What Data for This Category Looks Like
Sources of industrial park investment research data include park operation system ledgers, business materials submitted by settled enterprises, official industrial policy platform documents, and property operation logs. Update rhythms vary:
- Investment promotion progress data updates weekly.
- Industrial support policy documents enter the repository as official releases.
- Property operation logs are archived daily.
- Business data from settled enterprises is submitted quarterly.
Document structure falls into two categories:
- Structured tables with fields such as settled entities, rental area, and signing time.
- Unstructured documents such as park planning plans, industrial support rules, and investment promotion brochures.
Fields include clear units, such as ten thousand yuan per mu, square meters, and others.
What Constraints Do These Characteristics Impose on Vector Models and Indexing
Mixed structured and unstructured data structures require the indexing layer to support joint indexing of multimodal text and numerical fields. Pure text-only indexing schemes cannot adapt to semantic matching of numerical fields.
Data sources with multiple update frequencies require parallel configuration of incremental and full indexing updates. Full reindexing would otherwise consume excessive server resources.
Fields with clear units require vector models to retain the binding semantics of units and numerical values during encoding. This prevents incorrect matching of similar fields with different units.
Documents dense with proper nouns require recall rules to adapt to semantic similarity of domain terms. Generic recall models may otherwise lose key information related to professional investment research.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to park documents that include both long policy paragraphs and short structured table fields. Avoids splitting too finely to lose semantic context, or splitting too long to complete vector encoding |
vector_model_name | bge-large-zh-1.5 | Adapts to semantic encoding of specialized industrial terms for parks, and is compatible with common community usage requirements |
index_incremental_batch_size | 50 entries per batch | Adapts to the weekly or daily incremental update rhythm of park data. Avoids excessive server computing resource consumption from overly large single batches |
recall_top_k | Top 10 entries | Covers scenarios in park investment research where multiple policy and enterprise data references are needed. Avoids missing key information from too few recalled entries |
similarity_threshold | 0.72–0.78 | Adapts to semantic similarity matching for specialized park terms, filters low-relevance non-target documents |
parse_file_timeout | 900 seconds | Adapts to the parsing duration of large park planning plan documents, avoids index process interruption from timeout |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: After processing documents locally with
bge-large-zh-1.5and uploading to the server, recall results deviate significantly from local tests, or expected content cannot be matched. Cause: The vector model embedding dimension used on the server does not match that ofbge-large-zh-1.5, or the same text preprocessing rules were not used for encoding. - Symptom: A single large park planning plan document remains in the "indexing" state for a long time, with no progress updates on the interface, and the backend log returns a
408 Request Timeouterror. Cause: Theparse_file_timeoutparameter was not adjusted to adapt to the parsing duration of large documents, causing the parsing process to time out and interrupt. - Symptom: Content from two knowledge bases is recalled during question answering, and matching results from the target knowledge base cannot be prioritized. Cause: The
knowledge_base_priorityparameter was not configured, or the parameter was not bound to the corresponding knowledge base indexing task.
How to Verify Proper Configuration
- Upload a park policy document, check the vector embedding results returned by the model corresponding to the
vector_model_nameparameter, and confirm that the encoding logic is consistent with local tests. - Upload a large park planning document, check whether the indexing progress is completed within the duration set by the
parse_file_timeoutparameter, with no timeout errors. - Configure two test knowledge bases, upload park investment promotion data and industrial policy data respectively, set priorities and initiate question answering, confirm that the target knowledge base content is recalled first.
- Upload structured settled enterprise table data, check whether the index correctly recognizes the semantic meaning of fields with units, with no field confusion.
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.