Data Overview
Data for this use case is generated across the full tender lifecycle. Core documents include tender documents, qualification certificates, commercial deviation tables, and technical deviation tables. Data sources include tender announcements, publicly available tender documents, enterprise qualification databases, and supplier quotation databases. Each tender project’s data is updated once per project cycle, with content adjusted to match tender requirements. Document structures follow formats specified by the tenderer, with three core field types: clause response items, qualification matching items, and quotation verification items. There are no standardized fixed units; configurations must strictly follow field rules set by the tenderer.
Constraints for Multi-turn Dialogue and Prompt Engineering
Data sources are scattered and adjusted per individual project. Multi-turn dialogue must consistently associate with the exclusive context of the current tender project to avoid mixing data across projects. Document structures rely on formats specified by the tenderer. Prompts must strictly match field names set by the tenderer, otherwise compliant response content cannot be generated. Data is updated once per project cycle, so dialogue context must be retained for the full project cycle and reset afterward. The high time cost of responding to each clause individually requires multi-turn dialogue to support clause-by-clause breakdown and verification, preventing model understanding errors caused by inputting excessive content at once.
Configuration Settings
The following configurations are formulated based on the functional rules of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 token | Matches the total character count of single-project tender documents, prevents context overflow |
chatId binding rule | Bind a unique chatId to each single project | Isolates dialogue contexts for different tender projects, prevents data cross-use |
variables configuration | Pre-set tender number, project name, qualification library keywords | Quickly injects single-project exclusive data, reduces repeated input |
messages history truncation threshold | Retain the latest 30 dialogue records | Balances context completeness and model response speed |
| Maximum single reply length | 1500 characters | Matches the typical content length of single-clause responses, prevents output truncation |
| Question optimization switch | Enabled | Automatically breaks down complex requirements in tender clauses, adapts to clause-by-clause response needs |
Common Misconfigurations
- Symptom: Responses include qualification or quotation data from non-current tender projects. Cause: An exclusive
chatIdis not bound, leading to reuse of dialogue contexts across different projects. - Symptom: "Context limit exceeded" error is returned, or response content is truncated. Cause: No reasonable maximum context length and single reply length are set, exceeding model processing thresholds.
- Symptom: Only partial dialogue records are accessible, and full conversation history cannot be viewed. Cause: The
messageshistory retention parameter is not configured correctly, only partial conversation segments are called.
How to Verify Correct Configuration
- Create a dialogue for a new tender project, input response requirements for tender clauses, check that replies only associate with preset
variablesdata for the current project, with no irrelevant cross-project content. - Input long tender clause content, check that the model can respond completely without truncation or errors, confirm that maximum context length and single reply length are configured properly.
- Switch to the
chatIdof a different tender project, check that dialogue contexts are fully isolated, with no mixed cross-project data. - Call the dialogue record interface, confirm that all
messagesrecords under the currentchatIdcan be retrieved with no omissions.
The values provided are common starting points and should be assessed against samples specific to the target deployment scenario.
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.