What the data for this category looks like
Financial marketing data related to railways and highways is primarily sourced from ticket settlement systems, real-time passenger flow monitoring platforms, line operation logs, along-route advertising delivery backends, and financial cooperation promotion logs. Data update rhythms fall into two categories: real-time updates for ticket and passenger flow data, and daily updates for line logs and delivery data. Document structures are mostly structured tables and semi-structured reports, containing fields such as line number, station name, passenger flow time period, delivery channel, conversion amount, operating mileage, and cooperating financial product identifier. Units include passenger trips, yuan, kilometers, and others.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Real-time requirements mandate that multi-turn dialogue limit the context window to avoid introducing outdated passenger flow or financial delivery data. The structured nature of multiple fields requires prompts to explicitly specify mapping rules between financial product identifiers and passenger flow data, preventing field confusion in generated marketing content. Regularly updated documents require prompts to restrict retrieval to valid delivery data from the last 7 days, avoiding the use of outdated cooperating product information. The segmentation requirements of semi-structured reports require document parsing in dialogue scenarios to match fixed segment lengths, ensuring the completeness of retrieved content and precise matching of financial marketing scripts to passenger flow scenarios.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | First 8 turns of dialogue | Railway and highway financial marketing data focuses on passenger flow, lines, and delivery. Excessive context introduces redundant historical data |
embedding_model | text-embedding-3-small | Adapts to structured financial marketing data with multiple fields, balances vector index accuracy and indexing speed |
RECALL_TOP_N | Top 6 recall results | Financial marketing content matching requires balancing precision and coverage. 6 results cover major commuter passenger flow scenarios |
SIMILARITY_THRESHOLD | 0.72 | Filters low-correlation passenger flow or delivery data to avoid interfering with financial marketing script generation |
PARSE_CHUNK_SIZE | 800–1200 characters | Railway and highway financial marketing documents often include line descriptions and delivery reports. This segment length preserves complete business logic |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Parsing large financial delivery report files requires extended time to avoid timeout interruptions |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: When using the
meta-llama-3.1-8b-instructmodel, the dialogue process runs normally but vector index construction progress stalls. Cause: No vector model adapted to structured financial marketing data is specified, causing index tasks to fail to properly parse financial cooperation identifiers and passenger flow fields. - Phenomenon: Uploaded financial delivery report files cannot be read in dialogue scenarios, but the knowledge base upload process completes normally. Cause:
UPLOAD_FILE_MAX_SIZEis not configured to adapt to large report files, or the dialogue file upload permission in the deployment scenario is not bound to the marketing data directory. - Phenomenon: The knowledge base test can retrieve target line financial delivery data, but retrieval results are empty in dialogue scenarios. Cause:
SIMILARITY_THRESHOLDis set too high, filtering valid delivery data weakly correlated with dialogue context.
How to Confirm Configuration Is Complete
- Initiate a multi-turn dialogue including historical passenger flow queries, line recommendations, and financial delivery effect analysis, verify that returned content only relates to valid marketing data from the last 7 days.
- Upload a single financial delivery report file larger than 50MB to the dialogue scenario, confirm that the file can be parsed normally and participate in the retrieval process.
- Adjust
SIMILARITY_THRESHOLDto the preset range, compare the consistency of retrieval results between the knowledge base test and dialogue scenarios. - Trigger a full vector index construction task for marketing documents, check that the task progress bar updates normally with no stalls or interruptions.
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.