What the data for this category looks like
The data sources for refining and chemical marketing content include supply chain financial product manuals from financial institutions for refining and chemical enterprises, exclusive financing policy documents for regional distributors, periodic financing promotion notices, financial service promotional materials from industry exhibitions, and compiled frequently asked question documents for refining and chemical enterprise customers. Update rhythms are adjusted to match business cycles: product manual updates trigger when new products launch or production capacities shift in the refining and chemical industry. Promotion policies update monthly or quarterly. Distributor policies are reviewed every six months. Most documents are long-form, with wide variation in single-document length. Some are short promotion notices of a few hundred words, while others are thousands of words of product compliance descriptions. Fields include product grade, octane number, sulfur content, financing amount, distributor code, and more. Supporting units include ppm, mg/L, ton, ten thousand yuan/ton, and others.
Constraints on vector models and indexing
Refining and chemical marketing content has wide variation in long-text length, abundant professional terminology, and significant differences in timeliness. These traits create multiple constraints for the vector model and indexing process. Long documents must be split to match their content structure. Do not split cross-chapter financial product clauses and refining industry technical parameters into the same paragraph, as this reduces vector representation accuracy. Professional fields such as octane number and sulfur content (industrial terms) coexist with financial fields such as financing amount. This requires the vector model to understand Chinese semantics across both industrial and financial domains. General embedding models may fail to accurately map cross-domain semantics. Update rhythms vary significantly across content types. Incremental indexing must support triggering updates by document type, to avoid wasting resources on full index rebuilding. Time-sensitive promotion content requires the index to include a timestamp field. This supports filtering invalid financing or policy content by release time.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | shaw/dmeta-embedding-zh | Adapts to professional terminology in industrial and financial domains, with more accurate semantic representation for refining-related technical text and financial product text |
chunk_size | 800–1200 characters | Matches the chapter length of refining and chemical marketing documents, avoids splitting paragraphs that cross technical or financial clauses, and ensures semantic integrity of single-paragraph vector representations |
chunk_overlap | 100–150 characters | Compensates for semantic gaps after long-text splitting, maintains contextual association between adjacent paragraphs, and prevents cross-domain semantics from being disconnected |
index_incremental_update | Enabled | Adapts to the batch update rhythm of marketing content, reduces resource consumption from full index rebuilding |
recall_top_k | Top 8–12 results | Professional information density of refining and chemical marketing content is high. Too many recalls will introduce irrelevant content, while too few will fail to cover valid information |
filter_by_timestamp | Enabled | Marketing content has strong timeliness, requiring filtering of invalid promotion or policy content by release time |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, 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
- Phenomenon: When the
recall_top_kvalue is set too large and time filtering is not enabled, knowledge base search responses time out. Cause: Too many non-timely content recalls lead to linear growth of vector similarity calculation volume, exceeding hardware computing power limits. - Phenomenon: When using a general Chinese embedding model to process refining and chemical marketing text, search result matching accuracy is low. Cause: General embedding models have not learned the semantic association of cross-domain professional terms in industrial and financial fields, and cannot accurately map the semantics of fields such as octane number and financing amount.
- Phenomenon: When adding a custom index without specifying field filtering rules, financing policies and product technical parameters are recalled together. Cause: Specialized fields such as product grade and financing amount are not bound through index configuration, making it impossible to filter valid content by business dimension.
How to Confirm Correct Configuration
- Upload one refining product manual and one financing promotion notice. Check that split paragraphs are divided by chapter, with no cross-domain content splicing. Confirm that the paragraph splitting configuration matches the document structure.
- Submit a search request with a professional term such as "92 octane gasoline". Check that returned results prioritize refining-related financial service content. Confirm that the embedding model adapts to cross-domain semantics.
- Submit promotion documents with timestamps and historical policy documents. Verify that search results can be filtered by release time. Confirm that the time filtering configuration is effective.
- Simulate an incremental update of a promotion notice. Check that the index only updates the new document without performing a full rebuild. Confirm that the incremental index configuration is correct.
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.