What the data for this category looks like
Coke marketing-related data comes from three main sources: financial industry commodity news platforms, real-time quotes from spot trading platforms, and internal enterprise marketing material libraries for financial clients. There are three update cycles:
- Spot quote data updates daily
- Industry research reports are released weekly or monthly
- Promotional marketing copy is adjusted irregularly alongside campaigns
Document structures include structured trading parameter fields such as delivery grade, sulfur content, origin, and daily settlement price, as well as unstructured promotional scripts and regional market descriptions. Common units include yuan/ton, percentage, kilogram, and others.
What Constraints These Characteristics Impose on Vector Models and Indexing
The multi-type characteristics of coke marketing data create clear constraints for the vector model and indexing workflow. First, structured trading fields make up a large share of the dataset. Indexing must support joint retrieval of text vectors and structured metadata. Without this, user queries for conditions like grade and origin cannot be matched accurately. Second, existing data can reach hundreds of thousands of entries, and spot data updates daily. An incremental index refresh strategy must be configured to avoid performance losses from full index rebuilding. Third, marketing content includes many commodity-specific technical terms. A vertical domain-adapted vector model must be selected to ensure accurate semantic embedding. Fourth, individual document lengths vary widely, from short scripts to long promotional plans. Segmentation rules must be adjusted to handle text inputs of different lengths.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Accommodates the varying lengths of coke marketing documents, balances semantic completeness and retrieval accuracy |
embedding_model | text-embedding-ada-002 or vertical commodity embedding model | Covers coke domain-specific technical terms, meets validation standards for general deployment |
index_refresh_interval | 1 hour | Matches the update cycle of spot data, reduces performance overhead from full index rebuilding |
structured_field_filter_enabled | Enabled | Supports joint retrieval of structured fields such as origin and grade, improves matching accuracy |
embedding_batch_size | 32–64 items per batch | Adapts to memory limits of locally deployed PGSQL vector databases, avoids overflow during batch processing |
vector_db_top_k | Top 10–15 results | Focuses on highly relevant results, meets the precise retrieval needs of coke marketing scenarios |
The parameter values provided on this page are common starting points 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
- Issue: Knowledge base retrieval is slow, and the interface shows response timeouts. Performance issues are incorrectly attributed to the vector model. Cause: Incremental index refresh is not configured. Rebuilding indexes for hundreds of thousands of coke data entries occupies excessive resources of locally deployed PGSQL vector databases. Context processing overhead of the language model is also ignored. This issue is common in the locally deployed environment of version V4.8.20-FIX2.
- Issue: An error "No matching embedding model found" occurs when calling the knowledge base.
text-embedding-ada-002has been added to OneAPI and configured in the configuration file. Cause: The vector model configuration item does not exactly match the model name in the OneAPI channel. For example, the full name is mistakenly written as an abbreviation, causing the system to fail to recognize the available model. - Issue: Retrieval results do not include coke data from a specified origin. Cause: Structured field filtering is not enabled. Fields such as origin and grade are not included in the index association rules. Retrieval only relies on text semantics, so structured parameters cannot be matched accurately.
How to Confirm Proper Configuration
- Run a knowledge base index refresh task. Check whether the task log shows successful incremental synchronization, with no time-consuming alerts for full index rebuilding.
- Enter a query containing coke technical terms and structured parameters in the test interface. Check whether retrieval results include matching markers for the corresponding fields.
- Call the vector model interface. The returned embedding vector dimension matches the dimension specified in the configuration item, with no error returned.
- Check the index shard status of the vector database. Confirm that the number of shards matches the configured
vector_db_shard_countparameter, with no shard exceptions.
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.