What the Data for This Category Looks Like
Data sources include local housing and urban-rural development department building material filing systems, enterprise operation ledgers from industry associations, and credit approval ledgers from cooperative financial institutions. Updates run daily, with financing summary data for the previous day generated the same day. Each document includes fields such as project filing number, building material category name, financing subject unified social credit code, credit limit (unit: ten thousand yuan), approval date, financing term (unit: month), and fund usage. Some documents also include attached filing photos of the project site.
Constraints Imposed on Vector Models and Indexing
Daily high-frequency updates require the index to support incremental updates, avoiding performance losses caused by full reconstruction. Multi-field structured data requires vector models to adapt to multimodality, covering text and a small number of image attachments, while aligning the semantic weights of each field. Numeric fields such as credit limit and financing term must be vectorized together with text fields to prevent structured information from being lost in a single vector. A single document contains a large number of fields, so segmentation granularity must be controlled to avoid semantic dilution from long text vectors. Daily batch data imports will also create index write pressure, so appropriate batch processing parameters must be configured.
Recommended Configuration Parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_normalization | Enabled | This configuration was introduced in version 4.8.23. The consumer construction materials financing daily report includes mixed text and numeric fields. Some embedding models do not perform normalization. Enabling this setting enables vector alignment. |
chunk_size | 800-1000 characters | A single document has many fields. Excessively long segmentation leads to semantic dilution, while excessively short segmentation destroys the contextual association of structured fields. |
vector_index_batch_size | 32-64 items per batch | Daily batch data import volume is large. This parameter adapts to the disk I/O pressure of index writing and avoids write blocking. |
recall_top_k | Top 10-15 results | The associated information of financing daily reports is concentrated in a small number of related projects. Excessive recall introduces irrelevant data and reduces retrieval accuracy. |
max_context_token | 8000-12000 | The total field content of a single document is large. This setting ensures that the recalled context is complete and covers core financing information. |
enable_incremental_index | Enabled | Daily incremental data updates avoid time losses from full reconstruction and adapt to the daily update business rhythm. |
The parameter values provided on this page are all conventional recommendations used to determine a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: After switching the knowledge base vector model, the background synchronization progress remains unchanged for a long time, and the original model cannot be reselected in the interface. Cause: The vector cache cleanup logic is not configured correctly. Conflicts occur between the vector indexes of the old and new models, causing task blocking.
- Phenomenon: The number of context tokens passed in a single knowledge base query exceeds business expectations, triggering interface timeout or current-limiting errors. Cause: The
max_context_tokenparameter is not restricted, and unsegmented long documents are directly passed to the retrieval link. - Phenomenon: The filing image attachments in the documents cannot be indexed correctly, and the retrieval results do not include financing information associated with the images. Cause: Multimodal vector support is not enabled, or an embedding model adapted for images is not used.
How to Confirm the Configuration Is Correct
- Enter the vector model configuration page of the knowledge base, confirm that the currently selected embedding model matches the settings in the configuration items, and that the status of
embedding_normalizationmeets the preset requirements. - Upload a test consumer construction materials financing daily report document, check the progress bar of the background indexing task, and confirm that the task is completed within a reasonable time frame.
- Initiate a knowledge base retrieval, enter a test keyword, and check the recalled context content to confirm that no excessively long text fragments beyond expectations appear.
- Check the indexing logs to confirm that the incremental update task only processes newly added documents for the day and does not trigger a full reconstruction process.
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.