What This Category of Data Looks Like
This category of data primarily comes from procurement systems, supplier quotations, budget spreadsheets, and contract systems. Daily processed documents include procurement requests, price comparison records, contracts, and acceptance slips. Each document contains fields such as procurement items, budget amounts, supplier qualifications, delivery quantities, and acceptance results. Units include yuan, units, copies, and others. Data updates follow a single procurement order cycle, with related records updated once each order is completed. Procurement order data within a single month does not change frequently.
What Constraints Do These Characteristics Impose on Multi-turn Dialogue and Prompting
Data sources are scattered, so multi-turn dialogue needs to associate context from different systems such as procurement requests and price comparison records. This requires support for cross-document context retrieval. Data updates follow a single procurement order cycle, so conversations must be isolated per procurement order to avoid mixing context from different orders. Each document has a fixed field structure, so prompts must clearly specify validation rules for corresponding fields to prevent model outputs from deviating from business requirements. Additionally, the monthly update frequency means conversation context does not need to be overly long, but must accurately bind exclusive data for the current procurement order.
Configuration Settings
The following configurations are formulated based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Dialogue context window size | 10000–15000 token | Adapts to the total length of documents related to a single procurement order, preventing context overflow |
| Conversation isolation rule | Bind procurement order ID via chatId | Ensures conversation history for each procurement order is independent, preventing data mixing |
| Prompt variable binding | Enable variables parameter | Dynamically inserts exclusive fields such as procurement order number and supplier name to improve prompt accuracy |
| Message history storage configuration | Store complete messages array | Retains full conversation history for retrospective procurement review processes |
| Message history retention limit | First 20 conversations | Matches the typical number of conversation turns for a single procurement order review, avoiding redundant content occupying context |
| File upload format restriction | Support PDF, DOCX, XLSX | Covers mainstream formats of daily procurement documents, reducing the probability of parsing failures |
| Dialogue timeout duration | 600 seconds | Adapts to the typical conversation duration for procurement reviews, preventing mid-session disconnections |
Three Common Misconfigurations
- Issue: Unable to parse procurement-related documents after upload. Cause: Allowed file formats were not configured, and only formats not used for daily procurement documents were enabled.
- Issue: Conversation history from different procurement orders is visible to each other. Cause:
chatIdisolation rules were not properly configured, and conversations were not bound to procurement order IDs. - Issue: Model responses do not use streaming output, only returning a single complete result after processing finishes. Cause: Streaming output configuration was not enabled, or the selected model does not support streaming return logic.
How to Verify Proper Configuration
- Upload daily documents such as procurement requests and price comparison records, verify that they can be successfully parsed and included in conversation context.
- Create conversations for two different procurement orders, send identical field validation requests, verify that each conversation's history is not visible to the other.
- Send a prompt request that includes specific field validation, check that the output strictly follows the preset format and field requirements.
- Conduct a long conversation test with more than 10 turns, verify that the context window does not experience truncation or errors due to excessive content length.
The values provided here 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.