What the data for this category looks like
In marketing content and customer acquisition scenarios for the finance, insurance, and wealth management industries, papermaking category marketing data mainly comes from internal product specifications, customized proposal documents, marketing script libraries, industry exhibition promotional materials, and dealer feedback materials of partner papermaking enterprises. The update rhythm shifts with new product launches and quarterly marketing plan adjustments of partner enterprises. Single update materials cover three forms: short text scripts, dozens of pages of structured parameter tables, and mixed scenario proposals. Data fields include product category, grammage, material certifications, applicable scenarios, unit specifications, and some documents contain both structured parameters and unstructured marketing descriptions.
What constraints these characteristics impose on vector models and indexing
For marketing customer acquisition scenarios in the finance, insurance, and wealth management industries, the mix of short-text marketing scripts and long-document parameter tables requires vector models to adapt to a text length range of 80–5000 characters. This avoids semantic loss in short texts and semantic fragmentation in long documents. For scenarios where structured parameters and unstructured descriptions coexist, differentiated vectorization weights must be configured for different fields to ensure the recall priority of core product parameters. For the rhythm of batch updates and irregular new material additions, the index must support flexible switching between incremental synchronization and batch reconstruction to adapt to performance requirements under different update scales.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Ensures semantic integrity of long-document parameter tables in papermaking marketing materials, avoids losing core associations after long text splitting |
chunk_overlap | 50–100 characters | Retains contextual connection between segments, solves semantic breakage across segments in long parameter tables |
vector_model | bge-large-zh-1.5 or models with the same embedding dimension | Matches semantic understanding accuracy for Chinese papermaking industry marketing content. Embedding dimension must be compatible with the index library, and adapts to the model loading logic of version v4.8.7 |
recall_top_k | Top 8–12 results | Covers recall requirements for multi-scenario papermaking products, avoids missing customized proposal-related materials due to too few recall results |
similarity_threshold | 0.72–0.85 | Filters low-correlation recall results, adapts to precise matching requirements for papermaking product parameters |
index_batch_size | 50–100 items per batch | Adapts to batch update rhythms, balances performance and time consumption of index reconstruction |
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, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Unrelated auxiliary materials appear in recall results, with empty fields. Cause: Auxiliary data is not excluded from the vectorization index, or no field filtering rules are configured.
- Phenomenon: Duplicate segments appear in recall results after multiple vector groups are generated for a single piece of data. Cause: The
chunk_overlapparameter is set too large, leading to an overly high segment overlap ratio and generating duplicate vectors. - Phenomenon: Vector files generated by local models cannot be recognized by the server index. Cause: The embedding dimension of the server-side vector model is not matched, or the vector storage format supported by the server is not converted.
How to Confirm the Configuration Is Correct
- Vector generation logs are reviewed to confirm that segment length matches the set
chunk_size, with no abnormal truncation or overly long segments. - A small-batch test index is run, and the correlation of recall results is checked against business expectations, with the
similarity_thresholdadjusted to adapt to matching accuracy. - A set of test data containing both structured parameters and unstructured copy is imported, and the recall weights of different fields are verified to meet configuration requirements.
- The embedding dimensions of local and server-side vector models are compared, and it is confirmed that generated vector files can be loaded normally by the index library.
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.