What the data for this category looks like
Commercial property financing daily report data is collected from the daily lease ledgers of property operation management systems, bank financing connection records, and regional commercial lease monitoring data. It is generated daily, with full data collection completed on the same day. Documents are presented as structured tables. Each row corresponds to a single commercial property project, and includes fields such as project code, total lease area, total actual rent received, vacancy rate, intended financing amount, name of connected financial institutions, and more. Units are standardized to square meters, yuan, ten thousand yuan, and other standard units. There is no redundant nested long text content.
What constraints these characteristics impose on the vector models and indexing link
The structured table characteristics of commercial property financing daily reports require vector models to adapt to encoding of structured fields and numerical features, to avoid semantic deviation. Indexes must support precise filtering and recall based on fields such as project code and report date. The daily full data collection cadence requires index write throughput to match the daily data increment scale, and the timed refresh strategy must align with the daily report update cycle. Each daily report entry has fixed business fields, so long text splitting is not required. However, vector feature differentiation must be ensured between different projects for the same field, to avoid recall confusion.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | The total length of single-project fields in commercial property financing daily reports is moderate. This range can fully embed core business information for a single project, avoiding splitting that destroys field integrity |
vector_store_batch_size | 50–100 items/batch | Matches the number of projects in a single daily report, avoiding too many items written in a single batch that causes index timeout |
embedding_model_dim | 1024 | Adapts to the embedding dimension requirements of bge-large-zh-1.5, ensuring unified vector dimensions for text and numerical fields |
recall_top_k | Top 10 entries | Balances the demand for comparison of regional and similar properties in financing business and query efficiency, covering the core reference range |
index_refresh_interval | 86400 seconds | Aligns with the update cycle of daily financing reports, ensuring index data is synchronized with source data |
UPLOAD_FILE_MAX_SIZE | 200 MB | Matches the conventional scale of a single structured daily report file, avoiding upload timeout |
The parameter values provided on this page are all common recommendations used as starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Symptom: Index tasks remain in the "indexing" state with no progress. Cause: The
vector_store_batch_sizeparameter is not adjusted. Too many items written in a single batch causes index write timeout, blocking the task. - Symptom: Uploaded local documents are incompatible with the server-side vector model, and a large number of irrelevant content appears in recall results. Cause: The
embedding_model_dimparameter is not aligned. Inconsistent embedding dimensions used locally and on the server lead to vector space mismatch. - Symptom: A single financing daily report document only generates one set of vectors instead of multiple sets. Cause: The
chunk_overlapparameter is not configured correctly, and the multi-vector embedding switch for structured fields is not enabled. Vectors cannot be generated separately for multiple fields of a single project.
How to Confirm the Configuration Is Correct
- Check the vector model embedding dimension parameter, confirm it matches the output dimension of the currently used model.
- Run a vector generation test for a single daily report document, verify that the number of generated vectors matches the configured splitting rules.
- Trigger a full index task, verify that the task completion time aligns with the configured refresh interval.
- Run a filter recall test based on project code, confirm that recall results only include documents with matching fields.
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.