What the Data for This Category Looks Like
Photovoltaic financing daily report data is sourced from local energy regulatory authority photovoltaic project filing ledgers, credit issuance records from banks and financial leasing companies, and public financing information from third-party photovoltaic industry data platforms. Updates occur daily. Each entry corresponds to the daily financing updates of a single photovoltaic project. The document uses a standardized table structure, including fields such as project name, filing number, financing amount (unit: ten thousand yuan), financing party, fund provider, transaction date, project installed capacity (unit: MW), location, and financing purpose. No redundant nested content is included.
Constraints Imposed by These Characteristics on Vector Models and Indexing
The daily update requirement means the index must support incremental synchronization, to avoid resource consumption and delays caused by full reconstruction. The structured table has clear fields with mixed numeric and text types, so the vector model must adapt to semantic representation of Chinese financial terminology and numeric fields, while retaining the mapping relationship between fields and original data. The small per-entry data volume but large batch volume means the index sharding strategy must adapt to high-frequency incremental writes, while controlling the recall range to avoid irrelevant information interference. Additionally, the traceability requirement for financing daily reports means the vector index must bind the unique identifier and field information of the original document, to facilitate associating sources during subsequent question answering.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Doubao-embedding-v2 | Adapts to text semantics in Chinese photovoltaic and financial fields, with stable representation effects for project names and financing terminology |
recall_top_k | Top 10-15 entries | Photovoltaic financing daily reports have clear fields. Excessive recall will introduce irrelevant matches, while insufficient recall will fail to cover relevant financing updates |
similarity_threshold | 0.75-0.85 | Structured data has high field matching accuracy. This range balances the risks of false matches and missed matches |
index_refresh_interval | 2:00 AM daily | Matches the daily update rhythm of financing daily reports. Completes incremental index refresh after that day's data is stored, avoiding resource occupation from real-time refresh |
enable_document_source | Enabled | Meets the traceability requirement for financing daily reports, allowing association of original source data for matching entries in question answering results |
structured_table_parse_mode | Split by field for vectorization | Adapts to structured table data, retains precise semantics of each financing entry, and avoids semantic loss caused by full-text vectorization |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- A 400 Bad Request error is returned when calling the question answering interface, with the prompt "embedding model not supported". The cause is failure to specify a deployed vector model in the configuration, or the selected
embedding_modelis not in the platform's available list. - Question answering results do not include source document information for matching data. The cause is failure to enable the
enable_document_sourceconfiguration item, or failure to bind the original document's unique identifier and field mapping when building the vector index. - Newly entered photovoltaic financing data is not recalled after incremental index updates. The cause is an excessively long
index_refresh_intervalconfiguration, failure to trigger index refresh after that day's data is stored, or incorrect configuration of the incremental index synchronization path.
How to Verify Proper Configuration
- Access the FastGPT knowledge base configuration page, check the value of the vector model binding item, and confirm it matches the preset configuration.
- Upload a test photovoltaic financing daily report data entry, run the vector indexing task, and check that the task log has no errors and the status shows completed.
- Initiate a test query, enter keywords related to the test data, and confirm that the number of recalled results in the returned output matches the configured recall range.
- Check the additional information in the question answering results, confirm whether it includes the source document fields of the matching data, and verify that the traceability function works correctly.
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.