What the data for this category looks like
Special steel research reports primarily come from industry association public reports, internal technical documents from steel mills, securities firm industry research reports, and professional metallurgical journals. Update frequency varies by content type: industry policy-related reports are updated alongside policy releases, steel mill dynamic reports are updated in real time with fluctuations in production capacity and prices, and technical parameter reports are updated irregularly alongside process iterations. Documents typically include modules such as special steel grades, chemical compositions, mechanical properties, production processes, market supply and demand, and price trends. They contain a large number of structured tables with units, including fields like yield strength (MPa), carbon content (%), delivery status, and terminal application fields.
What constraints do these characteristics impose on the vector models and indexing link
The mixed structure of structured parameters and unstructured text in special steel research reports requires vector models to support both structured field encoding and long-text semantic understanding. The non-fixed update frequency requires the indexing system to support incremental updates, only processing new or updated data to avoid redundant calculations for already processed data. The document structure with embedded tables requires indexing tools to extract structured data from tables separately to generate vectors, preventing semantic discontinuity. The unified field unit requirement requires the indexing process to retain unit associations, preventing parameters with different units from being mistakenly identified as similar content.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Special steel research reports contain long process descriptions and associated structured parameter blocks. This length can fully cover the context of a single set of technical parameters |
chunk_overlap | 100–150 characters | Prevents structured parameters from being lost across segments, and retains the semantic association between adjacent parameters and process descriptions |
vector_store_type | FAISS with IVF index | The vector dimension of special steel research reports typically ranges between 768 and 1536. The IVF index balances retrieval speed and recall accuracy |
top_k | Top 10–15 results | Professional terminology is dense in special steel niche scenarios, so a sufficient candidate set must be retained for subsequent semantic reranking |
incremental_index_enabled | Enabled | Adapts to non-fixed frequency data updates for steel mill dynamics and policy updates, reducing the time cost of full indexing |
extract_table_fields | Enabled | Special steel research reports contain a large number of performance parameter tables. Enabling this option extracts structured fields to generate vectors separately, improving retrieval matching accuracy |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material forms, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: An error indicating the model is not supported is returned when calling a domestic vector model, and the retrieval process is interrupted. Cause: The
embedding_modelconfiguration item was not replaced with the identifier of the corresponding domestic model, or the access key and endpoint address of the model were not configured. - Phenomenon: After adding or deleting research report data directly through MongoDB, the vector retrieval results are not updated synchronously. Cause: The
incremental_index_enabledconfiguration was not enabled, and modifying the business database directly did not trigger the vector embedding and index rebuilding process. - Phenomenon: A 403 Forbidden error is returned when FastGPT calls a vector model deployed via ollama, but curl testing can normally generate vectors. Cause: The local access authorization parameters for ollama were not added to the FastGPT vector model configuration, or the proxy port and request headers were not configured correctly.
How to Confirm the Configuration is Correct
- Upload a special steel research report containing structured parameter tables, check if the parsed segments fully cover a single set of technical parameters, and verify the actual effect of the
chunk_sizeconfiguration. - Perform an incremental index test: add a new special steel research report, then verify that the new document can be normally recalled through the retrieval interface, confirming that the
incremental_index_enabledconfiguration is in effect. - Call the vector model test interface, input special steel professional terminology such as "GCr15 bearing steel yield strength", verify that the returned vector results meet expectations, and confirm that the model configuration and access permissions are normal.
- Adjust the
similarity_thresholdparameter, observe changes in the number of recalled retrieval results, and confirm that the threshold configuration can normally filter low-match results.
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.