What the Data for This Category Looks Like
Packaging and printing investment research data comes from four main sources. These are industry association public reports, upstream raw material supplier quotation systems, internal production operation ledgers, and environmental compliance regulatory documents. Update cycles vary by data type. Raw material unit prices and production scheduling data update daily. Industry capacity and technology trend reports release quarterly. Compliance policy documents adjust per regulatory requirements. Three document structure types are used. Structured formats include quotation sheets and ledger tables. Fields include print size (mm), paper grammage (g/㎡), and order delivery lead time (days). Unstructured formats include equipment technical white papers and customer requirement descriptions. Mixed formats include project bidding proposal documents.
Constraints Imposed on Vector Models and Indexing
These characteristics impose clear constraints on the vector model and indexing workflow. Mixed-format document structures require multiple parsing rules. Structured tables need field extraction by row or column, followed by corresponding vector generation. Unstructured text needs splitting by paragraph. Frequently updated raw material and scheduling data need incremental index update strategies. This avoids performance loss from full reconstruction. Numeric fields with specific units, such as print size and paper grammage, need either normalization encoding first, or a vector model that supports numeric feature fusion. This ensures correct encoding of numeric semantics. Metadata from multiple data sources, such as update time and document type, must be added to index metadata. This supports subsequent recall filtering.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Mixed structured tables and long text; this range balances completeness of table field extraction and semantic coherence of long text |
embedding_model | Open-source model supporting numeric feature encoding or Baidu embedding-v1 | Data contains a large number of numeric fields; general text models struggle to capture numeric semantics |
index_update_strategy | Incremental update | Raw material and scheduling data are updated frequently; full update will consume excessive system resources |
recall_top_k | Top 10–15 results | Investment research scenarios require coverage of multi-dimensional raw material, capacity, and customer data; excessive recall increases inference overhead, while insufficient recall misses critical information |
vector_db_metadata_fields | ["document_type", "update_time", "unit"] | Need to filter recall results by document type, update time, and unit to meet precise retrieval requirements of investment research scenarios |
embedding_api_timeout | 30 seconds | Reserve sufficient time for external embedding interface calls to avoid request failures caused by network latency |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing.
Three Common Misconfiguration Issues
- Phenomenon: Embedding interface calls return 404 errors, and interface request failure prompts are displayed. Reason: The embedding interface access path is not configured correctly, or the interface version does not match the configured model name.
- Phenomenon: After deploying a local vector model without a GPU environment, the model cannot be added to the platform. Reason: The remote access port for the model is not enabled, or the corresponding port is not mapped during container deployment, preventing the platform from connecting to the model service.
- Phenomenon: Numeric fields in recall results have low semantic matching accuracy. For example, searching for "100g/㎡ paper" returns results for "80g/㎡". Reason: Numeric fields are not normalized and encoded, or a vector model supporting numeric feature fusion is not selected, resulting in failure to correctly encode numeric semantics.
How to Verify Proper Configuration
- Submit retrieval requests that include numeric fields, verify that recall results match the expected numeric range and unit, and confirm that metadata filtering rules take effect as configured.
- Submit a new piece of raw material data, check the index update time and policy match, and confirm that the incremental update function operates correctly.
- View platform interface call logs, confirm that there are no embedding-related error status codes, and confirm that model connection configurations are correct.
- Test parsing and vector generation for different document formats, confirm that vector generation results for structured tables and unstructured text meet expectations.
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.