What the data for this category looks like
Railway and highway industry financial report data comes primarily from publicly disclosed periodic reports of listed companies and public statistical materials from industry associations. Disclosure follows a fixed schedule: annual reports are updated once per year, quarterly reports once per quarter, and monthly operational data is updated monthly. Documents include standardized financial statement modules and core operational indicator modules. Fields include operating mileage, passenger-kilometers traveled, freight-ton kilometers traveled, net profit, and more. Corresponding units are kilometers, hundred million passenger-kilometers, hundred million ton-kilometers, ten thousand yuan, and other matching units.
What constraints do these characteristics impose on vector models and indexing?
The long document structure and specialized indicator traits of this category create multiple constraints for the vector models and indexing workflow. Individual financial report documents have significant length, so chunk length must be controlled properly to avoid exceeding the model's context window. A large volume of specialized indicators with clear units requires vector models to accurately capture the semantic link between indicators and their units, preventing confusion between similar indicators with different units. The fixed periodic disclosure schedule requires the indexing workflow to support incremental updates, reducing resource costs from full reindexing. The mixed multi-module document structure requires presetting weights for core indicator paragraphs during indexing, to improve retrieval precision.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
embedding_model | text-embedding-3-large | This model has stronger ability to capture specialized terminology and unit semantics, and is suitable for railway and highway professional indicator fields in financial reports |
chunk_size | 800–1200 characters | A single financial report contains multi-module content, this chunk length balances context completeness and indexing granularity |
chunk_overlap | 100–150 characters | Prevents specialized indicators with units from being truncated during chunking, and preserves the association between indicators and their context |
recall_top_k | Top 8–12 results | Core financial report indicators are scattered across different paragraphs, so a sufficient number of relevant fragments must be retrieved to cover complete analysis dimensions |
similarity_threshold | 0.75–0.85 | Filters low-relevance non-indicator paragraphs to improve retrieval accuracy |
index_incremental_update | Enabled | Financial reports are updated on a fixed schedule, incremental indexing reduces resource and time consumption from full reindexing |
The parameter values provided on this page are common recommended 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 settings.
Three common mistakes
- Issue: When calling the knowledge base index creation interface, selecting a model other than
text-embedding-ada-002triggers theundefined model must match "^(texterror. Cause: Permission configuration for this model has not been completed on the platform, or the model name input does not match the allowed format rules of the platform. - Issue: After changing the
embedding_modelconfiguration, the retrieval results returned by the imported knowledge base differ significantly from those before the adjustment. Cause: The vector index for the imported documents has not been regenerated. Old vectors are still generated based on the vector space of the original model, and are inconsistent with the semantic mapping logic of the new model. - Issue: The sort order of reference fragments returned by knowledge base search does not reflect similarity priority. Cause: The re-ranking step after vector retrieval is not enabled, or the
recall_top_kvalue exceeds the default re-ranking range of the platform, resulting in retrieved fragments not being properly sorted.
How to confirm the configuration is complete
- Log in to the platform configuration page, confirm that the value of the
embedding_modelfield matches the preset configuration. - Upload a single test railway and highway financial report document, view the chunk details after generating the index, and confirm that the chunk length meets the configured range requirements.
- Initiate a knowledge base search request, check that the number of returned retrieved fragments matches the preset value of
recall_top_k. - Submit an incremental indexing task, check the platform logs, and confirm that only newly imported documents have generated new vector indexes.
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.