What the Data for This Category Looks Like
This category’s data comes from product proposal documents in the internal marketing material library, customer case compilations, offline presentation PPTs, online promotional copy, and industry reference materials from partner institutions. Updates sync with new product launches, regional leasing policy adjustments, and new customer cases, with no fixed cycle. Document structure has two parts: structured parameter fields and unstructured text. Structured fields include asset category, applicable customer group tags, and effective time periods. Unstructured parts include marketing copy, landing page details, and case descriptions. Field measurement methods use months for term and ten thousand yuan for amount.
Constraints for Vector Models and Indexing
Mixed structured and unstructured content requires vector models to support both natural language text encoding and structured field semantic extraction. This avoids losing the association logic between parameters and text after splitting. No fixed update cycle requires indexes to support incremental update mechanisms. This prevents resource consumption and time delays from full index reconstruction. Structured parameters with specific fields and measurement methods require indexes to retain field metadata. This supports filtered recall by dimensions such as asset category and customer group tags. Marketing content includes effective time periods and matching channel attributes. This requires indexes to include metadata filtering conditions to avoid recalling expired or mismatched materials.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | text-embedding-ada-002 or open-source vector models of the same dimension | Supports general text encoding, enables mixed encoding of structured parameters and unstructured marketing text, and is compatible with most vector databases |
chunk_size | 800–1200 characters | Financial leasing marketing content includes long case descriptions and structured parameters. This range preserves semantic integrity and avoids context breaks caused by too short chunks |
chunk_overlap | 50–80 characters | Prevents semantic gaps after long documents are split, ensuring continuity of cross-chunk marketing copy |
vector_search_top_k | Top 10–15 results | Marketing content recall needs to cover multi-dimensional matching scenarios. Too many results increase inference time, while too few will miss high-quality materials |
similarity_threshold | 0.75–0.85 | Filters low-correlation materials while retaining differentiated content suitable for different customer groups |
index_refresh_interval | Triggered according to business updates | Financial leasing marketing materials have no fixed update cycle. Triggering incremental indexing as needed reduces resource consumption |
The parameter values provided on this page are common recommendations for establishing configuration baselines. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and testing on local samples is recommended before finalizing settings.
Three Common Misconfigurations
- Symptom: After configuring
embedding_modelastext-embedding-ada-002, the knowledge base reports the error “No available model”. Cause: The interface key and access address for the model are not synchronized in the vector database connection configuration, or the model name does not match the model names supported by the vector database. - Symptom: After indexing hundreds of thousands of marketing materials, search response time fails to meet business requirements. Cause: Incremental indexing configuration is not enabled, and full indexing is not split by material type, leading to scanning of excessive data blocks during vector retrieval.
- Symptom: Recalled marketing materials include expired content. Cause: Metadata filtering is not enabled in the index configuration, and the effective time period field is not included in the index filtering conditions.
How to Verify Proper Configuration
- Upload a single financial leasing marketing material, check if the chunking result covers both structured parameters and unstructured text, with no obvious semantic gaps.
- Submit a retrieval request that includes specific customer group tags or asset categories, confirm that recall results include content matching the metadata.
- Manually trigger a material update, check if the index for the corresponding material in the vector database takes effect as configured.
- Adjust the similarity threshold, verify if the correlation of recall results meets business screening standards.
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.