What the Data for This Category Looks Like
Multi-financial financial report data primarily comes from public exchange disclosure documents, regulatory filing reports, and self-operated business ledgers. Update cycles fall into two categories: scheduled and ad-hoc. Scheduled reports are released quarterly, semi-annually, and annually. Ad-hoc announcements are triggered by major business changes. Documents center on structured financial tables, supplemented by unstructured management discussion and risk disclosure content. Common fields include net fee and commission income, investment income, return on net assets, net capital adequacy ratio, among others. Units include yuan, ten thousand yuan, hundred million yuan, and percentage.
What Constraints These Characteristics Impose on Vector Models and Indexing
Structured fields account for a large share of the data and follow unified naming rules. However, the unstructured notes section contains extensive long text. This requires vector models to support both structured metadata and long text embedding. Financial reports are updated on fixed cycles and support incremental synchronization. This requires indexes to support fast incremental writes while avoiding full reconstruction. Indicators across different business segments have strong correlations. This requires indexes to support filtering and recall based on metadata such as business segment and reporting period, to avoid interference from irrelevant data. Long text chunking must preserve financial report chapter boundaries, to prevent splitting continuous business indicator descriptions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk length | 1000–1500 characters | Adapts to the long text structure of financial report notes, preserves complete descriptions of individual business segments or financial indicators |
recall count | Top 10–15 results | Multi-financial financial reports have strong correlations across business segments, requiring recall results that cover multiple sets of related indicators |
similarity threshold | 0.75–0.85 | Filters low-relevance financial report entries, avoids interference from indicators of non-target businesses on query results |
incremental sync toggle | Enabled | Adapts to the quarterly incremental update requirements of financial reports, reduces resource overhead from full index reconstruction |
metadata filter fields | Reporting period, business segment | Binds core metadata of financial reports, enables precise recall based on reporting cycle and business type |
embedding_dim | Matches the output dimension of the deployed model | Adapts to the vector output format of third-party embedding models, prevents embedding failures caused by dimension mismatches |
The parameter values provided on this page are 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
- Phenomenon: Connection timeouts or dimension mismatch errors occur when connecting to Qwen3-Embedding-8B deployed via VLLM. Cause: The
embedding_dimparameter in FastGPT is not configured to match the actual output dimension of the model, or network access permissions for the VLLM service are not enabled. - Phenomenon: Knowledge base disk usage exceeds expectations, with additional redundant data. Cause: Incremental index cleanup configuration is not enabled, and old vector index files are not automatically cleaned, leading to accumulation of redundant data.
- Phenomenon: After setting up the index, recall results do not match expectations, with a large number of irrelevant financial report entries. Cause: The
metadata filter fieldsare not configured, or the filter conditions do not match the actual field names of the financial reports, leading to the index not taking effect.
How to Confirm Proper Configuration
- Upload a single multi-financial financial report document, enter the document management page to view chunking results, confirm that chunks do not split continuous business indicator descriptions in the financial report.
- Initiate a query that includes a specific business segment, check the metadata tags of the recall results, confirm that only document fragments from the corresponding reporting period are returned.
- Log in to the vector database management interface, view the index update time, confirm that incremental update tasks are automatically triggered according to the configured cycle.
- Call the embedding model test interface, compare the returned vector dimension with the
embedding_dimparameter value configured in FastGPT to confirm they match.
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.