What the data for this category looks like
Data sources for this category include enterprise procurement platforms, public resource trading platforms, supplier databases, contract management systems, and audit issue databases. The data update frequency is daily. Common documents involved include procurement plans, requirement specifications, bidding documents, bid evaluation records, contracts, acceptance receipts, and supplier complaint materials. Document fields cover procurement methods, budget basis, scoring methods, supplier qualification requirements, performance clauses, and other items, with formats varying by project type.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
The correlation of multi-source data requires that dialogue context be bound to all data source information of the corresponding procurement project to avoid compliance verification errors across projects. Daily updated data requires that the dialogue link can call the latest supplier database and audit issue database content in real time to ensure review timeliness. Adaptation to multiple types of documents requires that prompt engineering adjust verification logic according to the field characteristics of different documents, while multi-turn dialogue must retain key nodes of previous reviews. Compliance risks require that the complete review trail be retained during the dialogue process to ensure traceability.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context Window Size | 8000–12000 token | Adapt to long texts of bid and tender-related documents and multi-turn dialogue context |
chatId | Bind to the unique ID of the procurement project | Ensure consistent association of multi-turn dialogue messages under the same project |
variables | Preset procurement method and budget threshold fields | Allow prompt engineering to automatically call compliance verification rules for the corresponding project |
| Maximum Dialogue Turns | Calibrated based on actual testing for the first 20 turns | Cover the complete process of declaration material review |
| Knowledge Base Recall Count | Top 8 entries | Match the upper limit of document count for single-turn dialogue in this category |
| Prompt Template | Embed the instruction "verify using procurement plans and bidding documents" | Adapt to the core review scenarios of this category |
Three Common Misconfigurations
- Symptom: The prompt cannot switch to English format, and the compliance rules returned by the knowledge base remain in Chinese. Cause: No language switching parameter is configured in
variables, and the prompt template is not bound to the call instruction for the corresponding language. - Symptom: Context is lost after multi-turn dialogue, making it impossible to continue the case file review process. Cause:
chatIdis not bound to the unique identifier of the procurement project, resulting inmessagesfrom different sessions not being associated with the same review task. - Symptom: The text content extraction module only displays GPT-style model options, and the configured GLM model cannot be selected. Cause: All bound model interfaces are not enabled simultaneously in the module configuration, leading to a limited model selection range for the text extraction link.
How to Confirm Proper Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
- Initiate a query regarding a procurement plan, check if the conversation history retains the previous round’s review key points, and confirm that
chatIdbinding is active. - Call the knowledge base to query the compliance rules for the corresponding project, check if the prompt automatically brings in the content of the preset
variablesfields. - Switch the model option, confirm that both the text extraction and dialogue modules can display the configured GLM model, and check if the model binding rules are correct.
- Upload a bid and tender-related document, confirm that the system can recognize and associate it with the context of the current review project, and check if the knowledge base association range is configured correctly.
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.