What the data for this category looks like
Data sources for commercial property research reports primarily include publicly disclosed business district operation data and property leasing reports from commercial real estate industry associations and professional consulting institutions. Updates follow a monthly regular report rhythm, with special overall market analysis releases in quarterly and annual cycles. Document structures cover business district location overviews, leasing price ranges, tenant business format distributions, passenger flow time period characteristics, project operation adjustment plans, and more. Data fields have clear measurement associations: for example, leasing prices are linked to per-square-meter pricing units, and passenger flow statistics are linked to time period units.
Constraints imposed on deployment and upgrade
The multi-field measurement association feature of commercial property research reports requires configuring dedicated field extraction rules during deployment, to prevent generic parsing rules from missing core information such as business formats and rental prices. The monthly update release rhythm requires adapting incremental update scheduling configurations during the upgrade phase, to reduce resource consumption caused by full re-crawling. The relatively long length of individual research reports requires adjusting parameter thresholds for long document splitting during deployment, to avoid parsing interruptions or fragmented splits. Multi-source document formats (PDF, Excel, web pages) require compatible multi-format parsing plugin configurations during upgrades, to ensure research reports from all channels can be imported normally.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Commercial property research reports have long individual lengths, leading to longer parsing times. This avoids timeout interruptions of the parsing process |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Individual large commercial property research report PDF or Excel files have large sizes, allowing large file uploads |
maxContext | 8000–12000 characters | Commercial property research reports have strong content relevance, requiring sufficient context to ensure logical coherence of retrieval results |
Recall count | Top 8–10 results | Commercial property research reports have multiple detailed dimensions, requiring a sufficient number of recalled relevant fragments to cover information related to different business formats and rental metrics |
Similarity threshold | 0.72–0.78 | Commercial property data has strong professionalism. This balances retrieval precision and coverage, avoiding missing relevant content about detailed business districts |
Rerank return count | Top 3–5 results | Key information in commercial property research reports is concentrated. A limited number of reranked results is sufficient to cover core business requirements |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing values.
Three common errors
- Symptom: After deployment, location maps and passenger flow heatmaps in commercial property research reports fail to load normally. The interface displays placeholders or returns a
404 Not Founderror. Cause: External resource proxy is not configured. Image links in research reports use private domain names or intranet addresses, which cannot be accessed by publicly deployed FastGPT instances. - Symptom: After offline deployment, clicking "Model Test" prompts "Request error". Using curl to test the rerank model interface returns a
500status code. Cause: The container port mapping configuration for the rerank model is incorrect, or the offline image failed to properly load dependent model weight files. - Symptom: After offline upgrade of FastGPT, knowledge base query returns fragmented research report content, and the large model cannot generate coherent consolidated conclusions. Cause: The long document splitting parameters were not rematched after the upgrade, or the incremental update index was not rebuilt, leading to broken context association of retrieved fragments.
How to confirm the configuration is correct
- Upload a single commercial property research report. After parsing completes, check if the parsed text fragments include dedicated fields such as business formats, rental prices, and passenger flow. Verify the matching degree between the parsed results and the original document's fields.
- Initiate a knowledge base recall test. Check if the number of returned recall results falls within the configured recall count range. Adjust the similarity threshold to match business requirements.
- Trigger an incremental update task. Check if the update log only synchronizes the latest released research report data, with no records of full duplicate updates.
- Test the rerank model call. Check if the returned reranked results follow the priority of core information in commercial property research reports. Adjust the rerank return count to optimize display effects.
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.