What data looks like for this category
Oil and gas extraction research report data primarily comes from public exploration reports released by industry associations, annual development documents from oil and gas production enterprises, and specialized analysis from third-party energy consulting institutions. Update frequency varies by content type: new well commissioning and exploration progress updates are released on a monthly basis, while annual development plans and policy interpretations are updated quarterly. Document structures include fixed fields: block geographic parameters, drilling technical indicators, single-well production data, cost accounting items, and compliance requirements. Most technical indicator units are meters, cubic meters per day, and yuan per barrel. Production data fields often include continuous monitored time series segments.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Multi-source heterogeneous data sources create differences in report formats and field naming. Direct uploads will cause field mapping confusion and reduce recall matching accuracy. Content with different update frequencies must be split into incremental update tasks to avoid wasting computing resources from full re-scans. Individual research reports are lengthy, containing extensive technical details and data tables. Uploading full documents will exceed context window limits, so content must be split by technical chapter instead of uploading entire pages. Specialized business fields require search queries to link to specific dimensions, otherwise irrelevant cost calculation or policy interpretation content may be recalled, reducing retrieval accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_CHUNK_SIZE | 800–1200 characters | Technical chapter segments of oil and gas extraction research reports mostly fall within this range, preserving core context such as drilling parameters and recovery rate calculations |
RECALL_TOP_K | Top 8–12 results | Research report data volume is large; too many recalled results will exceed context window limits, while too few will fail to cover all relevant business dimensions |
SIMILARITY_THRESHOLD | 0.72–0.80 | Many specialized business fields require high matching accuracy to avoid recalling irrelevant cost calculation or policy interpretation content |
RERANKER_ENABLED | Enabled | Multi-source data introduces noise in initial recall; reranking further filters document fragments highly relevant to search queries |
UPLOAD_FILE_MAX_SIZE | 500 MB | Individual oil and gas extraction research report PDFs mostly fall within the 100–300 MB range, with reserved redundancy for merged document scenarios |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Long PDFs require significant time for table extraction and text splitting; this duration covers parsing requirements for most individual research reports |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific situations require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: Returned answers do not link to knowledge base citations, or cited content has no logical connection to the answer. Cause: The
SIMILARITY_THRESHOLDthreshold is not set, or the threshold is set too low, resulting in recall of irrelevant document fragments. - Phenomenon: LLM-generated content exceeds the scope of the knowledge base. Cause: Reranking via
RERANKER_ENABLEDis not enabled, and the system prompt does not explicitly limit responses to only using recalled fragments, so noisy fragments from initial recall are not effectively filtered. - Phenomenon: Knowledge base retrieval takes more than 30 seconds. Cause: Incremental update tasks are not split by update frequency, resulting in full scans of multi-source heterogeneous research report data, or
RECALL_TOP_Kis set too high, leading to an overly large initial recall candidate set.
How to confirm proper configuration
- Upload a single typical oil and gas extraction research report, check the parsed segmented results, confirm the segmentation logic matches the configured segmentation parameters, and no core technical content is truncated.
- Enter a search query containing specialized business fields, verify the relevance of recalled results, and adjust the matching threshold until all recalled results cover the target business dimensions.
- Check incremental update task logs, confirm that multi-source data is processed split by update frequency, with no abnormal full scan records.
- Trigger a retrieval and verify generated content, confirm that answers are only based on recalled knowledge base fragments, with no external information introduced.
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.