What Data for This Category Looks Like
Condiment enterprise financial report data primarily comes from periodic reports (annual reports, quarterly reports) and temporary announcements disclosed by exchanges. Industry survey data serves as a supplementary source. Quarterly reports are updated every 3 months, annual reports are updated once per year, and temporary announcements such as product price adjustments or capacity changes are released alongside business milestones. Document structure includes consolidated financial statements, detailed revenue breakdowns by main business products (soy sauce, oyster sauce, sauces, etc.), cost composition, and channel layout data. Most fields use currency units (RMB yuan/ten thousand yuan) and sales units (tons/kilo liters). Proportional fields such as gross profit margin and channel share are also included.
How These Characteristics Create Constraints for Vector Models and Indexing
Condiment financial reports contain extensive detailed business text, which requires vector models to have stronger semantic alignment capabilities for industry-specific terms such as soybean cost ratio and terminal sales rate. The mixed update rhythm of fixed-period updates and temporary announcements requires indexes to support incremental synchronization and real-time updates. Multiple unit fields exist within documents, so standardized mapping must be completed before indexing to avoid vector space confusion. Individual documents have lengthy content, so when splitting text, contextual connections between adjacent business modules must be preserved to prevent semantic breaks that reduce retrieval accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | bce-embedding-base_v1 or shaw/dmeta-embedding-zh | Adapts to Chinese professional financial report terminology, with more stable semantic alignment for detailed business content |
chunk_size | 800–1200 characters | Condiment financial reports include detailed product breakdowns. This range preserves business-related information while avoiding excessive splitting or irrelevant content |
retrieval_top_k | Top 8–12 results | Financial report data for detailed categories has multiple dimensions. A sufficient number of retrieved segments is needed to cover core modules such as product breakdowns and costs |
index_incremental_sync | Enabled | Adapts to the quarterly update and temporary announcement release rhythm of condiment financial reports, reducing repeated indexing overhead |
vector_db_similarity_threshold | 0.72–0.80 | Professional terminology similarity in financial report text requires precise differentiation. This range balances retrieval precision and coverage |
field_mapping_strategy | Map by financial report chapter grouping | Avoids vector space confusion caused by multiple unit fields, and aligns semantic boundaries between different business modules |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- When
bce-embedding-base_v1is used as the vector model, the interface prompts that no available channels are found. The cause is failure to correctly configure the API key and endpoint in FastGPT's third-party channel management, or failure to bind the channel to the corresponding knowledge base. - Under the configuration of 8 cores, 64GB memory, and RTX2070, knowledge base search takes too long. The cause is failure to limit the value of
retrieval_top_kand failure to properly split long documents, resulting in the number of loaded segments exceeding the hardware's carrying capacity during vector retrieval. - Non-condiment category revenue data is mixed into retrieval results. The cause is failure to enable classification indexing for document metadata, and failure to add exclusive tags to condiment financial report documents, resulting in inability to filter irrelevant data sources during retrieval.
How to Confirm Configuration Is Complete
- Access FastGPT's vector channel management page, confirm the channel associated with the configured vector model is available, and verify API connectivity.
- Upload a single condiment financial report document, review the chunking preview result, and confirm chunk boundaries align with business modules, with core detailed product revenue breakdowns not split.
- Run a financial report analysis retrieval, verify the number and relevance of retrieved results, and adjust
retrieval_top_kandvector_db_similarity_thresholdto a range meeting business requirements. - Review the indexing task log, confirm the incremental synchronization task triggers normally, and that the update process for periodic reports and temporary announcements aligns with expectations.
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.