What This Category of Data Looks Like
Coke investment research data sources include domestic commodity exchange futures market data, industry monitoring data from national coking industry associations, port spot transaction ledgers, and production and sales reports from upstream and downstream enterprises. Update frequencies cover trading-day updated futures market data, weekly or monthly released industry monitoring reports, and enterprise operation reports submitted on demand. Document structures include structured tabular data, long-form industry research reports, and single dynamic news items. Fields include delivery grade, dry ash content, dry sulfur content, crush strength, wear strength, daily trading price, total inventory. Units are grade code, measured value, measured value, strength grade, strength grade, yuan/ton, 10,000 tons respectively.
What Constraints These Characteristics Impose on Vector Models and Indexing
Coke investment research data includes multiple types of structured tables, long-form industry research reports, and high-frequency market data. Structured data contains multiple business-related fields. Long-form content includes upstream and downstream industry chain linkage analysis. Market data is updated at high frequency on trading days. Multi-field structured data has wide semantic span. Single text blocks must bind related fields to retain business integrity. The industry chain logic in long-form research reports requires retaining chapter context. Hard chunking will destroy semantic connections. High-frequency updated market data requires indexes to support incremental synchronization, avoiding resource consumption from full reconstruction. For large-volume ledger files, chunking must balance chunk size and business unit integrity, preventing semantic fragmentation within chunks or excessive vector generation pressure from too many chunks.
Configuration Recommendations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Aligns with the business unit length of daily single-commodity coke market data and associated indicators, matches the context limit of mainstream 1024 vector models, and avoids semantic splitting within chunks |
chunk_overlap | 100–150 characters | Retains cross-chunk association information for upstream and downstream industry chains in coke research reports, preventing chapter logic breaks |
vector_model_max_tokens | 1024 | Adapts to the maximum input limit of publicly available general-purpose vector models; truncate or split overly long business blocks in advance |
index_refresh_interval | 15 minutes | Matches the high-frequency update rhythm of coke futures market data, balances index real-time performance and server resource consumption |
recall_top_k | Top 8 entries | Covers core business analysis dimensions including market, inventory, upstream and downstream, and policy data required for coke investment research, balancing multi-dimensional associated data retrieval |
upload_file_max_size | 2000 MB | Supports uploading large-volume files such as monthly full port inventory ledgers, avoiding data omissions caused by file size limits |
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 case-by-case analysis. It is recommended to test on internal samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: When uploading Excel structured data with 10,000+ rows, default chunking leads to overly large individual chunks. Retrieval fails to match precise business dimensions, and excessive vector generation resources are consumed. Cause:
chunk_sizeandchunk_overlapparameters are not adjusted for coke's multi-field structured data. General document chunking logic is used, and data is not split by business unit. - Phenomenon: When uploading coke inventory ledger files larger than 10 MB, over 1,000 chunks are generated, and some chunks fail vectorization. Cause: The
chunk_sizeparameter is not limited, and automatic splitting of ultra-long chunks is not enabled. This leads to too many chunks, and some chunks exceed thevector_model_max_tokenslimit, triggering vector generation exceptions. - Phenomenon: Some chunks fail vectorization, and normal status resumes after repeated retries. Cause: The concurrency of vector generation tasks is too high. The
vector_batch_sizeparameter is not configured to limit simultaneous requests, leading to partial requests timing out due to insufficient server resources. Retries free resources and restore normal operation.
How to Verify Proper Configuration
- Upload a single daily coke market Excel file, check the number of chunks after splitting and the character count per chunk. Confirm that the
chunk_sizeconfiguration matches expected business unit requirements. - Retrieve keywords related to the coke industry chain, check the field completeness and business relevance of recall results. Confirm the configuration effects of
recall_top_kandchunk_overlapparameters. - Submit updated coke futures market data, check the time lag of index refresh. Confirm that the
index_refresh_intervalparameter matches the update rhythm. - Upload a ledger file larger than 10 MB, check the number of generated chunks and vectorization success rate. Confirm that the
upload_file_max_sizeand ultra-long chunk processing configurations are effective.
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.