What the data for this category looks like
Financial report data for the auto service industry comes primarily from monthly operation reports and quarterly financial documents of chain repair shops and 4S store groups. Most files are stored in .xlsx format. The core data update cycle is quarterly, with monthly store operation snapshots released alongside. Documents are mostly multi-sheet workbooks, including modules such as basic store information, revenue details, cost accounting, and after-sales work order statistics. Fields include store number, repair order count, parts procurement cost, main business revenue, net profit, and more. Units include yuan, ten thousand yuan, and order count.
What constraints do these characteristics impose on vector models and indexing
The multi-sheet Excel structure of auto service financial reports requires chunking by worksheet dimension, to avoid mixing data across sheets. The mixed data source of monthly operation snapshots and quarterly financial reports requires the index to support time-based filtering and recall. Numerical fields such as repair order count and revenue make up a large share of the dataset, so vector encoding parameters adapted for structured data must be configured. A single financial report file can reach tens of thousands of rows, and individual sheets can be lengthy. Chunk granularity must be adjusted to fit the vector model’s length limits, while also avoiding oversized single chunks that cause vectorization errors.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CHUNK_SIZE | 800–1200 characters | Adapts to the 1024 token input limit of mainstream vector models, balancing information completeness and chunk granularity for structured financial report data |
CHUNK_OVERLAP_RATE | 10–15 % | Preserves context for time-series fields across chunks in financial reports, avoiding logical breaks in data after chunking |
VECTOR_MODEL_MAX_TOKENS | 1024 | Matches the input length limit of common open-source vector models, preventing vectorization errors |
RECALL_TOP_K | Top 8–12 entries | Covers analysis needs for multiple stores and multiple dimensions in financial reports, ensuring comprehensiveness of recalled data |
UPLOAD_FILE_MAX_SIZE | 100 MB | Adapts to upload requirements for large auto service financial report files, avoiding timeout during single upload |
PARSE_TIMEOUT_SECONDS | 300 seconds | Supports full parsing of large multi-sheet financial report files, preventing mid-process interruptions |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: After uploading a large financial report file, the generated chunks contain mixed data across worksheets, and matching results after vectorization do not align with business logic. Cause: Chunking was not split by worksheet dimension, and chunking was done by global row count instead, leading to logical breaks in data.
- Phenomenon: Vectorization task errors occur, prompting that input tokens exceed the model limit. Cause: Chunk length settings do not match the 1024 token limit of the vector model, and single chunk content exceeds the encoding upper limit.
- Phenomenon: Some chunks fail vectorization, and some exceptions remain unresolved after repeated retries. Cause: Locally deployed vector models experience memory usage peaks during high-concurrency parsing, and no automatic retry mechanism is configured, leading to incomplete recovery after task interruption.
How to confirm proper configuration
- Upload a single quarterly financial report file, check the chunk preview interface, and confirm that each chunk only contains business data from a single worksheet.
- Test the length of chunked text, adjust parameters until the token count after vectorization matches the input limit of the vector model.
- Run a simulated recall test, and confirm that financial report data for the corresponding period can be recalled by filtering with the time dimension.
- Upload a test file larger than 10 MB, check the parsing logs, and confirm that no timeout or vectorization error reports appear.
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.