What the Data for This Category Looks Like
Marketing content for special steel targeting financial and wealth management sectors draws data from multiple sources. These sources include internal enterprise product technical manuals, customized quotation documents, industry application case files, dealer promotional materials, and investment value explanation documents for wealth management clients. Core information such as product grades and mechanical performance parameters appears in structured tables, with attached units including MPa and mm. Application scenario descriptions are mostly long text passages that include adaptation notes for specific operating conditions. Product model update cycles range from quarterly to semi-annual. Quotation data updates monthly. New investment value documents are generated as needed. Documents contain repeated parameter fields and technical terms, such as grade identifiers for high-strength alloy steel.
Constraints Imposed by Data Characteristics on Vector Models and Indexing
The structured parameters and technical terms in special steel marketing content require vector models adapted to industrial domain semantic encoding. This avoids semantic drift from general-purpose models for specialized terms. The mixed structure of long-text application scenarios and structured tables requires segment lengths to balance context completeness and retrieval efficiency. This prevents breaking of parameter associations. Differences in update frequencies across data types require indexes to support incremental synchronization rules configured by data type. This avoids inefficient full index refreshes. A large number of repeated parameter fields requires indexes to include built-in deduplication rules. This reduces redundant vector storage and retrieval overhead.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | bge-large-zh-v1.5 or text-embedding-v3 | Supports encoding of industrial domain technical terms, adapts to structured parameters and application scenario descriptions in special steel documents |
chunk_size | 800–1200 characters | Balances context completeness for long-text application scenarios and parameter associations for structured tables, avoids splitting parameters into separate segments |
recall_top_k | Top 10–15 results | Focuses on matching core special steel grades and application scenarios, avoids excessive irrelevant results interfering with retrieval accuracy |
index_refresh_interval | 1 day | Aligns with the monthly update cycle for special steel quotation data and quarterly update cycle for product models, balances synchronization timeliness and index overhead |
rerank_top_k | Top 5–8 results | Filters redundant similar retrieval results, focuses on the most relevant special steel parameter and application information for user queries |
embedding_request_timeout | 60 seconds | Adapts to encoding requirements for long segmented special steel documents, avoids interrupting vector generation requests due to timeout |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material forms, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- An interface prompt displays "No available vector model channels". This occurs when the API key and interface address for the corresponding embedding model are not added in the model provider configuration, or the current knowledge base is not authorized to access the model group.
- A 504 timeout error is returned for vector requests. This occurs when the
embedding_request_timeoutvalue is set too short to handle the encoding process for long segmented special steel marketing documents. - The number of retrieval results does not match the set value, and retrieval latency is too high. This occurs when
chunk_sizeis set too small, leading to an excessive number of segments, expanding the index scan range and increasing retrieval overhead.
How to Confirm Proper Configuration
- Access the model provider management page, check that the API keys and interface addresses for configured embedding models are valid, and confirm the model has been added to the current knowledge base's group permission list.
- Upload a special steel marketing document containing a structured parameter table, verify that the parsed segments match the
chunk_sizesetting, with no obvious splitting of parameter fields or text breaks. - Initiate a retrieval for a special steel grade or application scenario, check that the number of returned recall results matches the
recall_top_ksetting, and that the reranked results focus on core matching content. - Wait one day and retrieve updated special steel documents again, confirm that the incremental index has synchronized the latest quotation or product information, and that retrieval results include the latest content.
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.