What the data for this category looks like
Data sources for construction financing daily reports include daily fund submission ledgers from project construction parties, project fund supervision platforms of local housing and urban-rural development departments, and loan flow records from cooperative banks. Update frequency is daily generation of same-day entries. Documents are presented as structured tables paired with a small number of supplementary notes. Fields include project number, project name, construction scale, total investment amount, daily new financing amount, cumulative financed amount, financing subject, lending institution, fund usage, and disbursement time. Units involved include ten thousand yuan, square meters, and working days. The total length of individual documents varies widely. It is recommended to confirm based on statistics or actual measurements of samples collected for the specific deployment.
How these data characteristics impose constraints on vector models and indexing
The mixed structured and unstructured document structure requires vector models to adapt to both semantic encoding of structured fields and feature extraction of unstructured notes. The daily update rhythm requires the indexing system to support incremental writing and incremental index construction, to avoid performance losses caused by full reconstruction. The combination of multiple types of fields with units requires vector models to adapt to the association between numerical values and text across different semantic domains, while the index must support rapid filtering based on metadata such as project number and date. The close association between fields in individual documents requires the recall phase to prioritize semantic matching between fields, to cover potential associations between fields.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
embedding_model | bge-m3 (deployed via Ollama) or 混元Vector Model | Adapts to encoding of multi-semantic-domain fields, supports feature extraction for mixed structured and unstructured text, and matches the field characteristics of construction financing daily reports |
chunk_size | 800-1200 characters | Each individual construction financing daily report document contains multiple fields and a small number of notes. This range can fully retain field semantics and note information, avoiding semantic fragmentation |
retrieval_top_k | Top 8-12 entries | The fields in a single daily report document are closely associated. A sufficient number of related entries must be recalled to cover potential associations, while avoiding redundant results |
similarity_threshold | 0.75-0.85 | Semantic matching for financing data requires high precision. This threshold can filter out low-correlation non-project financing entries |
index_type | HNSW | Supports high-frequency incremental updates, adapts to daily new financing daily report entries, and ensures retrieval speed |
enable_metadata_filter | Enabled | Construction financing daily reports require rapid filtering based on metadata such as project number and date. This configuration combines vector recall and metadata filtering to improve retrieval accuracy |
The parameter values provided on this page are all 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 conduct actual tests on samples collected for the specific use case before finalizing the settings.
Three common mistakes
- Symptom: The interface displays the
embedding connection failederror, and the vector recall result is empty. Cause: The access port and address of the bge-m3 model deployed via Ollama are not configured correctly, or the channel information for the corresponding vector model is not added in FastGPT. - Symptom: The index construction task shows a
504 Gateway Timeoutstatus, and cannot complete data import. Cause: Incremental index mode is not configured, and full index reconstruction is performed on all historical financing daily reports, exceeding the system's default timeout threshold. - Symptom: The retrieval results contain a large number of non-target project financing entries with low field matching degrees. Cause: The
similarity_thresholdparameter is not set, or the threshold is set too low, failing to filter out low-correlation non-project data.
How to confirm the configuration is complete
- Enter the Vector Model Management page in FastGPT, check the status of the configured
embedding_model, and confirm that it displays "Connected". - Upload a single construction financing daily report test document, trigger the vector import process, and check that the index task log has no error messages.
- Initiate a retrieval request based on the project name or financing amount, verify the matching degree between the metadata fields of the returned results and the retrieval keywords, and adjust related parameters to meet business requirements.
- Simulate the daily incremental update scenario, upload new test daily report entries, and confirm that the indexing system can automatically perform incremental writing without performing full reconstruction.
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.