What the Data Looks Like
Residential development research report data is sourced primarily from land transfer and project approval data published by housing and construction authorities, quarterly and annual development and operation reports regularly released by real estate enterprises, and special research documents from industry consulting institutions. Data update cycles fall into two categories: monthly updates for land transfer and approval progress, and quarterly updates for sales and inventory data. Individual documents typically include structured fields such as project location coordinates, land transfer fees, total construction area, average sales price, and development cycle. They also contain unstructured content including supporting policy explanations and market trend analysis. Field units use industry standard measures such as square meters, ten thousand yuan, and floor area ratio.
Constraints for HTTP Interfaces and External Systems
The multi-dimensional structured fields and periodic update nature of residential development research reports require HTTP interfaces to support combined multi-condition filtering and incremental data pulling. The long text and structured data volume of individual documents require interfaces to support paged responses and resumable transmission. When connecting to external systems, teams must adapt to different field naming rules used by real estate enterprise ERP and housing construction data platforms to avoid data loss from field mismatches. The periodic update feature requires interfaces to support synchronization triggered by timestamps. Full data pulls are only used for initial synchronization scenarios. Charts and attachment data included in research reports require interfaces to support streaming transmission and parsing adaptation for binary files.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000-12000 characters | Residential development research reports have a relatively long average length per document, and need to accommodate complete project data, policy explanations, and market analysis content |
recallTopK | Top 8-12 results | Residential development research reports contain multi-dimensional project information, and need to cover multiple retrieval dimensions such as location, sales, and land |
similarityThreshold | 0.72-0.80 | Residential development research reports have a high degree of keyword overlap, and need to filter low-relevance non-residential or industry-wide content |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Individual research reports contain a large number of structured tables and charts, and parsing time exceeds the default threshold for general documents |
aiproxy.requestTimeout | 180 seconds | Pulling research report data from external systems requires multiple linked interface queries, which takes a long time |
externalSystem.fieldMapping | Follow the format: "Project Name → projectName, Construction Area → buildArea(㎡), Average Sales Price → price(yuan/㎡)" | Core fields of residential development research reports differ from field names in real estate enterprise ERP and housing construction data platforms, and need to be uniformly mapped to standard fields recognizable by FastGPT |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values will vary based on material format, data volume, and business rules. Specific scenarios require individual analysis, and testing on your own sample data is recommended before finalizing settings.
Three Common Configuration Mistakes
- A
504 Gateway Timeouterror is returned when calling the interface, and thePARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted. The parsing time for residential development research reports exceeds the default threshold. - Research report fields pulled by external systems are empty. The
externalSystem.fieldMappingconfiguration is not set up, and the field names from the self-developed system are not mapped to standard fields recognizable by FastGPT. - Recall results include large volumes of non-target content such as commercial real estate and industrial park reports. The
similarityThresholdis set too low, failing to filter low-relevance cross-category research reports.
How to Verify Proper Configuration
- Call the
/api/v1/rag/retrieveinterface, pass in exclusive keywords for residential development projects, and check if the returned result fields include standard fields such asprojectName,buildArea, andprice. - View the
aiproxyruntime logs, confirm that interface request durations do not exceed180 seconds, and no timeout errors such as504 Gateway Timeoutare present. - Manually upload a residential development research report document, and check if the parsed text includes core content such as complete project progress and land transfer data.
- Trigger the external system synchronization interface, confirm that the number of pulled research reports matches the preset monthly or quarterly synchronization rules.
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.