What Data for This Use Case Looks Like
Data primarily comes from quarterly reports officially disclosed by joint-stock banks, and files exported from internal financial report management systems. Core operating statements are updated quarterly. Full financial reports and accompanying notes are updated annually. Most documents use a format of structured tables nested with paragraph explanations. They include core business fields such as total assets, non-performing loan ratio, and operating revenue. Units are mostly hundreds of millions of yuan, percentage, and ten thousand yuan. A single full financial report can be dozens of pages long, with structured tables accounting for a relatively concentrated share of content.
Constraints Imposed on Vector Models and Indexing by These Characteristics
Regularly updated structured financial report data requires vector indexes to support incremental synchronization. This avoids the computing resource consumption caused by full index rebuilding. The multi-table nested document structure requires vector models to have structured field encoding capabilities. This prevents the loss of cross-table association semantics. Long single documents with concentrated core fields require indexing chunking to retain field association identifiers, while controlling dimensional redundancy of single-block vectors. The fixed quarterly and annual update rhythm allows adaptation to batch indexing scheduling. No real-time index trigger logic needs to be configured.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | bge-m3:latest or aliyun-text-embedding-v3 | Adapts to semantic encoding of structured financial report text, supports alignment of financial domain terminology |
chunk_size | 800–1200 characters | Matches the typical length of structured tables and accompanying note paragraphs in financial reports, avoids splitting across semantic units |
chunk_overlap | 100–150 characters | Retains associated explanatory text across segments, prevents semantic breaks between financial report metrics and their annotations |
retrieval_top_k | 8–12 results | Adapts to the concentrated distribution of core financial report metrics, avoids recalling excessive non-core field information |
embedding_batch_size | 32–64 | Balances single-batch computing resource usage and indexing construction efficiency, adapts to batch processing of single financial reports |
index_sync_mode | Incremental synchronization | Matches the quarterly update rhythm of financial reports, reduces resource consumption from full index rebuilding |
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 Misconfigurations
- Symptom: "Vector model connection failed" prompt appears when using
bge-m3deployed via Ollama as the embedding model. Cause: The local Ollama interface address was not correctly filled in the FastGPT embedding model configuration, or Ollama cross-domain access was not enabled. - Symptom: "No available channels under the default group" prompt appears when configuring
aliyun-text-embedding-v3. Cause: Access permissions for the vector retrieval service were not applied for for the bound API key, or the corresponding Alibaba Cloud region node was not specified in the FastGPT model group. - Symptom: Retrieval response times out during knowledge base question answering, and background logs show excessively long vector recall time. Cause: Financial report documents were uploaded as unsegmented full text, leading to excessively large single vector data volume, or the number of recalled entries was configured beyond a reasonable range, increasing retrieval computing load.
How to Confirm Successful Configuration
- Upload a single quarterly financial report document, check the FastGPT parsing task log. Confirm that the embedding task has no errors, and the number of generated vector blocks matches the expected document structure.
- Enter core financial report keywords in the knowledge base test page. Check whether the recall results include corresponding tables and note content, confirm that semantic matching meets business requirements.
- Enter the index management page, check the execution records of incremental synchronization tasks. Confirm that tasks can be automatically triggered according to the preset update rhythm, with no failed or blocked records.
- View the embedding model operation monitoring panel. Confirm that the time taken for single-batch vector generation is within a reasonable range, with no continuous timeouts or resource exhaustion.
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.