What this type of data looks like
Coke financing daily report data comes from spot trading data submitted by the domestic coal industry association, port coke warehouse receipt registration systems, and steel mill procurement ledgers. Full data for the prior day is updated every early morning.
Each document uses a format combining structured tables and daily industry abnormal movement remarks. It includes seven core fields: full name of the financing entity, financing amount (unit: ten thousand yuan), financing term (unit: calendar days), delivery location, corresponding coke grade (such as primary metallurgical coke), transaction unit price (unit: yuan/ton), and settlement method. A brief explanation of daily industry abnormal changes is attached at the end.
What constraints do these characteristics impose on the vector models and indexing link?
Structured fields account for more than 90% of coke financing daily report data. The data includes numeric fields with units and categorical fields. Direct full vectorization will introduce invalid noise from units and categorical identifiers.
The daily full update cadence requires the index to support high-frequency synchronization to avoid data lag. The daily industry abnormal movement remarks attached to each document are unstructured text with wide length fluctuations. Processing must adapt to segmenting text of different lengths, while retaining association information between fields to avoid breaking the binding relationship between financing entities and amounts after segmentation.
Semantic differences in financing data for different coke grades are significant. The vector model must be able to distinguish the associated features of grades and amounts.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
embedding_model | bge-m3 | Adapts to semantic associations between multimodal and structured fields, and supports vectorization conversion of both text and numeric fields |
chunk_size | 800–1200 characters | Matches the average length of the combination of remark text and structured fields in a single daily report, preventing excessive segmentation that disrupts financing scenario semantics |
chunk_overlap | 100–150 characters | Retains field association information across segments, avoiding breakage of the binding relationship between financing entities and amounts after segmentation |
index_refresh_interval | 86400 seconds | Aligns with the daily full update cadence to ensure index data stays synchronized with source data |
vector_search_top_k | Top 10 results | Retrieval needs for the coke financing scenario focus on daily core abnormal changes and amount matching. Excessively large recall result sets are unnecessary |
structured_field_embedding | Normalization by field type | Applies separate normalization processing to numeric fields such as amount and term, eliminating interference from units on vector similarity |
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 specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: Only vector data is stored in the vector database. Original structured fields cannot be associated, and retrieval results cannot display core information such as financing entities and amounts. Cause: The association storage path between original documents and vectors is not configured. Only vectorized fragments are uploaded, and the foreign key association of source data is not retained.
- Phenomenon: When using the
bge-m3model for retrieval, returned similarity scores are generally high and relevance is inconsistent with expectations. Cause: Numeric fields with units such as amount and unit price are not normalized. Original numeric values are directly used in vectorization calculations, which increases the weight of semantic similarity. - Phenomenon: After configuring the retrieval template, large model calls cannot trigger vector retrieval, and only general question and answer results are returned. Cause: The vector retrieval node is not bound in the retrieval template. Only the logic of direct question and answer via the large model is retained.
How to Confirm Successful Configuration
- Check the storage content of the vector database. Confirm that foreign key fields of original documents are associated with vector data, and verify consistency between fields and source data.
- Run a test retrieval. Enter combination keywords for coke grade and financing amount, review the similarity score distribution of returned results, and adjust
similarity_thresholdto a range that meets business requirements. - Trigger a manual index synchronization. Wait for the task to complete, then retrieve the latest daily report data from the current day, and confirm that the retrieval results include the latest entries.
- Review the retrieval template configuration. Confirm that the vector retrieval node is bound, and that trigger conditions match the business scenario of coke financing daily reports.
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.