What this category of data looks like
Data sources for residential development yield rate-related data include project land transfer agreements, project progress ledgers, pre-sale record filings, regional residential sales average price data released by local housing and urban-rural development departments, project financing agreements, and monthly sales receipt ledgers. Updates are triggered by project development milestones. The first round of core data updates is completed before the pre-sale milestone. Post-completion updates cover construction phase data. Sales-related data is updated monthly during the monthly sales cycle. Regional market data is updated quarterly. The document structure includes structured project operation ledgers, attached with engineering change orders, sales record scan copies, and regional market analysis documents. Fields include project unique identifier, total land acquisition cost, unit construction cost, unit pre-sale quotation, regional competitor unit sales price, total salable area, proportion of sold area to total salable area. Units: total land acquisition cost is in yuan; unit construction cost, unit pre-sale quotation, and regional competitor unit sales price are in yuan per square meter.
Constraints imposed by these characteristics on model access and configuration
Residential development data includes both structured operation ledgers and unstructured engineering documents and record scan copies. This requires support for multi-modal model access to handle both data types. Data update rhythms fall into three categories: project milestones, monthly sales cycles, and quarterly regional market updates. This requires configuring trigger-based recall logic to avoid resource consumption from full recall operations. Fields include multiple structured categories such as costs, sales prices, and area. This requires configuring field mapping rules to ensure the model can correctly associate project data with corresponding fields. Regional competitor data and internal project data have format differences. This requires configuring recall filtering parameters to ensure data alignment. Data update frequency is relatively high. This requires configuring model call timeout and retry mechanisms to ensure data processing timeliness.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Engineering documents and record scan copies for residential development projects are typically large in single-file size, with long parsing times. Setting 600 seconds covers the complete parsing process |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Single engineering documents and record scan copies for residential development projects can reach hundreds of MB. Setting 1000 MB covers common file upload requirements |
Recall Count | Top 8-12 entries | Residential development project data has many fields. Excessive recall will exceed the model's context window. 8-12 entries cover the data required for core yield rate calculations |
Similarity Threshold | 0.70-0.80 | The similarity differentiation of residential development project data fields is relatively high. This range filters irrelevant data and retains core project information |
VLLM_EMBEDDING_CONNECT_TIMEOUT | 30 seconds | When connecting to embedding models deployed via VLLM, 30 seconds covers normal network connection time and avoids connection timeout errors |
ENABLE_IMAGE_RECOGNITION | Enabled | Residential development data includes unstructured images such as engineering change orders and record scan copies. Enabling this feature extracts field information such as costs and area from images |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common misconfigurations
- Symptom: After enabling image recognition in the FastGPT knowledge base, uploading residential project record scan copies results in the conversation node failing to provide image content. Cause: The API address and key of the multi-modal model are not configured, or image recognition permission is not enabled in the conversation node.
- Symptom: When connecting to the Qwen3-Embedding-8B model deployed via VLLM, a connection timeout or 400 error is returned. Cause: The API port and model name parameters of the VLLM embedding model are not correctly configured, or network firewalls restrict API access.
- Symptom: When the model calls tools to generate yield rate analysis for residential development projects, the output is interrupted mid-process. Cause: The model context window is set too small, or
PARSE_FILE_TIMEOUT_SECONDSis set too short, causing model invocation to trigger before long document parsing is complete.
How to verify correct configuration
- Upload a single residential development project record scan copy, wait for the knowledge base to complete parsing, and check if the parsed results contain text information from the image to confirm the image recognition configuration is correct.
- Go to the model access configuration page, enter the API address, model name, and port parameters of the Qwen3-Embedding-8B model deployed via VLLM, initiate a test connection, and confirm no errors occur.
- Configure the knowledge base recall rules, import residential development project structured data and unstructured documents, initiate a test recall, and confirm the returned fields match the project data.
- Initiate a test conversation containing multiple residential development project documents, wait for the model to complete output, and confirm the output is not interrupted mid-process.
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.