What the Data for This Category Looks Like
Financial institutions use multiple data sources for tourist attraction intelligent due diligence, including official attraction operational ledgers, filing documents from cultural and tourism authorities, passenger flow monitoring system logs, merchant cooperation contracts, annual operating reports, and more. Update rhythms vary significantly: operational data such as passenger flow and revenue is updated daily or weekly, annual operating reports are updated per calendar year, and compliance filing documents are only updated when qualifications change. Document structures include standardized fields: basic attraction information (name, rating, geographic location), passenger flow data (number of visitors), revenue data (ticket sales, secondary consumption amounts), compliance document numbers, merchant lists (name, rent, contract term), and more. Some documents are unstructured scanned files or long-text reports.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Since data sources for tourist attraction due diligence used by financial institutions are scattered and update rhythms differ widely, multi-turn dialogue must first guide users to clarify query dimensions, avoid confusion between passenger flow, revenue and compliance-related questions, and align with the standardized audit logic of financial due diligence. Data with different update frequencies requires distinct calling logic in prompts. For example, real-time passenger flow data must link to the latest monitoring logs, while annual revenue data must specify the query period. The standardized features of multiple fields require prompts to clearly state field units to prevent confusion between "visits" and "yuan". For unstructured compliance documents, dialogue must guide users to provide specific document numbers or qualification types to accurately locate required content.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Attraction due diligence data includes multiple long-text ledgers and multi-dimensional reports; a long context is needed to accommodate multi-turn conversation history and retrieved knowledge base content |
recallTopK | Top 8–12 entries | Attraction data fields cover multiple dimensions including passenger flow, revenue and compliance; enough entries must be retrieved to cover complete query requirements |
similarityThreshold | 0.72–0.78 | Attraction data has duplicate merchant names and unified statistical calibers; low-correlation retrieved results must be filtered |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Annual attraction operating reports usually include multi-page passenger flow details; parsing takes longer than general documents |
UPLOAD_FILE_MAX_SIZE | 500 MB | Compliance files and passenger flow database files filed by attractions are generally large in size |
chunkSize | 1000–1500 characters | Long-text operational reports for attractions require proper segmentation to avoid context breaks that harm retrieval effectiveness |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require targeted analysis; it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Symptom: After local deployment and uploading attraction due diligence files, the data processing step displays as empty. Cause: The uploaded file exceeds the
UPLOAD_FILE_MAX_SIZEconfiguration threshold, or the file uses an encrypted format that cannot be parsed. - Symptom: Knowledge base search debugging works normally in the workflow, but the AI dialogue stage does not reference attraction due diligence data. Cause: The
similarityThresholdis set too high, filtering eligible attraction data entries, or the workflow node is not bound to the corresponding attraction due diligence knowledge base. - Symptom: No historical records of attraction due diligence conversations are found in the MongoDB database. Cause: The
enableChatHistoryconfiguration item is not enabled, or the database connection string is configured incorrectly, preventing logs from being written.
How to Verify Configurations Are Properly Set
- After uploading a single attraction due diligence file, review the parsing results in the data processing interface to confirm that all fields are fully extracted and no abnormal errors occur.
- Initiate a query with multiple follow-up questions to verify that the system can associate context to identify query dimensions. For example, first ask "Total passenger flow in 2023", then follow up with "What percentage was during the Spring Festival holiday"; the system should correctly associate the historical query content.
- When calling the dialogue interface, include the identifier of the specified attraction due diligence knowledge base, and verify that the returned results reference attraction data from the knowledge base.
- Check the conversation history collection corresponding to the MongoDB database to confirm that newly initiated conversation records have been successfully written.
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.