What Data for This Category Looks Like
Typical users include system integrator pre-sales consultants, solution architects, and sales teams. Data for this category originates from customer interview notes, public tender documents, partner vendor product materials, and historical project proposal libraries. Daily deliverables include technical proposal documents, customer requirement survey forms, quotation lists, and demo scripts. The overall structure is divided into standard modules: requirement clarification, technical roadmap, implementation boundaries, and cost accounting. Data updates follow a single-project core cycle. Projects that reuse templates may have content adjusted weekly. Fields cover business scenario descriptions, technical selection directions, estimated implementation cycles, and similar items. No fixed uniform unit standards apply, and adjustments are made based on specific project scenarios.
Constraints on Forms and Interactions
The data characteristics of this category impose multiple constraints on the forms and interactions phase. Dispersed data sources require forms to support both manual entry and external document fragment import modes. The high-frequency update feature for single projects requires external business systems to provide module copying and rapid reuse of historical project field capabilities. FastGPT forms only handle input collection. The modular document structure requires forms to display content in categorized blocks, such as technical proposals and requirement surveys, to avoid cluttered fields. Fields without fixed units require external business systems to support custom unit binding or unit adaptation logic. FastGPT forms only handle input collection. Pre-sales proposals must avoid overscope commitments. Compliance prompt interaction triggers are implemented by external business systems.
Configuration Guidelines
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Form Input Node formInput | Test example: Select 3 fields from pre-sales technical proposal fields as required inputs. Actual fields are determined based on the task | This node collects user input. Missing required inputs requires checking interaction status to prevent subsequent nodes from receiving incomplete input |
User Selection Node userSelect | Test example: Set 2 business options, verify corresponding branches one by one | This node handles user selection. Querying and filtering pre-sales technical proposal related records must be implemented by subsequent interfaces or external systems |
| Post-Submission Field Passing | Test example: Prepare 10 pre-sales technical proposal input samples, covering full completion, missing required fields, and canceled operations | Compare input and node output to verify whether subsequent nodes receive corresponding fields. Result thresholds are determined based on actual tasks |
| External Implementation Item: Form Field Block Display | Must be implemented separately by external systems. Divide into 3 independent blocks: technical proposals, requirement surveys, cost accounting | Aligns with the modular structure of documents in this category, improving entry readability |
| External Implementation Item: Custom Unit Binding | Must be implemented separately by external systems. External business systems implement period field unit binding for "days/weeks/months" and budget field unit binding for "ten thousand yuan/million" | Adapts to the field configuration rules of this category without fixed units |
| External Implementation Item: Compliance Prompts and Document Import | Must be implemented separately by external systems. External business systems implement field compliance prompts, set a single document import limit of 800–1200 characters | Avoids overscope commitments and prevents form loading lag |
Three Common Misconfigurations
- Phenomenon: After configuring third-party technical vendor endpoints, the form's model selection list does not match the actually deployed models, and preset custom models cannot be selected. Cause: FastGPT's model deployment configuration does not sync the model list deployed by the third-party technical vendor, only the base URL is configured without syncing the model list.
- Phenomenon: When cyclically calling AI sessions to generate proposal fragments, the input array parameter has no response, and the cycle does not trigger normally. Cause: The array parameter passed by the external business system does not follow the format required by FastGPT workflow cycle trigger nodes, and the cycle trigger condition is not correctly bound.
- Phenomenon: Preset requirement clarification guide words do not appear in the requirement document fragments generated by AI. Cause: The preset requirement clarification guide words are not bound to the AI system prompt configuration of the FastGPT workflow, only user input content is configured.
How to Verify Proper Configuration
The following verification actions are adapted for FastGPT v4.17.0. Some verifications must be completed in conjunction with external business systems.
- Import a historical project document fragment into the external business system, check whether the FastGPT
formInputnode can correctly receive and pass corresponding fields to subsequent workflow nodes. - Copy an existing proposal module in the external business system, check whether the FastGPT
formInputnode can correctly receive reused field content passed by the external system. - Configure custom unit rules in the external business system, check whether the field input interface corresponding to the FastGPT
formInputnode adapts to the unit configuration passed by the external system. - Enter field content that exceeds the preset range into the FastGPT
formInputnode, check whether the external business system triggers compliance prompts.
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.