What the data for this category looks like
Data for medical device financing daily reports is sourced from public medical industry financing disclosure platforms, regulatory public information, and industry association open data. The update cadence is daily. Each document is a mixed record of structured and unstructured content, including fields such as 企业名称, 融资轮次, 融资金额, 币种, 投资方名单, 融资公告日期, 所属医疗器械细分赛道, 核心产品品类. Some documents include text content such as company profiles or product descriptions.
Constraints for vector models and indexing
Daily incremental data requires indexes to support high-frequency incremental refreshes. This avoids excessive system resource usage from full index rebuilds. The mixed structured and unstructured document structure requires vectorization input to cover both semantic conversion of numeric fields and semantic extraction of text fields. There are many medical device sub-sectors. Field filtering is required to quickly narrow the recall scope and reduce invalid vector calculations. The text length of individual documents is moderate. Appropriate segmentation parameters must be configured to fully retain semantic information, avoiding over-splitting or under-splitting.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | doubao-embedding-text-zh-1024 or m3e-base | Adapts to the semantic understanding of Chinese industry text for medical device financing daily reports, and the supported input length covers the complete content of a single record |
chunk_size | 800-1200 characters | The text length of most individual financing daily reports falls between 500-1000 characters. This range fully retains the semantic information of a single record and avoids over-splitting |
index_refresh_interval | 1 hour | Financing daily reports are incremental data updated daily. A 1-hour refresh balances index real-time performance and system resource usage |
similarity_threshold | 0.75-0.85 | Semantic differences between financing events in the same track must be distinguished. This threshold filters low-correlation recall results |
recall_top_k | Top 10 entries | The number of financing events in a single daily report is limited. Too many recalls increase subsequent reranking overhead, while too few may miss relevant results |
filter_field_list | ["Financing Track","Financing Round"] | The core retrieval dimensions for medical device financing are track and round. Field filtering can narrow the recall scope in advance |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing values.
Three common misconfigurations
- Phenomenon: The similarity scores of vector recall results exceed reasonable ranges (such as reaching 10000+), and valid results cannot be filtered through conventional filtering rules. Cause: The corresponding similarity threshold for the currently used embedding model is not configured. The score normalization logic varies between different models, and no adaptation adjustments have been made.
- Phenomenon: Some medical device financing daily report documents fail to generate index records after upload, and the system prompts the
EMPTY_EMBEDDING_INPUTerror. Cause: The document only contains structured numeric fields, and the fields are not concatenated into vectorizable text content, resulting in no valid input during the vectorization phase. - Phenomenon: The
m3e-baseoption cannot be found in the model management interface, and this embedding model cannot be selected. Cause: The custom model channel configuration has not been completed according to the official documentation, and the available access channel for m3e has not been added to the system.
How to confirm correct configuration
- Upload a single standard medical device financing daily report document, check the vector generation logs, and confirm that the embedding model has successfully generated vectors with no errors.
- Initiate a retrieval request, check the similarity score distribution of recall results, and confirm that low-correlation results have been filtered according to the currently configured threshold.
- Add a new incremental financing daily report data, wait for the index to refresh, then retrieve the keywords of this data, and confirm that the incremental index has taken effect.
- View the field list in the index management interface, and confirm that the configured filter fields have been correctly loaded and can be used for retrieval filtering.
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.