What the data for this category looks like
Data is sourced from three main channels: public investment and financing disclosure platforms, education industry regulatory announcement sites, and daily financing reports submitted by partners. A full refresh of the previous day’s data is completed every early morning. Documents are split into two sections: structured fields and unstructured notes. Structured fields include the full name of the financing entity, affiliated education service sub-sector, financing amount, financing round, disclosure date, and list of investors. The unstructured section includes financing detail descriptions and exclusive clause summaries. Financing amounts are marked in ten thousand RMB units. Financing rounds use industry standard abbreviations such as Pre-A, Series B. Disclosure dates follow the YYYY-MM-DD format.
Constraints imposed by these characteristics on vector models and indexing
The daily full data update requirement means indexes must support high-frequency incremental synchronization or scheduled rebuilding. This avoids excessive full index build time that disrupts query response. The mixed structured and unstructured document structure requires generating vectors for both field-level structured data and text-level unstructured data. This requires compatible multi-modal vector generation configuration logic. The finely granular education service sub-sector classification, with distinct financing characteristics across tracks, requires dedicated vector calibration rules for the track field. This prevents false cross-track vector similarity matches. The standardized numeric financing amount field requires numerical normalization processing. This ensures balanced vector weighting between numeric features and text features.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
EMBEDDING_BATCH_SIZE | 32–64 items/batch | The text length of single entries in the education service financing daily report is moderate. An overly large batch will trigger model input length limits, while an overly small batch will reduce index building efficiency |
INDEX_INCREMENTAL_SYNC_INTERVAL | 86400 seconds | The data update frequency is once per day. Matching the sync interval to the data update cycle prevents duplicate indexing or delays |
PARSE_SEGMENT_MAX_LENGTH | 800–1200 characters | Most unstructured note entries have text lengths between 500 and 1000 characters. This segment length range covers most single notes, avoiding semantic fragmentation from excessive splitting |
STRUCTURED_FIELD_VECTOR_WEIGHT | 0.3–0.5 | Structured fields such as sub-sector and round carry higher weight for semantic matching in financing daily reports. This range balances vector contributions from the two feature types |
RECALL_TOP_K | 10–15 entries | Most financing daily report queries require multiple relevant recent entries. This recall count range covers most business query needs |
IMAGE_INDEX_ENABLED | false | Financing daily report data consists primarily of text, with no unstructured image content. Enabling image indexing will add unnecessary resource consumption |
The parameter values provided on this page are common starting points for configuration. Actual values should be evaluated against material formats, data volume, business rules, and tested on local samples before finalization.
Three common configuration mistakes
- When creating a new knowledge base on a locally deployed v4.9.0 version, no image index related configuration options are visible, making it impossible to enable the image index function. The root cause is that the open source version does not compile the image index dependency module by default. The function can only be enabled after manually adding the corresponding compilation parameters.
- When calling the OneAPI embedding model, a
500 Internal Server Erroris returned, and logs show model connection timeout. The root cause is that the local deployment did not configure the correct address forONEAPI_API_BASE, or network policies restrict outbound access to the model call port. - After index construction is complete, recall results for the financing track field do not match expectations, with high similarity matches across tracks appearing. The root cause is that no dedicated vector weight was configured for the structured track field, leading to text feature weights overriding semantic differences between track categories.
How to confirm configurations are correctly set
- Navigate to the knowledge base management page, check that the parameter values for index configuration items match the preset configurations, and confirm that the parameter values meet business requirements.
- Upload a test entry of education service financing daily report data, wait for index construction to complete, run a keyword query, and verify that recall results for fields such as track and amount match the query conditions.
- Check system logs to confirm that daily index synchronization tasks have executed successfully, with no timeout or error messages.
- Review structured field vector generation logs to confirm that vector generation for fields such as track and amount has no abnormal errors.
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.