Deployment and Upgrade for Competitor Quote Bidding

What the data for this category looks like

Competitor quote bidding data primarily originates from public bidding platforms, quote documents published by industry associations, and tender documents voluntarily disclosed by enterprises. Data update frequency fluctuates with project cycles, with no fixed schedule. Individual project documents range from hundreds to thousands of characters in length. Most document structures include fields such as project number, competitor entity name, itemized quote details, total quote amount, delivery cycle, and payment method. Common units are ten thousand RMB and natural days.

What constraints these characteristics impose on deployment and upgrade

Scattered data sources and inconsistent formats require configuration of multi-source data access adaptation rules and custom field mapping capabilities during deployment. Update cycles have no fixed schedule, so configurable incremental synchronization scheduling must be retained during upgrades to avoid excessive resource usage from full synchronization. Non-standard differences exist in fields and units, so preset field conversion rules must be configured during deployment to ensure consistent processing of competitor quote data from different sources. Wide variation in document lengths requires adjustment of timeout and memory allocation parameters for the parsing module to accommodate long document parsing requirements.

How to Set Configurations

Configuration Item	Recommended Setting	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Competitor quote documents are mostly long-form PDFs or web pages, with longer parsing times than generic documents; default timeouts are insufficient for complete parsing
`RECALL_NUM`	`Top 8 entries`	Single competitor quote data has concentrated information; too many recalled entries increase context redundancy, while too few fail to cover core bidding information
`SIMILARITY_THRESHOLD`	`0.75–0.85`	Differences in quotes between competing entities in the same industry must be distinguished; a threshold that is too low introduces irrelevant data, while one that is too high misses valid matches
`DATA_SYNC_INTERVAL`	`Project-triggered + daily incremental sync`	Competitor quote updates align with bidding project progress; fixed-interval syncs generate invalid requests, while on-demand triggering reduces resource consumption
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Some large tender project documents include multi-page attachments; file upload limits must be expanded to cover complete data
`CUSTOM_FIELD_MAPPING`	`Enable custom field mapping`	Non-standard differences exist in competitor quote fields; fields such as "total price" and "delivery cycle" from original documents must be mapped to system standard fields

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test against your own samples before finalizing settings.

Three Common Misconfigurations

The observed issue is API request failed: 404 - Resource not found. The root cause is incorrect configuration of the API interface address or permission parameters for the competitor quote data source, preventing the system from accessing the target data interface.
The observed issue is Docker build dependency installation timeout, with timeout exceeded appearing in the error log. The root cause is failure to adjust build timeout parameters; parsing dependency packages related to competitor quotes have large file sizes, and default timeouts are insufficient to complete installation.
The observed issue is empty fields in parsed competitor quote documents. The root cause is failure to enable custom field mapping configuration, so non-standard fields from original documents cannot be converted to system-recognizable standard fields.

How to Verify Correct Configuration

The docker ps command is executed, and the fastgpt-app and fastgpt-parser core containers are confirmed to be running with no abnormal exit markers.
A locally stored competitor quote PDF document is uploaded, and the parsed result is checked for core fields such as project name and total quote amount, with no missing or garbled characters.
A data sync task is manually triggered, the system backend logs are reviewed, and no error logs of the resource not found or parsing timeout type are confirmed to be present.
The knowledge base recall interface is called, competitor-related keywords are passed in, and the number of returned results is confirmed to match the value of the configured RECALL_NUM parameter.

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.