What Data for This Category Looks Like
Automated equipment research report data mainly comes from industry association public statistics, equipment manufacturer technical white papers, securities firm machinery industry research reports, and professional exhibition materials. Update rhythm adjusts with new product launches and quarterly industry data updates, with no unified fixed cycle. Each single document includes four core modules: equipment model, core parameters (such as rated power, operating speed), application scenarios, and market analysis. Field units include industrial professional units such as kW, m/min, and unit. Some documents attach parameter comparison tables and real photo analysis content.
What Constraints These Characteristics Impose on Vector Models and Indexing
Automated equipment research reports have dense technical terminology, uniform field units, and a large number of model codes. This requires vector models to adapt to semantic matching of industrial domain-specific vocabulary. Document lengths vary widely: some are short parameter tables with hundreds of characters, while others are thousands of words of market analysis. A flexible segmentation strategy is needed to avoid splitting the context of professional parameters. The non-fixed update rhythm requires indexes to support incremental updates, avoiding resource consumption from full reindexing. Additionally, the multi-field mixed document structure requires support for configuring vector recall weights by core fields, to avoid interference from non-key fields on retrieval results.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
EMBEDDING_MODEL | bge-large-zh-v1.5 | Adapts to semantic encoding of industrial domain terminology, matching the professional expression of automated equipment research reports |
chunk_size | 800–1200 characters | Covers core context of short parameter tables and long analysis documents, avoiding splitting the association between model numbers and parameters |
similarity_threshold | 0.72–0.85 | Filters low-match irrelevant results, adapting to the high matching requirements of professional terminology |
recall_top_k | Top 10–15 results | Balances retrieval efficiency and result coverage, adapting to medium-data-volume scenarios for automated equipment research reports |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Reserves sufficient time for long document parsing, complying with timeout limits of FastGPT open source version 4.8.17 and above |
INDEX_TYPE | HNSW | Supports fast incremental updates, adapting to the non-fixed update rhythm of research reports |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing values.
Three Common Misconfigurations
- Symptom: Retrieval results are irrelevant to the query, returning a large number of mismatched equipment parameter contents. Cause:
similarity_thresholdis set below 0.7, failing to filter low-similarity false match results. - Symptom: Index status shows not ready, and retrieval tasks cannot be triggered. Cause:
incremental_update_intervalis not configured, full index construction times out, orPARSE_FILE_TIMEOUT_SECONDSis set below 300 seconds, failing to complete long document parsing. - Symptom: The entire database multi-column table is directly used as a knowledge base index, resulting in low retrieval efficiency and redundant results. Cause: Core fields such as equipment model, core parameters and application scenarios are not screened, and non-key columns are not masked.
How to Verify Proper Configuration
- Upload a single automated equipment research report document, confirm that the parsing status shows success, with no field loss or parsing error prompts.
- Submit a test query containing professional terminology for this category, verify that the similarity scores of returned results fall within the preset threshold range.
- Submit an incremental update task, confirm that the number of newly added documents recorded in the index update log matches the actual submitted quantity.
- Export the current application's configuration file, check that it contains the configured vector model and index type parameters.
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.