What the data for this category looks like
Rural commercial bank research report data primarily comes from internal risk control surveys, regional agricultural industry analysis reports, industry guidance documents issued by regulatory authorities, and county-level economic monitoring materials from cooperating third parties. Internal survey documents are updated quarterly. Regulatory files are synchronized in real time as policies are released. Third-party materials are updated monthly. Most documents are long paragraph-style texts, containing fields such as regional industry distribution, farmer credit data, and risk warning items. Some include structured tables, with units mostly being localized statistical units such as ten thousand yuan, number of households, and number of people.
Constraints on Vector Models and Indexing
The mixed document structure from multiple sources requires the indexing system to support mixed vectorization of structured tables and paragraph texts, to avoid losing localized statistical information within tables. Widely varying update cadences require a hybrid scheduling mechanism for incremental and full indexing, to adapt to the update needs of real-time regulatory files and periodic survey documents. Dense localized terminology means vector models must adapt to exclusive industry vocabulary such as county-level agriculture and small micro-credit. General vector models have insufficient semantic recall accuracy for this use case. The high proportion of long texts requires setting a length threshold for single-segment vectorization, to avoid exceeding the model’s context window limits.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the paragraph length of rural commercial bank research reports, avoids overly long single segments exceeding the model context window, while ensuring semantic integrity |
chunk_overlap | 100–150 characters | Covers key terms across segments, prevents localized exclusive vocabulary from being split and lost |
recall_top_k | Top 20–30 results | Balances retrieval accuracy and response speed for rural commercial bank research report scenarios, adapts to recall requirements for targeted use cases |
similarity_threshold | 0.72–0.78 | Adapts to the semantic similarity distribution of county-level financial terminology, filters low-relevance general industry documents |
index_refresh_interval | Full refresh quarterly + incremental refresh hourly | Matches the update cadence of real-time regulatory file updates and quarterly survey document updates |
table_parse_enable | Enabled | Retains statistical information from structured tables in research reports, avoids losing structured data when only vectorizing paragraph texts |
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 case-by-case analysis. Testing on available samples is recommended before finalizing configuration values.
Three Common Configuration Mistakes
- Phenomenon: After adding a new embedding model and completing index construction, retrieval tests trigger errors, and the interface returns
400 Bad Request. Cause: The vector dimension configuration of the index database was not matched to the output dimension of the newly connected model, resulting in misalignment of vector queries. - Phenomenon: Retrieval results only return similarity scores, and associated original research report text fragments cannot be viewed. Cause: Metadata storage of original texts was not enabled during index configuration, only vector data was written to the database, and no association was made with the original content fields of the documents.
- Phenomenon: When using the
bge-m3model, the similarity scores returned by retrieval generally exceed 0.95, which is far higher than the normal range. Cause: No normalization processing was performed on vector data during index construction, causing the cosine similarity calculation results to deviate from reasonable ranges.
How to Verify Correct Configuration
- Verify the vector dimension configuration of the index database, ensure it matches the output dimension of the currently used embedding model.
- Run a retrieval test for a single research report, confirm that associated original document text fragments are visible in returned results.
- Adjust the similarity threshold parameter, observe changes in retrieval result relevance, confirm that the configuration adapts to scenario requirements.
- Check the running logs of index refresh tasks, confirm that the trigger cadence of full and incremental updates matches preset requirements.
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.