What the data for this category looks like
Core data for this category originates from central state-owned enterprise and public sector procurement platforms, public resource trading platforms, supplier databases, contract management systems, and audit issue databases, with updates occurring daily. Daily generated documents include procurement plans, requirement specifications, bidding documents, bid evaluation records, contracts, acceptance documents, and supplier complaint materials. All document types include dedicated fields such as project number, procurement method, budget amount, supplier qualifications, and evaluation criteria. The priority of fields across different documents adjusts dynamically based on the compliance review stage.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
Decentralized data sources with daily updates require multi-turn dialogue to accurately associate knowledge base documents for a specified project, and avoid retrieving irrelevant cross-project procurement data. Diverse document types with dedicated fields require prompts to clearly define the scope of documents to review and field priority, to ensure generated compliance opinions match the requirements of the corresponding stage. The most time-consuming verification steps center on core content such as procurement methods, scoring rules, and supplier qualifications. Multi-turn dialogue must retain historical review records to avoid repeated verification of the same information. Errors can lead to compliance risks and personnel accountability, so retrieved documents must be accurate, and unsupported review conclusions must not be generated.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for long documents such as bidding documents and bid evaluation records, and prevents key clauses from being truncated |
| Overlap Length | 100–200 characters | Connects the semantics of adjacent chunks, and ensures continuity of content such as cross-chunk evaluation criteria and qualification requirements |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance procurement data, and focuses on document content directly related to the current review project |
| Single Search Limit | Top 6 entries | Controls the total context length, matches the information density of tendering and procurement documents, and avoids model overload |
messages Parameter Configuration | Append historical context by conversation turn | Retains key records of multi-turn compliance reviews, and supports retrospective review of earlier stages including qualification review and scoring verification |
variables Variable Binding | Bind procurement project ID, department identifier | Accurately associates dedicated project documents in the corresponding knowledge base, and avoids confusion of cross-project data |
Three Common Mistakes
- Significant differences exist between results returned by online chat and API calls. When the API has
streamset to false anddetailset to true, expected compliance document references are not returned. The cause is failure to bind the corresponding project's knowledge base via thechatIdparameter in the API request, resulting in the context not loading the specified procurement documents. - Prompts and knowledge base reference templates are confused. The reference content template and reference template prompt are mixed, leading to generated review opinions that do not match the budget basis of the procurement plan. The cause is failure to clarify the usage boundaries of the two template types: the former is used to format retrieved document content, and the latter is used to guide the model to integrate compliance rules.
- Historical context is lost during multi-turn dialogue, making it impossible to trace earlier supplier qualification verification records in subsequent review stages. The cause is failure to append historical conversation content to the
messagesparameter, resulting in loss of information from key compliance review nodes.
How to Confirm Proper Configuration
- Initiate a multi-turn dialogue for a specific procurement project, and check whether the retrieved documents include dedicated content such as the project’s bidding documents and bid evaluation records.
- Review API request parameters, confirm that
messagesincludes historical conversation turns, and thatvariableshas bound the identification information of the corresponding procurement project. - Adjust the similarity threshold configuration, and observe whether the relevance of retrieved documents meets the actual requirements of compliance review.
- Trigger an API call and view the returned results, confirm that document content in the knowledge base has been correctly referenced, and that the context of the multi-turn dialogue is coherent.
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.