What Data Looks Like for This Category
Data for refractory material enterprise financial reports comes from publicly released annual and quarterly financial reports, as well as industry operation statistics published by industry associations. Update frequency follows: annual reports are released once per year, quarterly reports are updated each quarter, and temporary announcements are released as needed. Document structure includes core financial statements, operating performance analysis, production capacity and sales volume details, raw material and cost breakdowns, and other sections. Fields cover production volume, sales volume, unit selling price, raw material procurement costs, R&D investment, and more. Common units include tons, yuan, ten thousand yuan, and similar units.
What Constraints These Characteristics Impose on Vector Models and Indexing
Refractory material financial reports contain structured production capacity, sales volume and cost values, plus unstructured operating analysis text. Vector models must adapt to vectorization requirements for mixed content. Single annual financial report documents have significant length. A reasonable chunking strategy avoids exceeding model context window limits, while preserving associations between financial data and analysis content. Demand for comparative analysis across multiple report periods requires indexes to support cross-document temporal correlation retrieval. Combined retrieval of industry data and enterprise financial reports requires indexes to support mixed storage and correlation matching for multi-source data.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Refractory material financial report paragraphs contain financial details and analysis content. This length balances context completeness and vector dimension efficiency |
chunk_overlap | 100–150 characters | Financial data and analysis content in financial reports have cross-paragraph associations. Overlap preserves context coherence |
vector_batch_size | 4–8 | Refractory material financial reports have a relatively large number of chunks. Batch processing reduces API call latency and adapts to batch chunk vectorization requirements |
retrieve_top_k | 3–6 results | Financial report analysis requires combining multi-period data and detailed items. A small number of highly relevant results improves retrieval accuracy |
similarity_threshold | 0.75–0.85 | Financial report data has relatively high semantic consistency. This threshold filters low-relevant historical data or industry data |
embedding_model | Prioritize Alibaba multimodal-embedding-v1 | Adapts to mixed text and numerical description content in refractory material financial reports, and covers vectorization requirements for multiple types of fields |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Vector service call logs show only a single text slice is submitted, and batch processing mode is not triggered. The cause is that FastGPT 4.8.10 version does not enable batch vectorization configuration by default, and only submits vectorization requests for single-paragraph text.
- Calling the
get_chunk_indexinterface fails to obtain the complete content of the chunk index. The cause is that thedoc_idandchunk_indexfields are not retained during chunking, resulting in loss of index association information. - After adding the Alibaba multimodal-embedding-v1 model, the vectorization task returns a
500status code or aninvalid modelerror message. The cause is that the API key and endpoint address for the corresponding model are not added in the vector service configuration, or the input format requirements of the model are not adapted.
How to Confirm the Configuration Is Complete
- View the vector service call logs, and check whether the number of submitted text slices matches the number of document chunks.
- Perform a retrieval test, pass a financial report-related query statement, and check whether the returned chunk content contains complete financial fields and analysis text.
- In the vector model configuration interface, confirm that the API parameters of the target model are correctly configured, and test whether a single chunk's vectorization task can be completed normally.
- View the index metadata, confirm that multiple chunk indexes of the same document are associated and stored, and that all relevant chunks of the corresponding document can be returned during retrieval.
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.