What Data Looks Like for This Category
Data sources for iron and steel smelting business cover process specification documents, equipment operation logs, business handling forms (such as hot work approval, equipment maintenance application), annual maintenance plans, and other materials from iron smelting, steel making, and steel rolling links. Update frequencies are divided into real-time (equipment sensor data, temporary work approval), daily (process parameter summary), and weekly (process specification updates). Document structures include numeric fields (such as temperature, pressure, with units ℃ and MPa), text fields (such as process steps, responsible persons, approval opinions). There are both long documents with thousands of words and short forms with fewer than 100 words.
Constraints Imposed by These Characteristics on Vector Models and Indexes
Iron and steel smelting data includes both long process documents and short business forms. Vector chunking must balance context completeness and retrieval accuracy. The update frequency gap between real-time equipment data and bulk archived documents requires indexes to support both incremental and full update modes. Fields contain professional numeric values and process text. Embedding workflows must distinguish field weights to avoid non-business fields interfering with retrieval. Business handling processes follow a linear node structure. Index recall must prioritize matching process order. Large volumes of equipment log data create storage pressure on vector databases. Index sharding strategies must adapt to high-concurrency retrieval scenarios.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average length of iron and steel smelting process documents and approval forms, avoids losing process context due to overly fragmented chunks |
| Overlap Length | 100–150 characters | Connects process nodes across adjacent chunks, avoids missed retrieval across steps |
maxToken | 8000 | Supports context loading for long documents, meets recall requirements for complete process steps |
defaultToken | 2000 | Controls the output length of a single recall result, adapts to step-by-step display for business handling |
| Single Search Limit | Top 6 entries | Covers the number of nodes in a typical business process, avoids interference from redundant results |
| Similarity Threshold | 0.75–0.85 | Matches the precision of professional terminology for iron and steel smelting business, filters low-relevance non-process data |
Three Common Misconfigurations
- Issue: Custom vector database connection fails with a 500 status code. Cause: The server IP whitelist for FastGPT was not added to the vector database access configuration, leading to permission verification failure.
- Issue: Retrieval results are empty and knowledge base fields show no content after vector model configuration. Cause: Fields from iron and steel smelting business process documents and approval forms were not correctly mapped to the default fields of the FastGPT knowledge base, leading to loss of key business information during embedding.
- Issue: Index construction times out, with the task status remaining in running. Cause: Values for
defaultTokenandmaxTokenwere not adjusted, leading to excessive time spent on vector calculation for a single chunk, exceeding the system's default timeout limit.
How to Verify Successful Configuration
Upload a single hot work approval form for iron and steel smelting, run a retrieval test, and confirm the chunk length of recall results matches the configured chunk length. After configuring a custom vector database, click the test connection button, and confirm the interface shows a normal connection with no error prompts. Adjust the similarity threshold, and verify the relevance of retrieval results tightens as the threshold increases, aligning with business expectations. Check the knowledge base index logs in FastGPT v4.17.0, confirm there are no abnormal errors, and that the index status shows completed.
The values provided here are common starting points. They should be evaluated against the specific samples in use.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.