What Data Looks Like for This Category
Refined oil product sales-related data primarily comes from sales ledgers in gas station operation systems, VAT invoicing records, oil price adjustment notices, compliance quality inspection reports, and customer service work orders. Data updates follow three schedules: sales ledgers and invoicing records are updated daily; price adjustment notices are released temporarily alongside market adjustments; compliance documents and work orders are updated upon completion of individual transactions. Most document structures are structured tables and standardized documents, containing fields such as product number, sales volume, unit price, revenue, station number, etc. Units include liters, yuan, yuan per liter, batch numbers, and more.
What Constraints These Characteristics Impose on Vector Models and Indexing
High proportions of structured data and closely linked fields in refined oil product sales require retaining field-level association information during indexing, to avoid disrupting business logic via chunking. Mixed data from multiple sources (tables and documents) requires vector models to adapt to text encoding for different formats, otherwise semantic alignment deviations may occur. Daily updated sales data and temporarily released price adjustment notices require indexing to support incremental updates and on-demand refreshing, to avoid performance losses from full reconstruction. Multi-unit fields and standardized document structures require field standardization before indexing to ensure consistency in vector encoding.
How to Configure
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the information density per chunk of refined oil sales ledgers and standardized documents, avoiding overly short chunks that disrupt business logic or overly long chunks that cause redundant vector encoding |
| Overlap Length | 100–200 characters | Retains field association information between adjacent chunks, preventing cross-chunk business logic from being split |
| Similarity Threshold | 0.72–0.85 | Adapts to the semantic similarity distribution of refined oil sales data, filters low-relevance recall results, and ensures recalled content matches business requirements |
| Single Search Limit | Top 8 entries | Balances recall coverage and retrieval efficiency, adapting to recall needs for mixed data from multiple sources |
defaultToken | 4000 token | Adapts to the context length of single-document drafting, covering core business information |
maxToken | 12000 token | Meets context length requirements after merging multiple documents in batches, adapting to complex drafting scenarios |
Three Common Mistakes
- Observation: Retrieval logs show relevant sales ledgers and price adjustment notices are recalled, but the large language model outputs "no matching content found". Cause: The vector model normalization configuration is not enabled, causing a mismatch between the distribution of recalled vectors and query-encoded vectors, so the model cannot correctly match semantic associations.
- Observation: Knowledge base retrieval responses time out, or the token consumption of a single retrieval far exceeds the preset value. Cause: The
maxTokenvalue is not restricted, causing fully recalled documents to be sent directly to the large language model, exceeding the context capacity limit. - Observation: Recalled results for structured sales data after indexing are disorganized, and field associations for individual records are lost. Cause: Chunk length is set too small, splitting multiple fields of a single sales record into different chunks, preventing the model from associating complete business information.
How to Confirm Proper Configuration
- Access the vector model configuration interface in FastGPT v4.17.0, confirm that the normalization switch is enabled to adapt to the currently used embedding model.
- Perform a retrieval test for a single sales ledger, verify that the chunk length and overlap length of the recalled results match the preset configuration.
- Check retrieval logs to confirm that the number of recalled documents per single retrieval does not exceed the set single search limit.
- Test batch document drafting scenarios, verify that the context token count sent to the large language model does not exceed the
maxTokenlimit.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.