What the data for this category looks like
Paper manufacturing industry financial report data comes from three main sources: annual, semi-annual, and quarterly reports publicly disclosed by listed companies, and monthly industry operation data released by the China Paper Association. Update cycles follow annual, semi-annual, quarterly, and monthly schedules. The length of individual financial report documents varies widely, from dozens of pages to over 100 pages. Core fields include operating revenue, attributable net profit, mechanical paper and paperboard production capacity, raw material procurement costs, production-sales rate, and more. Common units are ten thousand yuan, yuan/ton, and ten thousand tons/year.
What constraints do these characteristics impose on vector models and indexing
Wide variation in document length requires preserving business context integrity during long text splitting. This avoids splitting cross-paragraph associated data such as production capacity and cost metrics. Core fields include both structured numerical values and unstructured analysis text. Vector models must support encoding for both data types. Indexes must support combining structured field filtering with vector recall. Multi-cycle updated data requires support for incremental index construction. This avoids resource consumption caused by full index reconstruction. Industry-specific indicators have strong semantic relevance. Index recall rules must align with the business logic of these indicators.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Balances context integrity and vector recall accuracy, and adapts to cross-paragraph associated data in long financial reports |
overlap_ratio | 15–20% | Preserves associated information such as cross-segment production capacity and costs, and prevents key business logic from being split |
embedding_model | shaw/dmeta-embedding-zh or bce-embedding-base_v1 | Adapts to structured and unstructured text encoding for Chinese financial reports, and supports semantic understanding of industry-specific indicators |
vector_top_k | Top 8–12 entries | Core indicators of paper manufacturing financial reports are concentrated in distribution. Excessive recall introduces irrelevant data, while insufficient recall misses key associated information |
index_incremental_update | Enabled | Financial reports are updated quarterly and monthly. Incremental updates reduce computing resource consumption and shorten update cycles |
filter_field | report_type, publish_date | Data must be filtered by report cycles such as annual and semi-annual to match user analysis needs |
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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- After configuring the
bce-embeddingchannel, the platform prompts "No available vector channel". The cause is failure to correctly configure the API address and key for the channel, or the channel's service port is not open to the FastGPT deployment environment. - In a deployment environment with 8 cores, 64G memory, and an RTX2070 graphics card, knowledge base search response times are too long. The cause is failure to enable GPU acceleration for the vector model, or an excessively large
vector_top_kparameter value leading to too much recalled data. - Split text paragraphs cannot associate the business logic of production capacity and raw material costs. The cause is mistakenly using the vector model for text splitting tasks, without using a large language model to complete structured splitting.
How to Verify a Successful Configuration
- Manually upload a single paper manufacturing financial report sample, check if the split text retains cross-paragraph associated information about production capacity and costs, and verify that the
chunk_sizeandoverlap_ratioconfigurations match expectations. - Test the encoding response of the configured embedding model on the vector model management page, confirm that the channel status is normal and there are no connection errors.
- Trigger an incremental index update, check if the index update process starts normally, and confirm that the
index_incremental_updateswitch is enabled. - Set a structured field filtering rule, test that after filtering reports of the corresponding cycle by
report_type, the recall results only include eligible financial report data.
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.