What the Data Looks Like
Industrial park research reports are core reference materials for financial institutions conducting industrial investment analysis and risk assessment. Data sources include operation archives publicly released by park management committees, industry research white papers, investment promotion public notices, annual operation reports, and similar documents. Updates occur irregularly, aligned with park operation dynamics and annual plan adjustments. Core operation data is updated quarterly or annually. Most documents mix structured and semi-structured content, with fields including location parameters, industrial cluster classifications, settled entity qualifications, land use indicators, and more. Units include square meters, ten thousand yuan, mu, and number of employees. Some documents also include associated policy rules and investment guidance.
Constraints on Vector Models and Indexing
Vector models must adapt to both text semantics and structured numerical feature encoding. Mixed document structures and multi-type fields in industrial park research reports risk losing numerical dimension information if only pure text vectors are used. Indexing systems must support incremental updates. Irregular update cycles and periodic core operation data iterations make full reindexing too costly. Indexes must support joint recall of multi-dimensional features. Multi-field joint retrieval is a core business requirement. Single global semantic vector matching will miss relevant results. Chunking must retain contextual associations for closely linked fields like location, industry, and land use. Splitting documents without preserving context will break underlying business logic.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk length | 800–1200 characters | The business units of industrial park research reports are mostly coherent policy clauses or single-type operation data. This length preserves complete business context and avoids losing associated logic after splitting |
recall count | 15–20 entries | The retrieval needs for park research reports mostly focus on accurately matching specific industrial or land use information. This range covers relevant content while reducing redundancy |
similarity threshold | 0.72–0.80 | Filters low-relevance generic text, retaining retrieval results that highly match the park's industrial positioning and land use indicators |
reranked return count | 5–8 entries | The final output should focus on core business information, avoiding excessive entries that interfere with the readability of retrieval results |
EMBEDDING_MODEL | bge-large-zh-v1.5 or equivalent multi-feature adaptive model | Industrial park research reports contain both text semantics and structured numerical fields. This model can encode both types of features, improving retrieval accuracy |
ENABLE_INCREMENTAL_INDEX | Enabled | Updates to park research reports are mostly incremental operation data supplements. Incremental indexing reduces update overhead and adapts to irregular content iterations |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: The matching relevance of structured numerical fields (such as mu-level output value, land area) in retrieval results is low, or they are not recalled at all. Cause: A vector model that only supports pure text semantic encoding was selected, which does not adapt to the mixed data characteristics of industrial park research reports, resulting in loss of associated information in the numerical dimension.
- Phenomenon: A
413 Request Entity Too Largeerror is triggered when batch uploading industrial park research reports, or index update times out. Cause: TheUPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDSparameters were not adjusted. Single upload file size is too large, or processing timeout is not allowed. - Phenomenon: The set recall count does not match the actual number of returned retrieval results, resulting in missing or redundant results. Cause: The multi-field joint recall logic was not configured correctly, and only global vector recall was used, causing some eligible documents to not be properly indexed.
How to Verify Correct Configuration
- Upload a sample industrial park research report containing structured numerical fields. Check the vector generation log to confirm whether both text and numerical features are encoded.
- Initiate a retrieval targeting the park's industrial positioning. Verify whether the returned result fields cover the core business information in the research report.
- Upload incrementally updated research report content. Check whether the indexing system only processes new files and does not trigger full reindexing.
- Adjust configuration parameters. Compare retrieval results before and after the change to confirm that parameter modifications have the expected impact on recall logic.
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.