What the data for this category looks like
Papermaking industry research report data comes primarily from authoritative industry institutions, securities firm research departments, public announcements of listed papermaking enterprises, and professional industry databases. Documents combine structured and semi-structured formats. They include fields such as industry supply and demand scale, individual plant production capacity, raw material costs, finished paper prices, and environmental policy requirements. Price and production capacity fields mostly use units of yuan/ton and ten thousand tons. Securities firms release research reports irregularly alongside research projects. Industry data documents update monthly or weekly. Enterprise announcement documents update in real time when disclosed. Individual document lengths range from short data tables with hundreds of characters to in-depth analysis content with tens of thousands of characters.
What constraints do these characteristics impose on the vector models and indexing workflow
Papermaking research report document characteristics create multiple constraints for vector models and indexing workflows. Properly split long research reports to retain complete semantic meaning. Avoid excessive splitting of short data documents, which causes semantic fragmentation. Fields include multiple types of numerical indicators. Vector models must support mixed encoding of structured numerical values and industry terminology text to avoid overlooking numerical information. Update frequencies vary significantly. Support incremental indexing for real-time announcement content. Use full indexing mode for regularly updated industry reports. Uneven character spans of individual documents cause fluctuations in token consumption during vector database ingestion. Adjust segmentation parameters to accommodate different document lengths.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
segment_length | 800–1200 characters, overlap rate 15–20% | Accommodates the mixed structure of papermaking research reports, which include both long-form analysis and short numerical tables. Balances semantic completeness and indexing density |
embedding_model | bge-large-zh-v1.5 or industry-fine-tuned embedding models | Supports mixed encoding of Chinese industry terminology and numerical fields, improving the accuracy of research report semantic retrieval |
recall_count | Top 10–15 results | Papermaking research reports have significant differences across market segments. This range avoids introducing irrelevant content while covering core relevant documents |
similarity_threshold | 0.72–0.80 | Semantic similarity of industry terminology is relatively high. This threshold filters out noise while retaining valid relevant content |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Allows uploading complete large-scale industry research report files, avoiding upload failures caused by oversized files |
incremental_indexing_switch | Enabled | Adapts to real-time updated enterprise announcement research reports, reducing computational overhead from repeated indexing |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Slow response during post-retrieval reranking, page load time exceeds 10 seconds. Cause: Set
recall_counttoo high, and do not adjustsegment_lengthto accommodate short documents. This creates excessive computational load from vector retrieval and reranking. - Phenomenon: Irrelevant research reports make up a high proportion of retrieval results, and returned content does not match papermaking market segments. Cause: Set
similarity_thresholdtoo low, or do not use an embedding model adapted to industry terminology. This leads to insufficient semantic matching accuracy. - Phenomenon: Fail to load the indexing model after Docker deployment, console reports
model not founderror. Cause: Do not configure the indexing model mounting path indocker-compose.yml, or do not correctly bind the embedding model interface via oneapi.
How to confirm the configuration is correct
- Upload a test papermaking industry research report. Check backend parsing logs to confirm segment length and overlap rate match configured parameters.
- Submit a retrieval request for papermaking industry terminology. Verify relevance and count of returned results. Adjust
recall_countandsimilarity_thresholdto meet business requirement ranges. - Upload a new papermaking enterprise announcement. Wait for indexing to complete, then retrieve the announcement content to confirm incremental indexing functions correctly.
- Check Docker container runtime logs. Confirm embedding model and indexing service connection status is normal, with no error messages.
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.