What data for this category looks like
Specialized equipment investment research data mainly comes from industry association public reports, manufacturer official product manuals, patent databases, bidding announcements, and supply chain research materials. The update rhythm changes flexibly with new product launches, industry policy adjustments, and quarterly industry data updates, with no fixed cycle. Document structures include structured parameter tables, long-form technical descriptions, application cases, and bid winning announcement information. Fields cover equipment model, rated power, rotational speed, manufacturer, certification number, commissioning date, and some fields are attached with standard units of measurement.
What constraints these characteristics impose on the "knowledge base retrieval and recall" link
The existence of structured parameter tables and multiple units of measurement requires the retrieval link to support field matching and unit normalization, to avoid matching failures caused by inconsistent units. Long-form technical descriptions and scattered parameter information require retaining context association during segmentation, to prevent splitting of technical logic. Sudden data with no fixed update cycle requires the recall link to support incremental indexing, to avoid resource consumption caused by full reindexing. The timeliness requirement of bidding announcements requires prioritizing newly published content during retrieval, while also meeting the retrieval needs for historical data.
How to set the configuration
| Configuration Item | Recommended Approach | Basis for This Setting |
|---|---|---|
embedding_model | text-embedding-3-large | Specialized equipment data contains professional technical terms and structured parameters. This model has higher matching accuracy for professional semantics |
chunk_size | 1000–1500 characters | Specialized equipment documents include long technical descriptions and parameter tables. Segments that are too long will split context association, while segments that are too short will lose logical connections between parameters |
recall_top_k | Top 8–12 results | Investment research scenarios need to cover multi-dimensional equipment parameters and application cases. Too many results will increase context processing load, while too few will miss key information |
rerank_top_n | Top 3–5 results | Low-relevance recall results need to be filtered, to focus on core technical parameters and application scenario content |
parse_table_enable | Enabled | Specialized equipment documents contain a large number of structured parameter tables. Enabling this allows extraction of table fields for precise field matching retrieval |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Large equipment product manuals include multi-page technical content. The default timeout is insufficient to complete the parsing and vectorization process |
The parameter values provided on this page are common recommendations used as a starting point 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 errors
- Phenomenon: After replacing
embedding_modelwithtext-embedding-3, the previously imported knowledge base cannot directly reuse the vector indexes generated by the old model, and a full re-import is required. Reason: Vector indexes are bound to the embedding model. When the incremental indexing function is not enabled, it is impossible to generate indexes compatible with the new model based on the vectors of the old model. - Phenomenon: When using PostgreSQL to store vector data, the parameter matching accuracy of retrieval results is lower than expected, and some accurately matched equipment parameters are not recalled. Reason: The vector extension plugin for PostgreSQL is not enabled. Only relying on full-text search cannot achieve semantic vector matching, and the retrieval efficiency and accuracy of native vector databases are more suitable for professional equipment data.
- Phenomenon: When importing a device manual with more than 500 pages, the task fails and returns a
504 Gateway Timeouterror. Reason: ThePARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default timeout is insufficient to complete the parsing and vectorization process of long documents.
How to confirm the configuration is correct
- Perform a parsing test on a single device parameter document, and check whether the segment length of the parsed text meets the preset
chunk_sizerange. - Trigger an incremental indexing task, and verify that when only new documents are added, the vector index can be generated normally without re-importing all historical data.
- Initiate a retrieval request containing equipment parameter keywords and units, and check whether the recall results include accurately matched field content.
- View the index status of the vector database, and confirm that the dedicated index for structured parameter fields has been correctly created.
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.