What this category of data looks like
Data sources for this category include state-owned enterprise procurement platforms, public resource trading platforms, supplier databases, contract management systems, and audit issue databases. The update frequency is daily. Routine documents cover procurement plans, demand specifications, bidding documents, bid evaluation records, contracts, acceptance documents, and supplier complaint materials. Document fields include procurement methods, budget basis, scoring methods, supplier qualification requirements, performance acceptance clauses, party building learning-related requirements, and more. The length of individual documents varies widely, ranging from hundreds to thousands of characters.
What constraints these characteristics impose on multi-turn dialogue and prompts
Daily data updates require multi-turn dialogue systems to associate the latest procurement data and party building learning materials in real time, and avoid using outdated information. Diverse document types and clearly subdivided fields mean prompts must adjust matching logic for different document scenarios, such as bidding document review and bid evaluation record verification. Data is associated with multiple departments and systems, so multi-turn dialogue must distinguish query needs of different roles, while ensuring that question and answer content complies with audit and discipline inspection requirements. Question and answer results directly affect procurement compliance and the accuracy of party building learning, so verification steps must be added to avoid erroneous conclusions related to bid rigging and audit rectification.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the current procurement project | Accurately associate procurement documents and party building learning materials for the corresponding project, and avoid confusion across projects |
messages | Retain the most recent 8 rounds of dialogue, with a total length not exceeding 10000 characters | Balance the completeness of context information and question and answer response speed, and prevent redundant information from interfering with compliance judgments |
variables | Pass in procurement project number, party building theme keywords, and document type tags | Limit the scope of question and answer, and only call procurement and party building data related to the current scenario |
| Timeout threshold | 60 seconds | Match the query requirements of real-time procurement data, and avoid dialogue interruptions caused by too long data loading |
| Number of recalled results | Top 5 | Focus on core procurement compliance and party building learning content, and reduce interference from irrelevant information on question and answer |
Three common misconfigurations
- Issue: An error
Cannot read properties of null (reading 'q')occurs when calling the question and answer interface. The cause is that the dialogue queue for themessagesparameter is not initialized correctly, resulting in a null pointer exception. - Issue: Different types of procurement documents cannot be uploaded separately in multi-turn dialogue. The cause is that the business scenario of the file upload entry is not distinguished through the
variablesconfiguration, resulting in mixed-up document classification. - Issue: The accuracy of problem category recognition in the workflow is low. The cause is that the prompt is not bound to the exclusive fields of procurement compliance and party building learning, resulting in the model generalizing to recognize irrelevant scenarios.
How to verify successful configuration
- Initiate a multi-turn dialogue that includes the procurement project number and party building theme, and check whether the question and answer results are associated with the procurement documents and party building learning content of the corresponding project.
- Upload multiple procurement documents of different types, and verify whether files from different upload entries are correctly classified and bound to the corresponding question and answer scenarios.
- After triggering a question and answer, check the system log to confirm that the context length of the
messagesparameter does not exceed the set threshold, and there are no abnormal errors. - Enter test content unrelated to procurement compliance or party building learning, and check whether the question and answer rejects irrelevant queries or triggers compliance verification.
The values provided 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 2026-09-28.