What data for this category looks like
The data sources for plastics and rubber due diligence reports mainly include industry association monthly research reports, spot market quotation systems, customs import and export ten-day data, and production enterprise quality inspection reports. Update rhythms vary across data sources: spot quotations are updated daily, industry research reports are updated monthly, and customs data is updated every ten days. Most documents are PDFs with structured appendices or Excel spreadsheets. Core fields include raw material grade, manufacturer, density, melt index, tensile strength, and current month average price. Corresponding units are g/cm³, g/10min, MPa, and yuan/ton. Fields in a single document have strong correlations, with multiple indicators for the same raw material usually grouped in the same table row.
What constraints these characteristics impose on knowledge base retrieval and recall
High structured data proportion and strong field correlation require retrieval to preserve field correspondence. Do not only use plain text fuzzy splitting. Wide variation in data source update frequencies requires setting different incremental sync periods for each data source. This avoids outdated data or duplicate syncs. Complex raw material grade naming rules, with many synonyms or similar names, require expanding the synonym library during retrieval. This covers grade naming from different manufacturers. High precision requirements for single document field values require strict core field matching during recall. This avoids introducing raw material data from unrelated categories. Due diligence reports need to associate multi-dimensional data. Recall results must be sorted by time. This lets users quickly access the latest quotation and quality inspection data.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_TABLE_STRATEGY | Retain field names + cell values | Most plastics and rubber documents use structured tables. This strategy fully preserves the correlation between core fields such as raw material grades and prices. It avoids field fragmentation after splitting. |
EMBEDDING_BATCH_SIZE | 32–64 entries | Table cells in plastics and rubber documents are mostly short text. An overly large batch size causes semantic deviation in embedding vectors. An overly small batch size reduces parsing efficiency. |
RECALL_TOP_K | Top 8–12 entries | Due diligence reports need to cover multi-dimensional raw material data. Too many entries cause context overload. Too few entries lead to missed key quotation or quality inspection data. |
SIMILARITY_THRESHOLD | 0.75–0.85 | Naming rules for plastics and rubber raw material grades are complex. This range balances precise matching and recall scope after synonym expansion. |
SYNC_INCREMENTAL_INTERVAL | Set according to data source (daily/monthly/ten-day) | Different data sources have different update frequencies. Matching the corresponding incremental sync cycle ensures data timeliness. |
MAX_CONTEXT_CHARS | 8000–12000 characters | Due diligence reports require associating documents from multiple data sources. This length can accommodate sufficient structured data fragments. |
The parameter values provided on this page are all common recommended starting points for configuration settings. Actual values are affected by material form, data volume and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: When uploading structured spreadsheet documents in version 4.8.9, the interface returns an
embedding errorprompt, and the backend returns status code 500. Cause:PARSE_TABLE_STRATEGYis not configured to retain field mode, resulting in failure to parse structured cell content during embedding. - Symptom: When deploying version 4.8.9 using docker-compose, after creating a new knowledge base, the raw material grade field of structured data is empty. Cause: The table structured parsing switch is not enabled, causing documents to only be split as plain text, losing the correlation between core fields.
- Symptom: Search results include plastic raw material data from unspecified categories, and the number of recalled entries exceeds expectations. Cause: The
SIMILARITY_THRESHOLDthreshold is not set, or the threshold is set too low, resulting in the recall of documents that are semantically similar but do not match the target category.
How to confirm the configuration is correct
- Upload a plastics and rubber quality inspection report containing a structured table. Check if the parsed text retains the correspondence between core fields such as raw material grades and unit prices. This confirms the table parsing configuration is correct.
- Initiate a search request for a specific plastic grade. Verify the relevance of the recall results, and adjust similarity-related configurations according to business needs.
- View the knowledge base sync logs. Confirm that the incremental sync cycle matches the update rhythm of the corresponding data source. This verifies the rationality of the sync configuration.
- Test uploading a file that meets the business document size limit. Confirm that the upload process does not have exceptions. This verifies that the upload configuration meets 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.