What the data for this category looks like
Data for paint and ink financing daily reports comes from public industrial and commercial change filings, local financial service platforms, and financing announcements disclosed by industry media. Updates follow a daily schedule, with full updates of financing dynamics from the previous natural day. Each document includes seven core fields: full name of the financing entity, affiliated subcategory (paint/ink/resin additive), financing amount, financing round, investor list, financing completion date, and registered location. The financing amount is measured in RMB ten thousand yuan. The financing round uses a general venture capital classification standard. The investor list uses a multi-value text format.
Constraints on Vector Models and Indexing
The multi-source nature of paint and ink financing daily report data requires vector tools to support parsing multiple file formats: PDF announcements, structured Excel files, and web text. The daily update rhythm requires indexes to support high-frequency incremental refreshes, avoiding performance losses from full index reconstruction. Core fields include numeric financing amounts and multi-value text investor lists, so a mixed index structure must be configured to balance text semantic matching and structured field filtering. Differences between full and shortened names of financing entities require vector models to maintain semantic consistency of entity names during encoding, reducing matching bias.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 50 MB | Financing announcements in the paint and ink industry are mostly compliance disclosure documents; 50 MB covers the size limit for most such files |
PARSE_CHUNK_SIZE | 800–1200 characters | Paint and ink financing daily reports include structured fields and short text descriptions; this chunk length preserves semantic integrity of fields and avoids context breaks from over-splitting |
VECTOR_RECALL_TOP_K | Top 10 results | Core matching need for financing daily reports is precise matching of financing dynamics for specific enterprises or rounds; 10 recall results balance coverage and relevance |
EMBEDDING_MODEL_TYPE | General Chinese text vector model (e.g. bge-large-zh) | Must support semantic encoding of multiple Chinese text fields including enterprise names, financing rounds, and investor names |
INDEX_REFRESH_INTERVAL | 15 minutes | Financing daily reports are updated daily; a 15-minute refresh interval ensures incremental data enters the index within a reasonable timeframe |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Errors
- Phenomenon: The knowledge base creation process gets stuck at the "build vector index" step for more than 30 minutes with no progress. Cause:
INDEX_REFRESH_INTERVALis not configured to match the high-frequency incremental update rhythm of paint and ink financing daily reports, orUPLOAD_FILE_MAX_SIZEis set too small, causing parsing failure of large-format financing announcement files. - Phenomenon: After switching to a custom embedding model, index construction succeeds, but semantic search returns a
500 Internal Server Error. Cause: The vector dimension of the new model does not match the dimension parameters of the existing vector index, or the model loading path is not correctly configured. - Phenomenon: When querying the vector database, only vector data is returned, and original financing daily report document content cannot be retrieved. Cause: The
SAVE_ORIGIN_DOCconfiguration item is not enabled, or the association mapping between the vector database and original document storage is not correctly configured.
How to Confirm Proper Configuration
- Upload a single typical financing announcement document, check if parsed chunks retain semantic integrity of core fields, and verify that the actual chunk configuration effect matches expectations.
- Run an incremental data import test, review index refresh logs, and confirm that incremental data completes index updates within the configured refresh interval.
- Run a semantic search test, verify that recall results include financing dynamics for target enterprises, and check that the number of recall results aligns with business requirements.
- View the association fields between the vector database and original document storage, confirm that original document content can be retrieved using the vector ID.
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.