What the data for this category looks like
Marketing content for coal chemical enterprises from financial institutions includes in-house supply chain financial product manuals, financing script templates, industry-specific promotional materials, plus publicly available coal chemical enterprise production capacity data, bidding project requirements, and industry technology updates. Update frequency varies by content type: financial product parameters update in real time alongside credit policy adjustments, industry-specific promotional scripts are optimized quarterly, and bidding information for coal chemical enterprises updates immediately as projects go live.
Document structure has two core forms: structured service parameter tables and long-form marketing scripts. Core fields include service name, service target type, service quota, service term, release date, and material type. Service quota is measured in ten thousand yuan, service term in months, and no percentage-based numerical statements are included.
What constraints do these characteristics impose on the vector model and indexing workflow
The mixed structure and update characteristics of marketing content for coal chemical enterprises from financial institutions create multiple constraints for the vector model and indexing workflow.
First, the mixed document format of structured service parameters and long-form scripts requires vector extraction to preserve completeness of cross-domain terminology, and avoid splitting that breaks parameter associations.
Second, coexisting real-time updated bidding information and periodically optimized promotional scripts requires the index to support a combined strategy of incremental synchronization and full refresh, to balance update efficiency and resource usage.
Third, content includes both financial and coal chemical professional terminology. Generic vector models struggle to accurately capture cross-domain semantics, so a multi-domain compatible embedding model must be selected.
Fourth, some materials contain duplicate standardized service parameters, so deduplication rules must be configured to avoid redundant retrieval.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | m3e-base | Compatible with Chinese cross-domain terminology, supports local deployment, meets common community configuration requirements |
chunk_size | 800–1200 characters | Balances segmentation accuracy for long-form scripts and structured parameter tables, avoids splitting parameter sets |
chunk_overlap | 50–80 characters | Preserves professional terminology connections between adjacent segments, prevents terms such as coal-based and financing from being split apart |
vector_store_type | HNSW | Supports fast approximate nearest neighbor search, adapts to real-time query requirements for million-scale marketing materials |
retrieval_top_k | Top 8–12 results | Covers retrieval requirements for multiple types of marketing materials, avoids insufficient or redundant recall |
retrieval_similarity_threshold | 0.75–0.85 | Filters low-relevance generic text, preserves retrieval accuracy for cross-domain content |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- The console displays "No available channels for embedding model", and no vector generation records appear after uploading coal chemical enterprise financing script materials. Cause: The deployment address or port for m3e was not added in system configuration, preventing normal invocation of the model service.
- After uploading a structured financial service parameter table, the index list is empty with no segmented data. Cause: The
chunk_sizeparameter was not adjusted. The default segment length is too short, causing the structured parameter table to be split in a way that does not match index rules. - The vector database returns high latency when querying the same coal chemical enterprise financing demand multiple times. Cause: Vector retrieval caching was not configured. Repeated queries do not reuse historical results, increasing database load.
How to Verify Successful Configuration
- Navigate to the model management page, check the running status of
embedding_model, confirm it displays "Connected". - Upload one financing script document for coal chemical enterprises, wait 1–2 minutes, then check the index list to confirm vector data for the corresponding segments has been generated.
- Initiate a query containing cross-domain terms such as "coal-based methanol" and "supply chain financing", check if the similarity scores of retrieval results fall within the configured threshold range.
- Upload a previously uploaded bidding project material, check if the index list only generates one new vector record with no duplicate entries.
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.