What the Data for This Category Looks Like
Data sources for construction machinery research reports include publicly available statistical data from industry associations, operational data disclosed by original equipment manufacturers, and research documents from third-party consulting institutions. Updates follow a monthly routine update schedule, with quarterly deep reports released irregularly. Document structures cover detailed equipment model parameters, market sales volume, industrial chain costs, policy interpretations, future trend forecasts, and other content. Fields include equipment model, rated power, operating hours, unit sales price, regional shipment volume, and more. Units include units, kilowatts, hours, ten thousand yuan, and others. Single-document length varies widely, ranging from short messages of a few hundred characters to in-depth analyses spanning tens of thousands of characters.
What Constraints Do These Characteristics Impose on Vector Models and Indexes
This category of data contains a large number of structured numerical fields and semi-structured analysis content. Standard semantic vector models struggle to accurately capture relationships between different equipment model parameters and sales data. Specialized vector models adapted for numerical features are required. The update rhythm of research reports is uneven, with both high-frequency short monthly documents and low-frequency long quarterly documents. Indexes must support incremental updates and batch rebuilding. Single-document length varies widely. Segmentation strategies must balance long-text splitting and retention of field relevance. They must also support joint recall across multiple fields to match retrieval needs across different dimensions, such as simultaneous retrieval of equipment model parameters and market trend content.
How to Set Configurations
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
embedding_model | m3e-large or bge-large-zh-v1.5 | Adapts to the semantic alignment needs of structured numerical and semi-structured research report text for this category, and supports multi-field feature fusion |
chunk_size | 800–1200 characters | Balances semantic completeness of single-segment text and field relevance. Avoids excessive length that causes vectors to lose local details, and excessive shortness that disrupts business logic connections |
recall_top_k | Top 10–15 results | Covers multi-dimensional retrieval needs, and avoids missing key equipment model parameters or market data due to too few recalled entries |
similarity_threshold | 0.72–0.85 | Filters low-correlation results, meets the precision requirements of research report retrieval, and avoids interference from irrelevant documents |
index_batch_size | 50–100 documents per batch | Balances index construction efficiency and server load, and avoids resource exhaustion caused by overly large single batches |
auto_refresh_index | Enabled, with refresh_interval set to 86400 seconds | Adapts to monthly updated research report data, and ensures indexes stay synchronized with the latest documents |
The parameter values provided on this page are common starting points for configuration setup. 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 settings.
Three Common Mistakes
- Phenomenon: Server read-write resources are continuously exhausted and triggered daily on a schedule when calling the interface to create a knowledge base vector store. Cause: No reasonable
index_batch_sizeis set. Too many research report documents are processed in a single batch, leading to excessive memory and disk IO usage. - Phenomenon: A 400 status code is returned with no response body when using the voyage index after upgrading the version. Cause: The vector model API key is not updated to adapt to the new version interface specifications, or the request parameters contain field formats not supported by the model.
- Phenomenon: Uploading research report files occasionally gets stuck during the 1st or 2nd batch of index construction, specifically when using the
m3e-largemodel. Cause: Some documents contain non-standardized equipment model fields. The vector model cannot generate valid vectors, leading to index construction interruptions.
How to Confirm Proper Configuration
- Run a vector generation test for a single research report, and verify that the generated vector dimension matches the standard dimension of the selected model.
- Send a multi-dimensional retrieval request, and verify that the recalled results include target fields such as equipment model parameters and sales data, and that the ranking meets expectations.
- Trigger an incremental index update, and verify that the index update time matches the publication time of the latest research report.
- Check the server resource monitoring dashboard, and confirm that memory and disk IO usage remains within a reasonable range during index construction.
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.