Vector Models and Indexing for Traditional Chinese Medicine Financing Daily Reports

The data for this category comes from publicly compliant industry and capital market disclosure channels, with an update frequency of synchronizing

What the data for this category looks like

The data for this category comes from publicly compliant industry and capital market disclosure channels, with an update frequency of synchronizing public financing information from the previous calendar day daily. Documents are presented in structured field combinations, including core fields such as full enterprise name, traditional Chinese medicine business track classification, financing amount, financing round, investor entity, financing completion date, brief project description, disclosure traceability link, etc. The financing amount is denominated in RMB, and the field hierarchy is clear with no complex nested structures.

The public disclosure attribute leads to format differences in some fields, for example, the unit expression of financing amounts is inconsistent. Standardized mapping must be completed before vectorization to avoid feature confusion. The daily incremental update feature requires the indexing link to support incremental synchronization, avoiding computing resource consumption caused by full reconstruction. The document structure that coexists with structured fields and unstructured project descriptions requires adapting to both structured feature encoding and natural language vector generation, and a hybrid indexing strategy must be configured. In addition, the existence of the disclosure traceability field requires retaining metadata associations in the index to ensure that recall results can trace compliant sources.

Configuration settings

Configuration ItemRecommended ValueRationale
embedding_batch_size8-16The length of a single financing document is moderate, and a small batch configuration can reduce the risk of rate limit exceeded when calling the vector model
index_incremental_modeEnabledData is updated incrementally daily, and incremental indexing reduces repeated calculations and improves synchronization efficiency
retrieve_top_kTop 10-15 entriesDecision-making reference for financing information requires a small number of accurate projects in the same track, avoiding too many redundant results
embedding_rate_limitCalibrated according to third-party platform API quotasAvoid exceeding the call limit of the third-party vector model and prevent task interruption
chunk_size300-500 charactersAdapt to the semantic length of a single financing document, and segmenting can retain complete project description information
metadata_index_enableEnabledRetain metadata such as disclosure traceability to ensure that recall results can trace compliant sources

The parameter values given on this page are conventional recommendations used to determine the starting point 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 configuration mistakes

  • Phenomenon: Vectorization tasks return a 429 status code, or logs prompt embedding rate limit exceeded. Cause: A reasonable embedding_batch_size and embedding_rate_limit are not configured, causing batch requests to exceed the call limit of the third-party vector model.
  • Phenomenon: Knowledge base retrieval response delay is significant, and recall result generation takes a long time. Cause: Incremental indexing mode is not enabled, and each retrieval triggers full vector database matching, consuming a large amount of computing resources.
  • Phenomenon: The same financing project in the dataset generates multiple independent index entries. Cause: Deduplication rules are not configured based on unique identifiers (such as full enterprise name + financing completion date), resulting in repeated data import during incremental synchronization.

How to confirm the configuration is correct

  • View the vector model call logs to confirm that the configured embedding_batch_size and embedding_rate_limit do not trigger errors related to call limits.
  • Perform a single incremental synchronization operation to verify that only newly added financing data is included in the index, with no duplicate index entries generated for historical data.
  • Perform a retrieval test to confirm that the recall results include preset metadata fields such as disclosure traceability.
  • Check the execution records of indexing tasks to confirm that incremental synchronization tasks can be completed normally and switch to a ready state.

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.