What This Category of Data Looks Like
Data for this category primarily comes from procurement systems, supplier quotation documents, budget spreadsheets, and contract management systems. Commonly processed documents include purchase requisitions, price comparison records, formal contracts, and acceptance documents. Typical fields include purchase order numbers, full supplier names, budget amounts, clause details, and delivery cycles. Data is updated per individual purchase order: corresponding documents are gradually generated from the initiation of a requirement through to completion of acceptance. Monthly aggregated archived data for all purchases in the current period is compiled each month.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The scattered nature of data sources requires that multi-turn dialogue clearly link to the unique identifier of the corresponding purchase order, to avoid mixing information such as purchase requisitions and contracts across orders. The requirement that updates are processed per individual purchase order means conversation contexts must be bound to the unique identifier of the target order, ensuring each interaction only calls data associated with that order. Documents include rigorous content such as contract clauses, so prompts must limit output formats and control context length to prevent key information from being truncated due to exceeding processing limits.
Configuration Settings
The following configurations are based on FastGPT v4.17.0
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the current purchase order | Ensure multi-turn dialogue only links to data such as purchase requisitions and contracts for the target order, avoiding cross-order information mixing |
| Max Context Length | 800–1200 characters | Documents in this category often contain long clause text; this range covers key content of core documents for a single order, preventing context overflow |
| Question Optimization | Enabled, calibrated based on actual testing | Pre-sales Q&A often involves vague procurement requirements; optimization enables more accurate matching of supplier quotations and budget data |
| Single Retrieval Limit | Top 6 entries | The number of associated documents for a single purchase is limited; too many recalled entries increase processing load, while too few may miss key price comparison records |
variables | Inject the purchase order number and budget amount for the current order | Allow prompts to directly call order-specific fields without repeatedly asking the user for known information |
| Conversation History Retention Period | 30 days | Matches the monthly archiving update rhythm of this category, avoiding storing excessive expired data |
messages | Append the latest interaction content by conversation turn | Preserve historical communication context for pre-sales Q&A, ensuring logical consistency of multi-turn dialogue |
Three Common Misconfigurations
- Symptom: After invoking a workflow, the running data in the conversation log is empty. Cause: The exclusive data of the corresponding purchase order is not bound via
chatId, so the system cannot match associated documents such as purchase requisitions and contracts, and cannot generate running data. - Symptom: When using the
anyparameter in the prompt of the text content extraction module to extract code blocks, the complete response shows the code block asundefined. Cause: The code block field of the corresponding purchase contract is not injected viavariables, so the prompt cannot locate the target extraction content, resulting in a null return value. - Symptom: After the user deletes a single conversation turn, the entire conversation log disappears synchronously. Cause: Independent storage rules for conversation context are not configured; the system default bound data associated with
chatIdis deleted synchronously, resulting in loss of order-specific conversation records.
How to Verify Proper Configuration
- Initiate pre-sales Q&A for a simulated purchase order, check that the conversation context only displays content associated with that order.
- View the conversation log to confirm that running data includes specific fields for the current order such as purchase order number and budget amount.
- Test deleting a single conversation turn, verify that remaining conversation content still retains associated information for the order, with no overall loss.
- Input a vague procurement requirement, confirm that the automatically optimized question can accurately match supplier quotation data.
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 2026-10-19.