What Data Looks Like for This Category
Residential development investment research data comes primarily from three sources: land transfer announcements from natural resources departments, construction permit and sales filing systems from housing and urban-rural development departments, and publicly available project feasibility study reports and monthly operation ledgers from real estate enterprises. Data update cycles vary significantly: land transfer information is updated quarterly, construction progress ledgers are synchronized weekly, and sales filing data is pushed in real time. Documents include structured tables with fields such as project name, plot number, floor area ratio, land acquisition date, and salable area, alongside unstructured reports. Field units are mostly square meters, floor area ratio, and ten thousand yuan. Some documents include multi-page project phasing planning details.
Constraints for Model Access and Configuration
The multi-source heterogeneous nature, varying update cycles, and structured field characteristics of residential development investment research data impose clear constraints on model access and configuration. Multi-source data must support parsing formats for both structured ledgers and unstructured reports. Configure parsing parameters to adapt to both document types. Align synchronization frequencies to match data sources with varying update cycles. Configure short-interval incremental synchronization for real-time sales filing data. Use batch scheduled synchronization for quarterly land data. Strictly align unit and mapping rules for structured fields to avoid model inference errors caused by mixed field units. Configure reasonable segment lengths for long documents with phasing planning content to fit model context window limits.
Recommended Configuration Values and Rationale
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Residential development project feasibility study reports and phasing planning documents are usually lengthy, so sufficient time must be reserved for single-file parsing |
maxContext | 8000–16000 characters | Adapts to input requirements after long document segmentation, preventing loss of key information such as project phasing and area due to insufficient context |
RECALL_TOP_N | Top 8–12 entries | Covers multi-dimensional investment research data including land transfers, construction progress, and sales filings, balancing recall accuracy and context load |
SIMILARITY_THRESHOLD | 0.75–0.85 | Requires high precision for structured field matching, filters irrelevant non-target project data to improve the accuracy of investment research conclusions |
UPLOAD_FILE_MAX_SIZE | 500 MB | Adapts to upload requirements for single large project ledgers or multi-phase planning documents, preventing parsing failures for large files |
VOICE_MODEL_ID | Calibrated via actual testing | Different voice models have varying performance in adapted investment research scenarios, and adjustments must be made based on actual business scenarios |
The parameter values provided on this page are common starting points for configuration setup. Actual values are influenced by material format, data volume, and business rules. Each situation requires individual analysis. It is recommended to test on deployment-specific samples before finalizing settings.
Three Common Misconfigurations
- Issue: Large models produce incorrect total calculations for residential project metrics such as total salable area and total investment amount, with returned values inconsistent with ledger data. Cause: Unified mapping rules for structured field units were not configured, and data from different plots in square meters and mu was added directly, leading to numerical calculation errors.
- Issue: Custom voice models fail to work properly after configuration, with the interface returning a
404 Not Founderror. Cause: The call address and secret key for the custom model were not correctly configured during the model access phase, causing the model mapping relationship to become invalid. - Issue: Locally deployed models return project time information that lags behind the current server time, and cannot display the latest sales filing data. Cause: The real-time data synchronization switch for the knowledge base was not enabled, or the model's time synchronization plugin was not configured, causing context data used for model calls to not update in a timely manner.
How to Verify Proper Configuration
- Upload a residential development project feasibility study report, check if parsed structured fields are complete and units are unified, and verify that file upload and parsing timeout configurations are effective.
- Initiate an investment research query, confirm that the number of recalled documents matches the configured recall count requirement, and that similarity matching results align with preset threshold rules.
- Call the custom model interface, check if returned results include the latest server time information, and verify that access configurations for local or voice models are working correctly.
- Initiate a total calculation query, confirm that returned values such as total salable area and total investment amount match ledger data, and verify that field mapping rules are effective.
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.