What this category of data looks like
Daily documents for this category include tender documents, qualification certificates, commercial deviation tables, and technical deviation tables. Data sources include tender announcements, tender documents, enterprise qualification databases, and supplier quotations. The update frequency is once per project. Documents use structured tables and clause text as their core structure, with dedicated fields such as qualification numbers, quotation amounts, and tender clause numbers. Strict alignment with the tenderer’s format requirements is mandatory. Each business corresponds to a separate dataset. Core processing steps focus on clause-by-clause response, qualification material verification, and seal packaging checks.
What constraints these characteristics impose on workflow orchestration
Data for this category must strictly match the exclusive requirements of a single tender project. Each dataset is independent, and format requirements vary widely across projects. Workflows must support dynamic loading of configuration rules per project. Document structures include multiple types of structured tables and deviation tables. Differentiated validation and parsing logic must be set for different document types to prevent cross-project data confusion. Errors directly lead to bid invalidation or bid deposit losses. Workflows must embed mandatory validation steps. Core qualification verification and clause matching steps cannot be skipped. Workflows must support quick locating and tracing of abnormal nodes.
How to set configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Variable Passing | Enable the variables parameter to bind externally passed values | Supports passing project identifiers via links to match exclusive data for individual tender projects |
| Context Retention Rounds | Retain 1 round for the first AI call, 5 rounds for the second AI call | Matches different business needs for clause response and qualification verification, avoiding redundant or insufficient context |
| Custom File Read Address | Configure CUSTOM_READ_FILE_URL to the internal file service address | Resolves file parsing path issues after local deployment, ensuring workflows can properly read uploaded files |
| Node Identifier | Set a unique nodeId for each node | Facilitates quick location of abnormal nodes during defect analysis and tracing of execution logs |
| Timeout Threshold | Set to 600 seconds | Accommodates time-consuming business steps such as qualification material verification and clause response, preventing mid-process interruptions |
| File Upload Node | Enable the workflow’s built-in file upload function | Supports direct upload of business documents such as qualification certificates and tender documents, without additional transit |
Three Common Mistakes
- Attempting to pass parameters in the
?data=1123format via a link without binding thevariablesparameter results in empty workflow global variables. This occurs because variable mapping rules are not correctly configured, and no association is established between external link parameters and workflow global variables. - Configuring dual AI model call nodes without separately setting context retention rounds leads to inconsistent chat history counts when accessing the same ID multiple times. This occurs because differentiated context rules are not configured for different business scenarios of clause response and qualification verification.
- Enabling the workflow file upload node after local deployment and directly calling the document parsing tool results in a file path inaccessible error. This occurs because the
CUSTOM_READ_FILE_URLenvironment variable is not configured, so workflows cannot locate the actual storage path of uploaded files.
How to confirm proper configuration
- Construct an access link with project identifier parameters, trigger the workflow, and check the global variables panel to confirm passed parameters are correctly loaded.
- Call the two AI model nodes separately, check their respective context logs, and confirm retention rounds match the configured requirements.
- Upload a test document to the workflow file upload node, call the document parsing tool, and confirm normal return of parsing results without errors.
- View the workflow node list, confirm each node has a unique
nodeIdthat can be quickly identified via its identifier.
The values given 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 2026-10-19.