What this use case's data looks like
Data sources for industrial park financial report analysis are monthly operation reports, annual audit financial statements, and investment promotion archives exported from park operation management systems. Update cycles fall into two categories: monthly operation data is updated monthly, and annual financial statements are updated per fiscal year after third-party audit.
Document structure primarily consists of structured tables, with fields including rental area, unit rent, property fee collection rate, number of settled enterprises, tax amount, and more. Units are mostly square meters, yuan/square meter·month, ten thousand yuan, number of enterprises, etc. Some documents include attached detailed files for settled enterprises, primarily in Excel, PDF, or CSV formats.
Constraints on vector models and indexing
First, most data is structured tables with multiple fields and unit differences. Vector models must support structured semantic encoding, or fields must be pre-aggregated by business semantics to avoid unit differences interfering with vector matching accuracy. Second, update cycles are split into monthly and annual categories. Incremental update indexes must support targeted refreshes based on update timestamps to avoid computing resource consumption from full reindexing. Third, some documents include nested settled enterprise details. Support for vector splitting and associated indexing of multi-level documents is required to avoid confusion between detailed data and overall park business data. Fourth, document formats include non-pure text table files. Structured parsing must be completed before vectorization, otherwise invalid unstructured text encoding will occur.
Configuration settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | Prioritize models that support structured encoding, or text-embedding-v3 | Industrial park financial reports include multi-field structured data. Models that support structured encoding can retain semantic associations between fields and avoid matching deviations caused by unit differences |
chunk_size | 800–1200 characters | Financial report documents are mostly long table texts. This segment length can fully retain the semantic integrity of a single business module and avoid breaking data associations during splitting |
index_refresh_mode | Incremental refresh (by update timestamp) | Monthly operation data and annual financial statements have different update cycles. Incremental refresh reduces resource usage of full indexes and only updates changed data |
retrieve_top_k | Top 8–12 results | Financial report analysis requires coverage of multi-dimensional business data. This number of retrieved results balances retrieval accuracy and computing overhead |
similarity_threshold | 0.75–0.85 | Semantic matching for structured fields requires a relatively high threshold to avoid low-relevance data being retrieved and interfering with analysis results |
nested_chunk_enable | Enabled | Some documents include attached settled enterprise detail files. Enabling nested segmentation enables associated indexing of detailed data and overall park data |
The parameter values provided on this page are general recommendations for starting point configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: When configuring
embedding_modelas a model other thanada-002, theundefined model must match "^(texterror occurs. Cause: The interface key and whitelist configuration for the corresponding vector model have not been completed in the system settings. - Symptom: Similarity scores of retrieval results are generally higher than 0.9, but matching results are irrelevant to financial report analysis requirements. Cause: Semantic aggregation of structured field values and units has not been performed, and the vector model directly encodes raw text, leading to semantic matching deviations.
- Symptom: When a single park financial report includes multiple sets of detailed data, the generated vectors cannot be associated with the original park entity. Cause: The
nested_chunk_enableconfiguration is not enabled, and no associated index identifier is added for nested detailed data.
How to confirm configurations are correctly set
- Navigate to the vector model configuration page, confirm that the model matching business requirements has been selected, and verify interface connectivity.
- Upload a single park financial report document, view the segment preview results, and confirm that the segment length meets expectations and does not damage business module integrity.
- Run a retrieval test, adjust the
similarity_thresholdvalue, and verify that the retrieved results match analysis requirements. - View the index refresh log to confirm that incremental updates only target changed data and no full reindexing is triggered.
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.