What this category’s data looks like
Plastics and rubber investment research data comes from multiple sources: public bulk commodity spot trading data, monthly supply and demand reports from industry associations, warehouse receipt data from futures exchanges, and public financial reports of industrial chain enterprises. Update frequencies vary significantly: warehouse receipt data updates in real time, spot quotes update daily, and industry reports are released weekly or monthly. Document structures include structured tables (such as spot quote sheets with product name, specification, origin, price, and trading volume), semi-structured research reports, and unstructured policy documents. Field units include yuan/ton, ten thousand tons, number of contracts, and more. Some specification fields require differentiation based on origin and brand.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
The diversity of data sources and formats requires the retrieval system to adapt parsing logic for structured, semi-structured, and unstructured documents. This prevents information loss caused by single parsing rules. Differences in update frequency require differentiated incremental sync strategies. Real-time data needs high-frequency sync to maintain timeliness, while low-frequency reports can be updated on a scheduled basis. Diversity of field units requires establishing a unified unit mapping rule during retrieval. This avoids retrieval failures caused by unit mismatches. The wide span of document lengths requires segment parsing rules to adapt to content of different lengths. This prevents truncation of core investment research information.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
RECALL_TOP_K | Top 10-15 results | Plastics and rubber data includes multi-dimensional structured and unstructured content. Initial recall of sufficient entries can cover associated information for different scenarios |
SIMILARITY_THRESHOLD | 0.72-0.80 | Structured data such as spot quotes has high precision requirements. Unstructured data such as research reports has relatively larger error tolerance. This interval adapts to mixed scenarios |
PARSE_SEGMENT_LENGTH | 800-1200 characters | The length span of plastics and rubber research reports is large. This length can fully retain core paragraphs such as supply and demand analysis and policy interpretation, avoiding truncation of key data |
INCREMENTAL_SYNC_INTERVAL | 15 minutes / Daily | Real-time warehouse receipt data is synced every 15 minutes. Spot quotes are synced daily. Differentiated update frequencies adapt to different data sources |
RERANK_TOP_K | Top 5-8 results | The number of initial recall entries is large. Reranking can screen out core content most matching investment research queries, improving retrieval accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large industry research reports takes a long time. This duration avoids interruptions during large file parsing |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are influenced by material forms, data volume, and business rules. Specific issues require targeted analysis, and testing against one’s own samples before finalizing settings is recommended.
Three common mistakes
- Phenomenon: After uploading a supply and demand data table in Excel format, the specified production capacity field cannot be matched during retrieval. Cause: Column mapping configuration for Excel structured parsing is not enabled, so no association is established between field names and retrieval keywords.
- Phenomenon: After calling the chat interface, the returned results do not carry the unique identifier of the associated knowledge base. Cause: The retrieval traceability function is not enabled, and the knowledge base ID is not written to the
sourcefield returned by the interface. - Phenomenon: For product specification images included in the uploaded spot quote sheet, the text content in the images cannot be recalled during retrieval. Cause: OCR recognition configuration for document parsing is not enabled, so embedded information in images is not extracted.
How to confirm the configuration is correctly set
- Upload a mixed test file containing a structured quote sheet and unstructured research reports. Verify that field information in different formats can be extracted after parsing.
- Initiate retrieval queries including "plastic spot price" and "rubber futures delivery rules". Check that the update time of recalled results matches the latest update time of the data source.
- Call the chat interface. Check whether the returned results include the
sourcefield and the associated knowledge base identifier. - Adjust the similarity threshold parameter. Verify that the number of retrieval results changes as expected with the threshold.
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.