What the data for this category looks like
Data sources for internal office and employee services in coal transportation and sales come primarily from internal OA systems, sales and transportation business ERPs, weighing management systems, and employee training platforms. Data types include structured business documents such as sales and transportation plan sheets, settlement sheets, and weighing sheets; unstructured documents such as internal notifications, job manuals, and training courseware; and employee service content such as attendance rules and salary explanations. Structured documents include fields like contract number, coal type, shipment tonnage, and settlement unit price, with units including tons, yuan per ton, and yuan. Unstructured documents are mostly paragraph-length natural language, some containing professional terms for coal transportation and sales. Business documents update in real time alongside sales and transportation workflows. Internal notifications release as needed. Training documents and salary rules update quarterly or annually.
What constraints these characteristics impose on vector models and indexing
Coal transportation and sales business data includes both structured fields and industry-specific professional terms. This requires vector models to balance associated matching of structured information and semantic understanding of natural language. Real-time updated business documents and on-demand internal notifications require indexes to support flexible switching between incremental updates and batch rebuilding. Document lengths vary widely, from weighing sheets of a few hundred characters to training courseware of several thousand characters. Chunking processing must adapt to different lengths. Professional terms such as "washed coal" and "free on board price" require vector models to recognize industry term semantics, avoiding mismatches of similar expressions. Internal office scenarios require fast location of core business information. Index retrieval results must balance relevance and business relevance.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the common length of coal transportation and sales business documents, avoid splitting core business fields in a single chunk |
| Overlap Length | 100–150 characters | Retain cross-chunk business association information, such as multiple sections of document content linked to a contract number |
| Similarity Threshold | 0.72–0.85 | Distinguish similar expressions of coal transportation and sales professional terms, filter low-relevance search results |
| Single Search Limit | Top 10 entries | Adapt to the quick search needs of internal office scenarios, reduce employee screening costs |
| maxToken | 4096 | Adapt to the input limits of mainstream vector models, cover the total context of sales and transportation documents and training documents |
| defaultToken | 2048 | Process employee service documents of conventional length, such as attendance explanations and job manuals |
Three Common Misconfigurations
- Phenomenon: A 504 timeout status code returns when calling the knowledge base interface to generate an index. Cause: The chunk length is not adjusted for long coal transportation and sales business documents. Excessively large single chunk content causes vector generation and index construction time to exceed interface limits.
- Phenomenon: After replacing the vector model, previously imported knowledge bases cannot reuse the original index. Cause: Batch reindexing is not performed. Vectors of original documents are not updated synchronously with the new model. Only newly imported documents generate vectors adapted to the new model.
- Phenomenon: Search result sorting does not meet business expectations. Documents with high similarity to professional terms do not rank first. Cause: Weights are not configured for core coal transportation and sales business fields such as coal type and contract number. General similarity calculation does not prioritize matching core business information.
How to Confirm Configurations Are Set Correctly
- Upload a single 1000-character coal transportation and sales settlement sheet. Check if chunked content fully retains core fields including contract number, coal type, and tonnage.
- Call the knowledge base interface to import 10 historical sales and transportation documents. Check if index generation time meets business scenario expectations.
- After replacing the vector model, perform a batch reindexing operation. Verify that search results for original documents align with the new model’s semantic matching logic.
- Enter the professional term "washed coal". Check if search results preferentially return business documents containing this term.
The values provided are common starting points and should be measured against the reader's own samples.
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.