What the data for this category looks like
Due diligence data for the plastics and rubber category comes from multiple sources: public market data from domestic commodity exchanges, monthly survey data from industry associations, import and export declaration data from the General Administration of Customs, and production capacity and operating rate information officially disclosed by manufacturing enterprises.
Update frequencies vary across data sources: spot market data updates daily, supply and demand balance data updates monthly, policy updates are real-time, and corporate financial reports update quarterly.
A single due diligence report typically includes modules such as category overview, spot market conditions, upstream and downstream supply and demand, cost structure, policy impact, downstream applications, and risk warnings. It includes structured data tables and unstructured analysis text. Exclusive fields include melt flow index (unit g/10min), listed price (unit yuan/ton), and inventory (unit ton), among others.
What constraints do these characteristics impose on deployment and upgrade?
The multi-source heterogeneous data characteristics of the plastics and rubber category impose multiple constraints on deployment and upgrade workflows.
Differing update frequencies across data sources require configured incremental synchronization scheduled tasks with varied intervals. This prevents wasted index resources or data lag.
Fixed exclusive units for structured fields require preset field mapping and validation rules. This prevents subsequent analysis failures caused by mismatched units after parsing.
Each due diligence report contains multiple long tables and analysis text. Adjust file parsing chunk length and recall threshold to adapt to long document splitting and indexing.
When upgrading, synchronously update API adaptation rules for multi-source data. This avoids synchronization interruptions caused by changes to data source interfaces.
How to set the configurations
| Config Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Plastics and rubber due diligence reports contain multiple long tables and analysis text. Standard timeout durations are insufficient to complete full parsing |
maxChunkSize | 1000–1200 characters | Adapts to splitting structured data table fields and long text paragraphs, avoiding disruption to data relevance |
RECALL_TOP_K | Top 8 entries | Covers due diligence data across multiple dimensions including supply and demand, cost, and policy. Avoids redundant context or missed critical information |
MAX_QPS_PER_NODE | 100–200 | Adapts to daily retrieval request volumes, preventing overload on individual nodes |
ENABLE_MULTI_NODE | Enabled | Supports multi-node deployment to improve concurrent processing capacity, adapting to high-traffic scenarios |
SIMILARITY_THRESHOLD | 0.75–0.85 | Accurately matches industry-specific terminology and data fields, filtering low-relevance retrieval results |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. Testing should be conducted on local samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: When calling the MCP database query service with a locally deployed model, a
500 Internal Server Erroris returned. The log shows a database connection timeout. Cause: Network intercommunication rules between the local model and MCP service are not configured, and the corresponding port permissions are not opened for the privately deployed model. - Phenomenon: After uploading a plastics and rubber due diligence report, some structured fields are empty or units display incorrectly. Cause: Category-specific field mapping and unit validation rules are not preset. The general parsing module cannot recognize exclusive formats such as
g/10minandyuan/ton. - Phenomenon: The incremental indexing task trigger frequency is abnormal, either occupying too many server resources frequently or data updates lagging beyond the preset cycle. Cause: Differentiated synchronization intervals are not configured for different data sources. A fixed interval is used uniformly for all category data, which cannot match the update rhythm of monthly supply and demand data.
How to Confirm Successful Configuration
- Upload a standard plastics and rubber due diligence report, view the parsed field list, and confirm that exclusive fields such as
melt flow indexandlisted priceare correctly identified and their corresponding units are retained. - Trigger an incremental synchronization task, view the synchronization logs, and confirm that different data sources execute synchronization according to preset intervals, with no duplicate or missing synchronization records.
- Call the knowledge base retrieval interface, input industry terminology such as "polyethylene melt flow index", view the number and relevance of returned results, and adjust corresponding parameters to a range that meets business requirements.
- Test the connectivity between the locally deployed model and the MCP service, execute a database query call, and confirm no connection timeout or permission errors.
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.