What the data for this category looks like
Packaging and printing marketing content primarily comes from internal design systems, ERP production systems, customer custom requirement documents, and process manuals from partner suppliers. Updates occur per individual project, triggered by the launch of new materials or changes to custom requirements. There are three types of document structures: plain text process instructions, material parameter lists with structured tables (including material, grammage, dimensions, and more), and design specification files with attachments. Fields include material name, material type, printing process, delivery lead time, customer ID, and more. Most units use industrial standard measurements such as grams (g), millimeters (mm), square meters (㎡).
What constraints do these characteristics impose on vector models and indexing
Packaging and printing marketing content includes long text process instructions, structured parameter lists, and attached design files. Vector models must support mixed text structures, and cannot rely solely on short text embedding logic. Updates follow a per-project schedule, not real-time. Indexes must support incremental refresh, not full reconstruction, to reduce computing resource usage. Structured information across multiple fields must first be converted to text formats compatible with embedding. This prevents loss of semantic association for structured parameters that cannot be embedded directly. Document lengths vary significantly across different materials. A variable-length chunking strategy is required to prevent truncation of critical process parameters or duplicate embedding of short text entries.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
EMBEDDING_MODEL | bge-large-zh-v1.5 (open source and free) or text-embedding-3-small | Packaging and printing documents contain specialized industrial terminology. Chinese embedding models can accurately match semantics around processes and materials, and meet dimension requirements of mainstream vector indexes |
Chunk Length | 800–1200 characters | Balances semantic completeness for long text process instructions and recall accuracy for short text material parameters, avoiding excessive splitting or truncation of critical information |
Chunk Overlap Rate | 10–15% | Preserves contextual association for process steps and parameter descriptions, preventing loss of semantic coherence across paragraphs after splitting |
RECALL_TOP_K | Top 6–8 results | Retrieval needs for packaging and printing marketing content mostly focus on matching specific material types. A small number of precise recalls can cover common business scenarios |
SIMILARITY_THRESHOLD | 0.72–0.78 | Differentiates documents with similar materials or processes, avoids recalling mismatched material parameters, and balances recall rate and precision |
INDEX_REFRESH_MODE | Incremental refresh | Matches the per-project document update schedule, eliminates the need for full index reconstruction, and reduces system load |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material types, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: No matching packaging process parameters are returned after retrieval. Cause: Structured fields such as grammage and dimensions were not converted to text formats compatible with embedding, causing vectors to fail to match semantic queries.
- Symptom: External index model connection fails with error code
Connection refusedwhen deploying version 4.9.6 locally. Cause: Access keys and intranet addresses for external index models were not configured in system settings, or corresponding ports were not opened in the container network. - Symptom: Index creation fails to associate with large language model context, returning
context not founderror. Cause: The corresponding embedding model was not bound in index configuration, causing retrieval results to fail to associate with original text sources and preventing valid context generation.
How to confirm correct configuration
- Upload a packaging and printing marketing material document, check the embedding task logs to confirm vector generation succeeded with no truncation errors.
- Submit a query that includes specific material parameters, verify the match between fields in the recalled results and the original document, adjust the similarity threshold to meet business requirements.
- Test the incremental index update operation, confirm newly added material documents can be retrieved normally without requiring full index reconstruction.
- Check the connection status of the external index model, view vector storage read/write latency in the system monitoring panel to confirm normal connection.
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.