What this category’s data looks like
Baijiu financing daily report data primarily comes from listed company announcements, publicly disclosed financing information from industry associations, and third-party public financial data platforms. Updates occur on a daily basis, covering financing updates for baijiu enterprises disclosed on the same day and in the recent period. Each document includes fields such as full enterprise name, financing round, financing amount, investor list, financing disclosure date, sub-category track of the baijiu sector, and information disclosure source. Financing amount is measured in RMB ten thousand or hundred million yuan. Date fields use the YYYY-MM-DD standard format.
Constraints on vector models and indexing
The high daily update frequency requires indexes to support incremental synchronization. Full reindexing must be avoided to prevent duplicate computing resource consumption. The multi-field structure requires distinguishing between structured numeric fields such as financing amount and unstructured text fields such as investor descriptions, using a hybrid vector indexing strategy. The sub-category track field has strong semantic relevance. Separate vector encoding weights must be configured for this field to improve the accuracy of track-related searches. Single documents are short, but batch import volumes are large. Adjust the index shard size to balance query efficiency and write performance.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | text-embedding-3-large | Supports long-text semantic encoding, meets the semantic association needs of multiple fields such as baijiu track and investor descriptions |
chunk_size | 800–1200 characters | Adapts to the field concatenation length of a single financing daily report document, avoiding information loss caused by semantic chunking |
index_batch_size | 500 documents/batch | Matches the daily updated financing data scale, balances resource usage and efficiency for index writes |
recall_top_k | Top 10 results | Controls the number of candidate results returned per query, adapts to the narrow-range retrieval needs of financing daily reports |
similarity_threshold | 0.75–0.85 | Filters low-relevance search results, prevents financing entries from non-baijiu tracks from appearing in search results |
enable_incremental_index | Enabled | Adapts to the high daily update frequency, reduces resource consumption from full index re-building |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- After importing a dataset, the interface continuously displays "Indexing" and does not complete beyond the preset timeout period. The cause is that incremental indexing is not enabled. Full index re-building cannot adapt to the daily high-update scale of baijiu financing data, triggering a write timeout.
- The similarity values returned in search results exceed 10000, falling outside the standard 0-1 range. The cause is that no normalization is applied to the vector model’s output results, and no filtering range is configured for the similarity threshold. Non-standardized scores are directly used for retrieval matching.
- After multi-replica deployment, some query requests return 504 timeout errors. The cause is that no cross-replica load balancing strategy is configured for the index. Index shards on some nodes become overloaded, unable to respond to query requests in a timely manner.
How to confirm the configuration is correct
- Check the vector encoding run logs. Confirm there are no error records for field encoding failures, and the encoding process for each data entry completes normally.
- Run a custom retrieval test. Input a query term that includes a baijiu sector track and financing amount. Verify that the field matching degree of the returned results meets business expectations.
- Observe the index status of the dataset. Confirm that newly submitted data can complete index updates within the time allowed by the business, with no continuous hanging status.
- Check the load monitoring for multi-replica deployment. Confirm that the request distribution for index shards is even, with no abnormal single-node overload performance.
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.