What the Data for This Category Looks Like
Data comes from daily declaration documents and archived files across all links of railway operations. This includes passenger and freight transport declaration forms, train dispatching applications, qualification review files, and more. Data updates trigger with business processes: new data is generated when a single transaction is submitted. Archived files are organized in quarterly batches. Documents mostly combine structured forms and unstructured attachments, with fields such as declaration number, business type, submission time, and review node records. Units involved include train number, freight bill number, piece count, and tonnage, among others.
Constraints Imposed on Vector Models and Indexing
Mixed structured and unstructured data structures require vector models and indexes to support multi-modal content chunking and encoding. This prevents over-compression of identification information in structured fields. Coexistence of real-time updates and batch archiving requires indexes to support dual-mode switching between incremental refresh and full reconstruction. This adapts to update requirements for different business scenarios. Specific unit fields such as train number and freight bill number require vector models to retain semantic associations between units and business identifiers during encoding. This avoids semantic confusion across businesses. A single file may contain multiple attachments. Chunking strategies must split content by business units to ensure semantic integrity of associated content.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average text length of forms and attachments in railway declaration materials, avoids overloading single chunks |
| Overlap Length | 100–200 characters | Retains cross-chunk context association, adapts to semantic integrity of coherent text such as review comments |
maxToken | 8192 token | Matches the context window limit of most open-source embedding models, compatible with long-text attachments in railway files |
| Similarity Threshold | 0.72–0.80 | Distinguishes valid review associations from irrelevant business records, adapts to precise matching requirements of railway business |
| Single Search Limit | Top 20 entries | Covers the number of associated review records for a single file, avoids result redundancy |
defaultToken | 1024 token | Unifies the encoding length of short-text fields, ensures semantic consistency of identifiers such as declaration numbers |
Three Common Mistakes
- Phenomenon: Knowledge base search takes more than 30 seconds, and the interface displays a loading timeout. Cause: The single search limit is not adjusted for the batch data of railway files. This leads to too many redundant results being recalled in a single request, increasing vector retrieval latency.
- Phenomenon: After configuring the embedding model channel, the interface prompts that no vector channel is available. The log returns a 400 error. Cause: The input token length of the embedding model is not verified to match the
maxTokenconfiguration. Railway long-text attachments exceed the model's supported limit, causing the request to fail. - Phenomenon: Irrelevant train number declaration materials appear in the associated results of review files. Cause: The similarity threshold is not set, or the threshold value does not meet business matching requirements. This results in recall of low-relevance business records.
How to Verify Proper Configuration
- Upload a standard railway freight declaration form. Check whether chunked text fragments retain key unit information such as freight bill number and tonnage.
- Trigger an incremental index refresh. Check whether the index progress log distinguishes the update rhythm between real-time business and archived files.
- Call the retrieval interface. Verify that the number of returned results matches the single search limit configuration.
- Test the encoding request for long-text attachments. Confirm that no error prompts related to
maxTokenare triggered.
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.