What the Data for This Category Looks Like
Coal transportation and sales documents and materials mainly come from transportation and sales contracts, weighing slips, settlement statements, monthly transportation and sales plans, railway/port shipping ledgers, and similar sources. Data update rhythms fall into three categories:
- Immediate update after a single business document is generated.
- Daily ledger data synchronized per shipping batch.
- Monthly settlement data summarized and updated after the current month’s business concludes.
Single documents have a fixed structure, including fields such as shipper, consignee, coal type, calorific value, tonnage, freight rate, settlement amount, contract number, and shipping date. Corresponding units for these fields include tons, kcal/kg, yuan, yuan/ton, and similar units.
Constraints Imposed on Vector Models and Indexing
The multi-field structure and specialized terminology of coal transportation and sales documents require vector models to accurately identify industry-specific concepts such as calorific value and ash content. This avoids semantic matching deviations. Data sources with different update rhythms require indexing strategies to distinguish trigger timing between incremental updates and full updates. This adaptation supports immediate, daily, and monthly update frequencies. Single documents vary widely in length, from short weighing slips of a few dozen characters to long contract clauses of thousands of characters. Index chunking strategies must accommodate text of different lengths, avoid splitting specialized terms, and preserve contextual associations.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Covers the complete content of most single fields in coal transportation and sales documents, avoids splitting cross-field specialized terms |
| Overlap Length | 100–150 characters | Retains contextual information of adjacent chunks, prevents key indicators such as calorific value and freight rate from being truncated by chunking |
| Similarity Threshold | 0.72–0.78 | Specialized terminology in coal transportation and sales documents has high similarity differentiation, this range effectively filters irrelevant matching results |
| Single Search Limit | Top 8 entries | Retains sufficient candidate entries for subsequent reranking, while controlling retrieval overhead |
maxToken | 8192 token | Adapts to the context limit of the default large language model in FastGPT, meets processing requirements for long contract texts |
defaultToken | Fill in according to the key provided by the third-party channel | Binds the embedding model channel for the configuration, ensures correct call permissions |
Three Common Misconfigurations
- Symptom: The vector model call returns the error "This token is not authorized to use the model: text-embedding-3-large". Cause: The embedding model adapted for coal transportation and sales professional text is not configured as the default model, and an unauthorized model interface is still used.
- Symptom: Knowledge base search response time exceeds 20 seconds. Cause: The chunk length is set too small, resulting in far more index chunks than actual demand, requiring traversal of too many entries during retrieval.
- Symptom: A prompt indicating no available channel appears after configuring the third-party embedding channel. Cause: The
defaultTokenparameter corresponding to the channel is not filled in correctly, or the channel name does not match the configuration on the third-party platform.
How to Confirm Correct Configuration
- Upload a standard coal transportation and sales contract. Check whether the embedding model call record in the index generation log matches the configured target model.
- Initiate a retrieval request targeting the fields of the transportation and sales ledger. Verify that the matching logic of the returned results conforms to the professional characteristics of coal transportation and sales documents.
- View the index statistics of the vector database. Confirm that the correspondence between the number of chunks and the chunk length meets expectations.
- Simulate multiple concurrent retrieval requests. Check whether the response time falls within an acceptable range, and adjust the configuration according to actual business scenarios.
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.