What This Category's Data Looks Like
Data originates primarily from regional economic analysis reports and peer benchmarking research reports from internal risk control and strategy departments of urban commercial banks, plus regional financial operation briefings released by local financial regulatory authorities. Two update cycles apply: monthly regular industry benchmarking reports, and ad-hoc research reports released alongside changes to regulatory policies or regional economic conditions. Most documents use PDF or Word format, and include report numbers, release dates, region coverage fields, core metrics such as deposit balances and non-performing loan ratios, with units of 100 million yuan and percentage points. Some documents include structured tables of regional credit allocation details.
Constraints for Vector Models and Indexing Workflows
The following constraints apply to the vector model and indexing workflow based on data characteristics:
- Region-specific fields require vector index partitioning by geographic dimension. This narrows recall scope and avoids interference from cross-region irrelevant data.
- Ad-hoc updates for temporary research reports require index support for incremental synchronization. This avoids high resource consumption from full index rebuilding.
- Structured core metric fields must be extracted separately as metadata. This enables precise filtering alongside vector recall.
- Parsing differences across multiple document formats require vector models to support mixed-format text embedding. This avoids embedding bias in unstructured text.
- Small regional data sample sizes require adjusting vector recall thresholds for small datasets. This reduces redundant recall results.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
VECTOR_DB_INDEX_PARTITION_FIELD | Regional Coverage | Matches the geographic segmentation feature of urban commercial bank research reports, assigns data to index partitions corresponding to their regions, narrowing recall scope |
EMBEDDING_BATCH_SIZE | 32–64 entries/批 | Adapts to the long text length of individual urban commercial bank research reports, balancing embedding efficiency and memory usage |
INDEX_INCREMENTAL_SYNC_ENABLE | Enabled | Adapts to the ad-hoc update requirements of temporary research reports, avoiding resource consumption from full index rebuilding |
METADATA_FILTER_FIELDS | Report Number, Release Date, Regional Coverage | Extracts core metadata from research reports, enabling precise filtering alongside vector recall |
RECALL_TOP_K | Top 10–15 entries | Adapts to the small sample size scenario of urban commercial bank research reports, avoiding excessive redundant recall results |
EMBEDDING_MODEL_TYPE | Domestic Open-Source Vector Model | Meets data compliance requirements and adapts to the local data processing needs of urban commercial banks |
The parameter values provided on this page are common starting points for configuration. Actual values vary based on material formats, data volume and business rules. Specific issues require individual analysis. Testing on local samples is recommended before finalizing configuration.
Three Common Misconfigurations
- Phenomenon: Permission denied errors occur when attempting to directly manage vector database create, update and delete operations via MongoDB. Cause: Vector database permission management is handled by FastGPT's built-in access control. Directly operating the external database bypasses the built-in permission verification logic.
- Phenomenon: Index construction fails after integrating a domestic vector model, returning an unsupported format error. Cause: Text preprocessing rules corresponding to the model have not been configured. Structured table text in urban commercial bank research reports is not extracted correctly, resulting in input formats that do not meet model requirements.
- Phenomenon: FastGPT returns a connection refused error when invoking a vector model deployed via Ollama, but curl tests can successfully connect to the model service. Cause: The access address or port of the model service is not correctly filled in the FastGPT vector service configuration, causing internal requests to fail to connect to the external model service.
How to Verify Successful Configuration
- Upload one urban commercial bank research report with a region coverage field, check the partition tags in the index management interface, and confirm that data is assigned to the index partition corresponding to its region.
- Trigger an incremental update operation, check the system logs for full index rebuild prompts, and confirm that the incremental synchronization configuration is active.
- Initiate a research report retrieval request, combine regional metadata filtering, and check if the returned result fields include the configured metadata filter items.
- Call the vector model test interface, input research report text, and confirm that the returned embedding vector dimension matches the configured model dimension.
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.