance analysis |
RECALL_TOP_N | Top 8 entries | Marketing data has multi-dimensional associations, requiring sufficient recalled entries to cover core indicators such as placement channels, spending, and performance |
SIMILARITY_THRESHOLD | 0.72–0.78 | Balance recall precision and coverage, avoiding mixing irrelevant placement records while retaining associated data for similar marketing campaigns |
SCHEDULE_SYNC_CRON | 0 0 2 * * * | Synchronize the latest weekly placement data every day at 2 AM, adapting to the update rhythm of advertising and marketing data |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Response timeout or slower-than-expected response when calling the chat interface externally. Cause: The
RECALL_TOP_NandSIMILARITY_THRESHOLDparameters are not adjusted, resulting in too many irrelevant marketing data entries being recalled, which increases model inference time. - Phenomenon: Module loading errors related to
chakra-ui/reactoccur during local compilation and deployment. Cause: The officially specified Node.js version for FastGPT is not used, or files are missing due to network interruptions during dependency package installation. - Phenomenon: A
500 Internal Server Erroris returned when testing the interface with curl after Docker deployment. Cause: The dedicated field extraction prompt for advertising and marketing financial reports is not configured, causing the model to produce format errors when parsing structured data, triggering backend exceptions.
How to confirm the configuration is complete
- Upload a typical monthly advertising and marketing financial report document, verify that the parsed output fields cover core dimensions such as placement channel, spending amount, and exposure, and that field units match the original document.
- Initiate a test analysis request, verify that the interface response status code is
200 OK, and adjust relevant parameters according to your own business response time requirements. - Check the running logs of the scheduled synchronization task, confirm that the incremental data synchronization process has no abnormal errors, and matches the update rhythm of advertising and marketing data.
- Simulate adjusting the placement strategy field of a historical financial report to trigger model inference, verify that the analysis result adapts to the updated field logic.
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.