What this type of data looks like
Residential development research report data mainly comes from public disclosures by housing and construction authorities, monthly survey data from industry associations, development project ledgers officially disclosed by listed real estate companies, and land auction transaction information. Data update frequency varies with event timelines. Land transaction data is updated in real time. Project progress data is synchronized quarterly. Industry trend data is released monthly. Most documents combine structured tables and analytical text. Core fields include project location coordinates, total land purchase price, planned floor area ratio, total construction area, construction milestones, and sales cycle. Units include square meters, ten thousand yuan, ratios, and others.
Constraints imposed by these characteristics on model access and configuration
The multi-source, scattered nature and uneven update rhythm of residential development research reports require configuring trigger synchronization rules for multi-source data, and distinguishing recall timeliness for real-time and periodic data sources. The large number of structured fields and diverse units require configuring field verification parameters to ensure correct field mapping and unit matching during model calls. Single documents contain large amounts of project detail data, with a volume far exceeding that of general documents. This requires adjusting segmentation parameters to adapt to long text splitting and avoid truncating critical information. Differences in format across data sources require configuring parsing templates to adapt to research reports with different structures, reducing parsing failure rates.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 120–180 seconds | Single residential development research report contains multiple project details, with longer parsing time than general documents |
UPLOAD_FILE_MAX_SIZE | 500 MB | Some large real estate company research reports include full-region project ledgers, resulting in large single-file size |
Segment Length | 800–1200 characters | Research reports have dense structured fields. Too long segments cause field association breaks, while too short segments increase context fragmentation |
Recall Count | Top 8–12 results | Residential development research reports require covering multi-dimensional project data. Too few results will miss critical information |
Similarity Threshold | 0.72–0.85 | Project data similarity needs to balance accurate matching and differentiation between different projects in the same region |
Rerank Return Count | Top 4–6 results | Final display needs to focus on core project information, avoiding excessive redundant content that interferes with retrieval results |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Each scenario requires individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: Interface shows "Request Error" and no content in reranking results. Cause: Correct absolute path for local reranking model is not configured, or reranking model dependencies are not properly installed for FastGPT 4.8.22.
- Symptom: Model loading failure is returned during vector recall, and the console shows the error "model not found". Cause: After upgrading to version 9.0, the configuration directory for the m3e model was not updated synchronously, causing the system to fail to locate the model files.
- Symptom: Model stream response is empty on the frontend, but complete replies can be obtained via terminal curl calls to the interface. Cause: The model response timeout parameter configured for the frontend is set too short, and does not match the actual response duration of the backend interface, causing the request to terminate early.
How to confirm configuration is complete
- Upload a single large-volume residential development research report, and check whether the parsing task completes within the set timeout period without timeout errors.
- Enter a search term containing project location and planning indicators, and verify whether the number of recall results falls within the configured recall range, and whether the similarity scores fall within the set threshold range.
- Call the reranking interface, and check whether the number of returned results matches the configured reranking return count, and whether the sorting logic meets business requirements.
- Manually trigger a recall of real-time land data, and confirm that updated content can be retrieved normally.
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.