What the Data for This Category Looks Like
General equipment research reports originate from public documents of securities firm mechanical industry research teams, China General Machinery Industry Association, and annual technical white papers from leading equipment manufacturers. These reports serve industry analysis needs for financial institutions. The standard update cadence consists of quarterly industry tracking reports, supplemented by monthly sub-category dynamic reports. Temporary reports are released during sudden policy changes or technology iterations. Document structures include core equipment parameters, production capacity scale, supply chain supporting data, market price ranges, and technology iteration milestones. Field units include kilowatts, units per year, ten thousand yuan, and other standard metrics.
Constraints on Vector Models and Indexes
The multi-field mixed structure of general equipment research reports requires vector models to support multi-field encoding, preventing loss of structured parameter information from single-text chunks. The quarterly update cadence requires indexes to support incremental refresh, avoiding resource consumption from full index reconstruction. Content blocks of varying lengths—including short parameter entries and long analysis paragraphs—require chunking strategies tailored to content density, preventing damage to parameter integrity during chunking. Cross-document supply chain associated data requires indexes to support cross-document associated recall, improving retrieval accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | General equipment research reports contain short parameter entries and long analysis paragraphs. This range balances chunk integrity and retrieval accuracy, avoiding splitting core parameters or truncating key analysis |
embedding_batch_size | 16–32 | Single documents for general equipment research reports have a large number of chunks. This batch processing range balances vectorization efficiency and memory usage, adapting to batch indexing requirements |
recall_top_k | Top 10–15 results | General equipment research reports cover many sub-categories. Too many recalled results increase subsequent filtering costs, while too few may miss associated reports |
similarity_threshold | 0.72–0.78 | Semantic similarity is high for parameter-related content in general equipment reports. This threshold filters low-relevance results and retains core matching items |
index_refresh_interval | 7 days | Industry research reports are updated on a quarterly basis. A 7-day refresh interval covers monthly dynamic reports, avoiding lag in index data |
embedding_model | Select based on scenario | General-purpose text embedding models can be used for pure-text research reports. Multimodal embedding models, such as multimodal-embedding-v1, can be used for reports containing parameter charts |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Issue: Vectorization tasks only accept single-text chunks, and batch processing is not triggered. Cause: The
embedding_batch_sizeparameter is not configured, or the batch vectorization interface is not adapted, causing the API to only pass single chunks. - Issue: Direct viewing of raw chunked index content is not supported in FastGPT 4.8.10. Cause: This version only stores vector indexes and metadata by default, and does not provide a direct query interface for chunked content. Custom API calls are required to retrieve the content.
- Issue: An error occurs in vectorization tasks after configuring the
multimodal-embedding-v1model. Cause: The image parsing switch for the multimodal embedding model is not enabled, or the model's API key and access address are not configured correctly.
How to Verify Proper Configuration
- Upload a sample general equipment research report, check the background chunk preview, and confirm that core parameter entries are not split during chunking.
- Initiate a retrieval request, review the number of returned results and similarity distribution, and adjust corresponding configuration items to fit business requirement ranges.
- Monitor index refresh logs, confirm that incremental refresh tasks run according to the preset cycle, and that no full reconstruction anomalies occur.
- Test the multimodal embedding model configuration, upload a research report containing parameter charts, confirm that no errors occur in vectorization tasks, and that matching results are returned.
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.