What This Type of Data Looks Like
Railway and highway investment research data primarily comes from road network operation ledgers, project completion archives, passenger and freight traffic monthly reports, line maintenance logs, industry policy documents, and feasibility study reports. Daily operational data is updated daily. Project archives and long-term maintenance plans are archived and updated upon completion or revision. Some industry policy documents are released irregularly. Document formats include structured tables (such as line parameter lists, traffic statistics sheets), long-text reports (such as line feasibility studies, maintenance plans), and semi-structured operation logs. Most fields include clear physical units: line mileage is measured in kilometers, traffic volume in passenger trips or tons, and maintenance costs in yuan.
Constraints on Vector Models and Indexing
The diversity of data formats and update patterns imposes specific constraints on the vector models and indexing workflow:
- Abundant structured table data requires support for generating independent vectors for individual fields. This prevents generic vector recall from losing precise field information from tables.
- Long-text reports and planning documents need controlled chunking granularity. This avoids cross-semantic chunking that breaks contextual connections of professional content.
- Differentiated update rhythms require the indexing system to use incremental update strategies. It must distinguish update frequencies between high-frequency operational data and low-frequency project archives, reducing resource overhead from full index rebuilds.
- Fields with physical units require vector models to recognize unit-related semantics. This prevents recall result deviations caused by differences in unit wording.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | Doubao-embedding-large | Covers professional terminology for railway and highway engineering and operations, supports custom request address and apikey configuration |
chunk_size | 800–1200 characters | Balances semantic completeness of long document chunks and indexing density, adapts to text lengths of feasibility study reports and maintenance plans |
table_vector_enable | Enabled | Generates separate vectors for each field for structured data such as railway operation tables and project lists, improving recall accuracy for structured information |
index_incremental_strategy | Incremental synchronization by update time | Adapts to differentiated update rhythms of daily-updated operational data and quarterly-updated project archives, reducing resource consumption for index updates |
recall_top_k | Top 8–12 results | Covers multi-dimensional information required for railway investment research, avoiding insufficient recall or redundant results |
similarity_threshold | 0.72–0.78 | Filters low-semantic-match results, adapts to recall accuracy for professional domain terminology, reducing interference from irrelevant documents |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material formats, data volumes, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Symptom: After enabling the
Doubao-embedding-largemodel in knowledge base settings, entering a custom request address and apikey, and clicking test, a401 Unauthorizederror is returned. Cause: The apikey has not been granted permissions, or the custom request address is configured incorrectly and does not point to a compliant vector service endpoint. - Symptom: The knowledge base is configured correctly and the agent test passes, but after refreshing the page, a prompt stating "No available index model detected" appears. Cause: The vector model configuration was not saved to the index cluster associated with the knowledge base, or the service status of the index cluster has not been synchronized to the front-end cache.
- Symptom: After enabling multi-vector support for railway table data, the recall results do not include table field information. Cause: The vector generation option for table fields was not checked, or the structured fields of the table were not correctly identified during document parsing.
How to Verify Successful Configuration
- Navigate to the vector model configuration page in the knowledge base settings. Confirm that the target model is selected, and that the custom request address and apikey fields have no empty values.
- Upload a railway operation table document that includes line parameters and traffic statistics. Check whether separate table field vector entries are generated in the parsing results.
- Initiate a test query. Enter a professional question that includes line mileage and traffic volume. Check whether the recall results include matching structured data.
- Wait 10 minutes, then refresh the knowledge base page. Confirm that the "No available index model detected" prompt no longer appears.
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.