What the Data for This Category Looks Like
Carbon steel financial report data is primarily sourced from periodic reports disclosed by domestic and overseas stock exchanges, and public statistical materials from industry associations. Updates follow fixed quarterly, semi-annual, and annual cycles, with temporary performance announcements and capacity adjustment updates released as needed. Document structures include consolidated financial statements, detailed revenue and cost breakdowns by business segment, capacity and production statistics, and management discussion and analysis paragraphs. Fields include revenue, net profit, production capacity, unit product cost, and others, paired with standard industry-specific units. Documents primarily combine structured tables and long-form text analysis, and contain a large number of industry-specific terms and business-related data.
Constraints Imposed on Vector Models and Indexing
The multi-structured tables and industry-specific terms in carbon steel financial reports require vector models to support long-text segmentation and domain semantic encoding, to avoid losing business context associations after segmentation. Fixed-cycle batch update requirements mean indexing configurations must support incremental updates and batch reindexing, reducing computational overhead from repeated full imports. The presence of multi-dimensional business fields requires indexing to support cross-field associated recall, while adapting to joint matching logic for structured numerical values and unstructured text, to improve the accuracy of search results.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | text-embedding-3-large | Adapts to carbon steel industry-specific terms and long-text segmentation for semantic encoding, provides higher-dimensional vector representations to improve financial report text matching accuracy |
chunk_size | 1200–1500 characters | Preserves complete context for individual financial statements or business analysis paragraphs, avoiding loss of business association logic after segmentation |
chunk_overlap | 150–200 characters | Connects contextual information between adjacent segments, ensuring semantic coherence of long documents and avoiding semantic breaks caused by segment boundaries |
index_refresh_strategy | incremental batch update | Adapts to the fixed-cycle update rhythm of carbon steel financial reports, reducing time overhead from full index reconstruction |
retrieve_top_k | top 10–15 results | Covers multi-dimensional business data and analysis content in financial reports, avoiding missed key matches due to too few recalled entries |
structured_field_embedding | enable field association mapping | Adapts to structured numerical fields in financial reports, converts financial indicators into encodable text descriptions, enabling joint recall across semantic and numerical dimensions |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Timeout errors occur when calling the knowledge base interface to create an index, or indexing tasks for a single financial report take significantly longer than expected. Cause: The
chunk_sizeandchunk_overlapparameters are not adjusted for long documents, resulting in an excessive number of segments and increased computational load for vector encoding and index construction. - Symptom: After replacing the
embedding_model, the relevance of search results from the already imported knowledge base decreases significantly. Cause: Vector reindexing of old data is not performed, and the original vector space does not match that of the new model, making semantic alignment impossible. - Symptom: Knowledge base search results are not sorted by business semantic relevance, with some low-match content appearing at the top of the list. Cause: The
structured_field_embeddingconfiguration is not enabled, relying solely on pure text semantic recall without associating structured indicator fields in financial reports, leading to deviations in sorting logic.
How to Verify Proper Configuration
- Run an indexing task for a single typical financial report, confirm that the number of segments matches the preset
chunk_size, with no over-short segments caused by excessive splitting. - After replacing the
embedding_model, trigger a batch reindexing operation, verify that the semantic matching accuracy of search results meets business expectations. - Check the index construction logs to confirm that the
structured_field_embeddingconfiguration is active and structured fields are correctly encoded into vectors. - Test multi-keyword combined searches, confirm that the returned results are sorted consistently with business semantic relevance.
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.