What Data for This Category Looks Like
Construction machinery investment research data primarily comes from manufacturer public technical manuals, industry association monthly working condition reports, bidding project announcements, equipment operation logs, and patent literature. Data updates are triggered by manufacturer new product launches and industry policy adjustments, with no fixed cycle. Core parameter documents see no substantial updates within six months. Single documents are mostly structured parameter tables, semi-structured project minutes, or long-text analysis reports. Fields include rated power, operating weight, working radius, and similar metrics. Units are mostly kW, t, m. Some documents include multilingual parameter comparisons.
Constraints on Vector Models and Indexing
Construction machinery investment research data has a large number of structured parameter fields and fixed units. This requires vector models to support structured metadata encoding or associated retrieval to avoid semantic confusion. Semi-structured project minutes and long-text analysis reports contain lengthy technical paragraphs. Reasonable segmentation rules must be applied to prevent semantic cuts from disrupting professional logic. Data updates have no fixed cycle and include duplicate project information. This requires indexes to support incremental updates and precise deduplication. Some documents include multilingual parameter comparisons. Vector models must have cross-language semantic alignment capabilities to ensure consistency in cross-language retrieval.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the long paragraph characteristics of construction machinery technical documents, avoids cutting professional terms and complete technical logic |
chunk_overlap | 10%–15% of segment length | Connects context between adjacent segments, prevents professional terms from being truncated at segment start or end |
top_k | 15–25 entries | Covers multi-dimensional parameters and project information required for investment research, balances retrieval accuracy and response latency |
score_threshold | 0.75–0.85 | Distinguishes precise professional parameter matches from irrelevant content, reduces false recall probability |
incremental_index | Enabled | Adapts to the characteristic of no fixed data update cycle, avoids full index reconstruction consuming computing resources |
batch_import_size | 800–1200 entries per batch | Adapts to import stability for 100k-level CSV data, avoids single upload timeout or memory overflow |
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 individual analysis. It is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: After importing 100,000 entries of test CSV data, the number of remaining documents after vectorization is less than 100,000, with thousands of entries missing. Cause: The
batch_import_sizeconfiguration is not set to match the per-batch data volume, leading to partial batch upload timeouts being truncated, or incorrectly formatted rows in the CSV not being automatically filtered. - Phenomenon: Collection creation is reported as successful, but the page shows that the index has failed to build with no clear error logs. Cause: The
incremental_indexconfiguration is not enabled, or the vector model API call times out without triggering automatic retries, causing the index construction task to interrupt without automatic recovery. - Phenomenon: Vector retrieval works normally locally, but when packaged as a Docker image and run, all text vector scores are completely identical. Cause: The access credentials or environment variables for the vector model are not correctly configured inside the Docker container, causing all requests to call the default baseline vector, or the model call link has an uncaught exception.
How to Confirm Configurations Are Correct
- Import 100,000 entries of test CSV data, verify that the number of imported documents matches the source data, confirming no bulk import omissions.
- Trigger an incremental update task, check the index construction logs, confirm that only newly added or modified documents are updated, and no full reconstruction is performed.
- Run pure vector retrieval and hybrid retrieval separately, compare response times and recall results, confirming that the retrieval logic complies with configuration requirements.
- View the vector model call logs, confirm that each call returns a unique vector result, with no abnormal situation of completely identical scores.
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.