What the Data for This Category Looks Like
Marketing content data for packaging and printing mainly comes from print order management systems, design draft shared libraries, customer communication ledgers, and printing process parameter manuals of financial, insurance, and wealth management institutions. Data updates trigger synchronously when new orders are created or design drafts are revised, with no fixed batch update cycle. Single marketing documents mostly use structured tables and mixed text-image content. They include fields such as packaging dimensions (unit: millimeters), material grammage (unit: grams per square meter), printing process type, quotation details, and delivery cycle (unit: days). Some materials also include physical scan images of printed finished products and compliance identification files.
Constraints on Model Integration and Configuration
Packaging and printing marketing content has many structured fields and industry-specific units. Model integration must accurately match the format and units of fields such as order dimensions, grammage, and processes from financial, insurance, and wealth management institutions. This avoids incorrect parameter values in model outputs. Data sources with no fixed update cycle require a knowledge base synchronization mechanism that supports on-demand triggering. This adapts to temporarily added order data. For mixed text-image material documents, configure a reasonable segment length. This prevents splitting that damages the integrity of process descriptions such as hot stamping and lamination. Some documents include physical scan images. Configure recall rules that allow associating non-text materials to ensure the integrity of marketing content.
How to Set Configuration Values
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Packaging and printing documents often include high-definition design drafts and process parameters, which take a long time to parse. 300 seconds covers the parsing process for most single documents |
rag_top_k | 6–8 entries | The process parameters and order fields of packaging and printing marketing content are highly correlated. A small number of precise recalls can meet model generation requirements |
SIMILARITY_THRESHOLD | 0.75–0.85 | Structured fields have high matching precision requirements. Filter out low-similarity irrelevant order data |
UPLOAD_FILE_MAX_SIZE | 500 MB | Packaging and printing design drafts are mostly high-definition vector images or PDF files. Support large file upload and parsing |
SYNC_KNOWLEDGE_MODE | On-demand synchronization | Packaging and printing orders have no fixed update cycle. On-demand synchronization avoids unnecessary knowledge base update overhead |
FIELD_EXTRACTION_RULE | Configured in the fixed format of order number, dimensions, grammage, process, delivery cycle | The field structure of packaging and printing marketing data is standardized. Fixed rules improve field extraction accuracy |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: When calling the deepseek32b model deployed locally via ollama, the test interface returns
500 Internal Server Error, and the model output has extra spaces and altered capitalized letters. Cause: The API address and request header parameters of the local model are not configured correctly, so FastGPT cannot establish a stable communication connection with ollama. Additionally, the built-in format correction logic of the model is not disabled. - Phenomenon: When parsing packaging and printing PDF documents, process parameter fields are split into different segments. Cause: The value of
PARSE_DOC_SPLIT_LENGTHis too small, which forces long-format process descriptions to be split and damages information integrity. - Phenomenon: The order data recalled by the knowledge base does not match the current marketing requirements. Cause: The value of
SIMILARITY_THRESHOLDdoes not meet the precision requirements of the current scenario. A large number of low-similarity historical order data are recalled, which interferes with model generation.
How to Verify Successful Configuration
- Upload a single packaging and printing design draft PDF, view the parsed segmented content, and confirm that the process description is not incorrectly split.
- Initiate a model test call, input packaging and printing marketing requirements, and check whether the parameter units and field formats in the model output are consistent with the source data.
- Manually trigger a knowledge base synchronization, check whether the synchronization log includes newly added order data, and confirm that the synchronization mechanism is working properly.
- Check the model list in the FastGPT backend, confirm that the deployed ollama model is displayed normally and can be used for test calls.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.