What the Data for This Category Looks Like
Refinery investment research data comes from real-time equipment operation logs, process operation specifications, batch quality inspection reports, crude oil property databases, and industry supply and demand research reports. Operation logs are updated hourly or minute-by-minute. Process specifications and industry standards are revised quarterly or annually. Quality inspection reports are generated synchronously with each production batch. Documents fall into two categories: structured parameter tables and long-text analysis reports. Structured fields include reaction temperature, tower pressure, and material flow rate. Common units are degrees Celsius, megapascals, and cubic meters per hour. Long texts mostly cover process anomaly analyses and technical renovation plans.
How These Characteristics Create Constraints for Vector Models and Indexing
The real-time or batch-level update rhythm of refinery investment research data requires indexes to support incremental updates. This avoids repeated vector calculations for existing documents. The mixed structure of structured parameter tables and long-text reports requires support for both structured field retrieval and semantic vector recall. Fixed units and dedicated fields for process parameters require preserving field metadata during preprocessing. This prevents loss of key semantic information during vector encoding. The high proportion of long-text analysis reports increases single-document processing time. This requires vector models adapted to long contexts and reasonable chunking strategies.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk length | 800–1200 characters | Refinery process documents mostly contain continuous process logic. Overly long chunks will destroy context association, while overly short chunks will lose critical parameter association |
recall count | 10–15 entries | Refinery investment research needs to cover multi-dimensional process parameters and associated reports. Too few entries will miss critical information, while too many will increase inference overhead |
similarity threshold | 0.72–0.85 | Semantic similarity requirements for refinery process parameters are relatively high. A threshold that is too low will introduce irrelevant working condition data, while a threshold that is too high will filter out valid matching items |
incremental indexing | Enabled | Refinery data has real-time or batch-level updates. Incremental indexing avoids the time overhead of full reconstruction |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Vector encoding and index generation for long-text process reports take a long time. The default duration is insufficient to complete processing |
reranked return count | 5–8 entries | Investment research scenarios require precise top results for decision-making. Too many entries will interfere with analyst judgment |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on one’s own samples before finalizing values.
Three Common Configuration Mistakes
- Phenomenon: Setting
chunk lengthto 3000 characters results in lost text blocks for some documents. Cause: Some refinery process documents contain continuous long tables and unbroken parameter lists. These exceed the maximum context window limit of the vector model, leading to automatic truncation of uncounted chunks during parsing. - Phenomenon: The knowledge base upload status remains stuck at "indexing" for extended periods with no progress updates. Cause: The
PARSE_FILE_TIMEOUT_SECONDSduration was not adjusted. Full indexing of large refinery reports exceeds the default timeout limit, interrupting the task. - Phenomenon: Vector retrieval results do not include unit information for corresponding process parameters. Cause: Structured field metadata was not preserved during preprocessing. Only plain text content was processed during vector encoding, losing the association between parameters and units.
How to Verify Correct Configuration
- Upload a typical refinery process report. Check the number of chunks in the background parsing log to confirm the chunk length matches the preset configuration.
- Initiate a vector retrieval request. Verify that the number of returned results and similarity range meet business requirements.
- Test uploading an updated quality inspection report. Confirm the indexing task only processes new documents, verifying the incremental indexing function is active.
- View the vector model call log. Confirm no timeout or encoding failure error messages appear.
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.