What This Category of Data Looks Like
This category of data comes from customer manuscripts, color management systems, printing press data, ink and substrate batch records, spectrometers, and barcode detectors. Daily documents include pre-press files, imposition sheets, printing process sheets, ink color matching records, first article proofing, barcode inspection records, and die-cutting lamination records. Data is updated per order, per batch, or each time a plate is changed. Document fields include plate number, number of printing colors, substrate grammage, ink batch number, inspection results, and more. Units include g/㎡, color number identifiers, batch codes, and more.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Words
This category of data has a high update frequency and is differentiated by order, batch, or plate change. Multi-turn dialogue must avoid mixing data across orders, and the context range must be clearly defined in prompt words. There are various types of daily documents, so prompt words must guide users to specify the document type being queried to avoid mixed search results. Fields include detailed items such as plate number, grammage, batch number, and more. Multi-turn dialogue must gradually guide users to clarify specific query dimensions to reduce vague searches. At the same time, data comes from multiple types of equipment and systems, so prompt words must specify the data source tags to be retrieved to ensure results match the business scenario.
Configuration Settings
| Configuration Item | Recommended Practice | Rationale |
|---|---|---|
variables | Bind by order ID | This category of data is updated per order. Binding order ID via variables limits the search scope and avoids mixing data across orders |
| Maximum Context Length | 800–1200 characters | The total volume of documents per single order in this category is limited. An overly long context will introduce irrelevant historical content and reduce search accuracy |
| Maximum Search Results per Query | Top 5–7 results | This category has various types of daily documents. Limiting the number of search results avoids mixed outcomes and focuses on the document type for the current query |
| Question Optimization | Enabled | This category has many detailed fields. Optimizing questions guides users to clarify specific query dimensions such as plate number and batch number |
messages | Isolate by session ID | Multi-turn dialogue requires distinguishing different user sessions. Isolating via messages prevents data leakage across different sessions |
Three Common Configuration Mistakes
- Phenomenon: The chat interface cannot render embedded web content, and prompt content cannot be clicked to jump. Cause: Rich text rendering configuration for the chat interface is not enabled, and only plain text output is supported.
- Phenomenon: No image content is generated after calling, or there is no response when text-to-image is specified in the prompt words. Cause: Call permissions for the text-to-image model are not configured, and the image generation function is not integrated for the current Q&A scenario.
- Phenomenon: After configuring global variables, only 2 available variables are displayed, and historical configuration items cannot be loaded. Cause: In FastGPT v4.17.0, global variables must be bound separately via the application configuration page. The global variable entry logic differs in older versions, and the application context is not correctly associated.
How to Confirm Proper Configuration
- Enter a test order ID, bind it via
variables, initiate a query, and confirm that search results only include documents related to that order. - Initiate a multi-turn dialogue, gradually supplement detailed fields such as plate number and batch number, and confirm that the system can optimize questions and return accurate results.
- Switch to different session IDs to initiate queries, and confirm that conversation history and search results for different sessions are isolated from each other.
- Adjust the maximum search results per query parameter, and confirm that the number of returned documents matches the set range.
The values provided here are common starting points and should be measured against the reader's own samples.