What Does the Data for This Category Look Like?
The data sources for small metal marketing content include monthly supply and demand reports from non-ferrous metal industry associations, real-time market data from spot trading platforms, product specifications and marketing copy from manufacturing enterprises. Update frequencies cover daily (spot market data), weekly (industry updates), and on-demand (marketing materials). Document structure includes fields such as category identifiers, origin information, performance parameters, application scenarios, and market supply and demand overview. Quotation data uses the unit yuan per kilogram, while production capacity data uses the unit tons per year. Individual document lengths range from short copy of several hundred characters to industry research reports of several thousand characters.
What Constraints Do These Characteristics Impose on Vector Models and Indexing?
Small metal marketing content mixes structured parameters and unstructured analytical text. Generic vector models tend to produce semantic encoding deviations, so targeted adjustments to preprocessing logic are required. Daily updated spot market data requires indexes to support incremental updates, avoiding delays caused by full reindexing. Large differences in document lengths require flexible text chunking rules, preventing short copy from being over-split and long documents from losing semantic integrity. The variety of field units also requires unified text preprocessing before vectorization, to ensure consistency in vector semantics.
How to Configure the Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CHUNK_SIZE | 800–1200 characters | Small metal marketing documents include both short copy and long research reports. This range balances semantic integrity and vector dimension redundancy. |
CHUNK_OVERLAP | 100–150 characters | Structured parameters such as quotation and production capacity often appear at document start and end positions. Overlap prevents these parameters from being truncated. |
VECTOR_MODEL | text-embedding-ada-002 or bge-large-zh-v1.5 | These models deliver balanced semantic encoding performance for industrial category parameters and marketing text. |
RECALL_TOP_N | Top 5–8 results | Small metal marketing content has high demand for precise matching. Too many recall results introduce irrelevant industry redundant data. |
SIMILARITY_THRESHOLD | 0.75–0.85 | This range filters general metal text unrelated to the small metal category, retaining highly relevant matching results. |
INDEX_REFRESH_INTERVAL | 1 hour | Spot market data updates daily. A 1-hour refresh balances index delay and computing resource usage. |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing the settings.
Three Common Misconfigurations
- Symptom: After running knowledge base vectorization, no new vector files appear in the backend disk, or a storage path error is displayed. Cause: The vector database storage path is not specified in the FastGPT configuration, or the path permissions are insufficient, resulting in failure to persist vectorization results.
- Symptom: Disk usage of locally deployed FastGPT continues to grow, with no clear incremental source found. Cause: The
UPLOAD_FILE_MAX_SIZEparameter is not limited, and automatic cleanup of vector chunks is not enabled, resulting in a large number of redundant original files, split chunks, and embedded vectors generated by repeatedly uploaded small metal marketing documents. - Symptom: After initiating a query for the small metal category, the recall results include a large amount of general metal content unrelated to the target category. Cause: The
SIMILARITY_THRESHOLDparameter is not set, or the threshold is set too low, causing the model to misclassify semantically similar general metal text as matching results.
How to Confirm Proper Configuration
- Upload a small metal spot market document, check the vectorization task logs, confirm there are no "encoding failed" errors, and that the generated vector dimensions match the standard dimensions of the selected model.
- Enter the vector database management interface, check the file size changes in the storage path, confirm that the number of new vector files matches the number of uploaded documents, with no redundant duplicate chunks.
- Initiate a query for the small metal category, check that the number of recall results matches the set
RECALL_TOP_Nvalue, and that all results include small metal-related parameters or scenarios. - Wait for the duration specified by
INDEX_REFRESH_INTERVAL, then upload a new spot market document, confirm that the index updates automatically without requiring manual full reindexing.
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.