What Data for This Category Looks Like
Data for hotel and catering intelligent due diligence reports is sourced from structured reports from store operation systems, public review texts from third-party life service platforms, supply records from food material suppliers, and compliance public notice information from local regulatory authorities. Update frequencies differ across sources: store revenue and supply records update daily, review data updates in real time, and compliance public notice information updates weekly. Document structure splits into three modules: basic information, revenue and supply chain, and compliance records. Fields include store name, registered address, business license number, daily revenue, customer unit price, food material purchase quantity, hygiene rating, and more. Units include yuan, yuan/person-time, kilogram, star rating, and more. Some fields use date formats.
Constraints Imposed on Model Access and Configuration
Hotel and catering due diligence data includes structured reports, unstructured text, and compliance public information. Mixed multi-source data requires models to support both structured field extraction and unstructured semantic understanding. Targeted configuration of vector models and text understanding models is required. Differences in update frequencies across data sources require models to handle real-time incremental data access, and avoid context window overflow. Multiple unit fields require vector models to support unit recognition and normalization. Without this, field matching errors will occur in recall results. When processing batch files, the volume and parsing time per due diligence file are higher than general scenarios. Adjust timeout and chunking parameters to adapt to data scale.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Hotel and catering due diligence data includes multiple supply chain reports, 30-day revenue details, and crawled review texts. Single-file volume is generally large |
maxContext | 8000–12000 characters | Must accommodate complete store revenue details, supply chain records, and compliance information to avoid truncation of critical data |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | When batch parsing multiple due diligence files, structured parsing of food material purchase lists takes a long time. The default timeout is insufficient for processing |
embedding_model | Vector model that supports multi-unit recognition | Data includes multiple unit fields such as yuan, yuan/person-time, and kilogram. General vector models are prone to unit matching errors |
recall_top_k | Top 8–10 entries | Due diligence reports need to cover three core data types: supply chain, revenue, and compliance. Excessive recall increases context window pressure |
chunk_size | 800–1000 characters | Single records of catering revenue details are moderately long. This chunking length avoids splitting that damages field integrity |
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 settings.
Three Common Misconfigurations
- Symptom: After uploading a due diligence file, the parsing status shows "field recognition failed". Cause: No vector model supporting multi-unit recognition is configured. General vector models cannot distinguish between different unit fields such as yuan, yuan/person-time, and kilogram.
- Symptom: A
408 Request Timeouterror is returned when batch processing due diligence files. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted. The default timeout is insufficient to complete the multi-file parsing process. - Symptom: After configuring DingTalk access, the published version prompts "receiving message address verification failed". Cause: The publicly accessible address where FastGPT is deployed was not entered in the corresponding configuration item of the DingTalk Open Platform, and public domain name validity verification was not completed.
How to Confirm Configurations Are Correct
- Upload a test file that includes store basic information, revenue details, and compliance records. Check that the
file parsing statusshows "completed", and the parsed field list includes custom fields such as customer unit price, purchase quantity, and revenue. - Initiate a due diligence report generation task. Check that the
embedding_modelparameter in the model call logs matches the configured item, and the number of recalled context entries falls within the range set byrecall_top_k. - Test uploading a test file with a volume of 2100 MB. Confirm that the system prompts that the file exceeds the upload limit, verifying that the
UPLOAD_FILE_MAX_SIZEconfiguration takes effect. - Simulate DingTalk message push. Confirm that the receiving address can normally receive test messages with no verification error prompts.
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.