What Data Looks Like for This Category
Refined petroleum product sales ledger data originates from gas station POS terminals, inventory management systems, oil tank level monitoring equipment, and value-added tax invoicing systems. Data update cycles cover three categories: real-time retail transactions, hourly inventory checks, and daily business summaries. Each ledger entry includes fields including fuel type, transaction time, station code, sales volume (unit: liters), unit price (unit: yuan per liter), revenue amount (unit: yuan), and inventory balance. Some stations’ ledgers also include oil tank level photos as supplementary supporting materials. Most documents are structured tables or structured JSON, while some historical ledgers exist as scanned PDF documents.
Constraints These Characteristics Impose on Vector Models and Indexing
The multi-source nature and varied update cycles of refined petroleum product ledgers require indexing to support both incremental and full update modes. This matches synchronization needs for real-time transaction data and daily summary data. The standardization of structured fields and units requires vector models to support mixed field encoding. This avoids vector matching deviations caused by unit differences. The presence of scanned PDF ledgers requires the indexing system to support storage and retrieval of multimodal vectors. It also requires separate encoding of structured fields to improve matching accuracy. Batch data ingestion must limit per-batch size to avoid exceeding the input limit of the vector model.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | A single business record for refined petroleum product ledgers is approximately 300–800 characters. Chunking must cover complete business units to avoid splitting associated records across stations or fuel types |
| Overlap Length | 100–150 characters | Prevents loss of key binding information such as station codes and fuel types at chunk boundaries. This ensures multiple chunks of the same business record can be associated and retrieved |
| Similarity Threshold | 0.72–0.85 | Ledger data fields are relatively standardized. This range filters low-match irrelevant records while retaining associated results for the same fuel type and time period |
| Single Search Limit | Top 10 entries | Ledger summary and report generation typically covers records from the past 7 to 30 days. A single retrieval returns enough associated data to support complete summarization |
maxToken | 8000 token | Adapts to the default context limit of FastGPT v4.17.0. It meets the context length required for summary report generation across multiple stations and fuel types |
defaultToken | Calibrated via actual testing | Matches the input token limit of the selected vector model. This prevents call failures caused by input length exceeding the model’s upper limit |
Three Common Configuration Mistakes
- Phenomenon: A call error occurs after configuring the vector model, returning a 400 status code or "parameter format error" prompt. Cause: The appropriate vector model was not selected based on the ledger data type. A general multimodal vector model was used to process purely text-based structured ledgers, or the model’s access key and region information were not configured correctly.
- Phenomenon: The retrieval results include a large number of records from irrelevant stations, or sales data for the same fuel type is scattered across multiple unrelated chunks. Cause: The chunk length was set too small. A single complete station sales record was split into multiple independent chunks, causing key binding fields to be split and preventing accurate retrieval of associated data.
- Phenomenon: Some stations’ sales data is missing from the generated summary report. Cause: The update frequency of the incremental index does not match the update cycle of the ledger data. Newly generated transaction data was not synchronized to the vector index library in a timely manner.
How to Verify Correct Configuration
- Upload a single standard structured refined petroleum product ledger document, trigger the vector ingestion process, and check the number of chunks for that document in the index list. Confirm the chunk length and overlap length match the preset configuration.
- Enter a test query such as "June 2024 Chengdong Station No. 95 gasoline revenue". Check the field matching status of the retrieval results, then adjust the similarity threshold to a range that meets business requirements.
- Run a single ledger summary test task, check the context length of the generated report, and confirm that the
maxTokensetting adapts to the content volume of this summary. Ensure there are no context overflow prompts. - Check the vector model’s call logs to confirm there are no timeouts or authentication errors. Confirm that the
defaultTokenconfiguration matches the input limits of the selected vector model.
The values listed above are common starting points, and should be evaluated against the reader’s own sample data.
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.