What This Category of Data Looks Like
This category of data is sourced from enterprise procurement platforms, public resource trading platforms, supplier databases, contract management systems, and audit issue databases, with a daily update frequency. Daily documents include procurement plans, requirement specifications, bidding documents, bid evaluation records, contracts, acceptance receipts, and supplier complaint materials. Core fields include procurement methods, budget amount, supplier qualification requirements, scoring rules, performance clauses, and others. Some documents are multi-page long texts, and data structures vary based on procurement project type.
Constraints Imposed by These Characteristics on Workflow Orchestration
Configure multi-source data access to align with the format specifications of different systems to avoid data parsing failures. Add a scheduled synchronization node to the workflow for daily updated data, ensuring compliance reviews use the latest supplier database and audit issue database data. Split long documents such as bidding documents to align with business logic, avoiding separation of key compliance clauses. Add step-by-step verification nodes to the workflow for time-consuming links including procurement method verification and scoring method review, reducing error risks.
Configuration Settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the content density of long documents such as bidding documents and bid evaluation records, avoiding overloading single chunks |
| Overlap Length | 100–200 characters | Ensures information continuity between adjacent chunks, preventing key compliance clauses from being split across two chunks |
| Single Search Limit | Top 5 entries | Focuses on core procurement basis, avoiding redundant information interfering with compliance review judgments |
| Maximum Context | 8000 tokens | Calibrated through actual testing, adapting to the context association requirements of procurement documents for Chinese public sector and state-owned enterprises |
| Timeout Setting | 60 seconds | Meets the response timeliness requirements for daily service inquiries and responses |
variables | Bind procurement project-specific fields | Adapts to the personalized data needs of Chinese public sector and state-owned enterprise procurement, ensuring accurate project information is called by the workflow |
nodeId | Bind compliance verification node ID | Ensures the correct execution order of verification links in the workflow |
Three Common Mistakes
- Published workflow links display local addresses: Access links use the
http://localhost:3000/chat/format. This happens when the deployment domain name is not configured, and the workflow is published using the local startup environment directly. - AI output formats fail to meet business requirements: Returned content lacks a fixed compliance review structure. This happens when no output format constraint node is set in the workflow, and the content range of AI output is not limited.
- Knowledge base long document recall performance is poor: Key information in long bidding documents is missing during recall. This happens when title hierarchy is used as the delimiter, and the paragraph and compliance clause structure of procurement documents is not adapted.
How to Verify Proper Configuration
- Confirm that the workflow published link uses the official deployment domain name, replacing the local address with a publicly accessible address.
- Trigger the workflow, enter a compliance review inquiry question, and confirm that the AI output conforms to the preset structured format.
- Upload a single long bidding document, confirm that the recalled chunk content covers core compliance clauses such as procurement methods and budget basis.
- Confirm that the
variablesparameter is bound to the current procurement project's specific fields, ensuring accurate information is called by the workflow.
The values listed are common starting points and should be evaluated 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 2026-09-28.