What the data for this category looks like
The data sources for commercial property investment research include internal operation systems, leasing management platforms, energy consumption monitoring terminals, public channels of government housing and urban-rural development departments, and industry association reports. The update rhythm has tiered characteristics: basic project archives are updated quarterly, rental ledgers are updated monthly, energy consumption monitoring data is updated daily, policy documents are synchronized on demand, and industry operation reports are updated quarterly. Document types include structured table documents (such as monthly rental settlement statements), project operation reports combining text and images, and policy and regulatory PDF documents. Fields include rentable area, monthly rental unit price, number of occupied tenants, and monthly energy consumption value, with units of square meters, yuan per square meter per day, units, and kilowatt-hours respectively.
What constraints these characteristics impose on deployment and upgrade
The coexistence of multiple data types requires the deployment phase to support a combined capability of structured parsing, semi-structured text extraction, and unstructured document parsing. The upgrade phase needs to synchronously update the parsing engine's adaptation rules for professional real estate documents. Tiered update rhythms require configuring differentiated synchronization trigger rules during deployment, and optimizing incremental synchronization resource usage and efficiency during upgrades. The professionalism of field units and semantics requires configuring field mapping verification logic during deployment, and adding verification rules for emerging format fields during upgrades. The decentralized nature of multiple data sources requires completing API connection adaptation for multiple systems during deployment, and compatibility with old data source interface call formats during upgrades.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Commercial real estate documents often include high-definition energy consumption monitoring charts and long-cycle operation reports, requiring support for large file uploads |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing long documents such as annual operation reports takes a long time, so extending the timeout period avoids parsing interruptions |
chunkSize | 800–1200 characters | Real estate documents contain a large number of professional terms and long sentences, and a moderate segment length preserves semantic integrity |
recallTopK | Top 8 entries | Investment research requires balancing comprehensiveness and relevance, and 8 recall entries can cover multi-dimensional real estate operation data |
similarityThreshold | 0.72–0.8 | Filter low-relevance non-real estate documents and retain accurately matched operation data |
incrementalSyncInterval | Hourly | Energy consumption data requires high-frequency synchronization, while basic archives can be synchronized daily; configuring hourly intervals balances real-time performance and resource usage |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Search results are displayed directly in the interface, resulting in duplicate output with AI-generated responses. The
showRawContextconfiguration item is not disabled, causing recalled knowledge base fragments to be exposed directly. - The interface prompts during deployment require pnpm version 9.0 or above. The project's dependent pnpm major version is not locked, or an incompatible older version of pnpm is used to execute deployment commands.
- Deployment instances continue to consume resources after being paused, leading to negative account balances. The
autoStopIdleInstanceparameter is not configured, or the trigger conditions are set too loosely, failing to identify idle instances.
How to confirm the configuration is complete
- Upload a typical commercial real estate monthly rental ledger, and check if the parsed fields include correct area and rental units with no missing fields.
- Initiate a search containing energy consumption data keywords, and verify that the number of recall results matches the
recallTopKconfiguration. - Simulate an incremental synchronization operation, and check that only newly added real estate data is synchronized to the knowledge base with no duplicate data.
- View deployment logs to confirm there are no
parse timeouterrors, and that parsing time is within the range configured byPARSE_FILE_TIMEOUT_SECONDS.
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.