What This Category's Data Looks Like
The data for infrastructure construction project financing daily reports primarily comes from local public resource trading platforms, housing and urban-rural development department project management systems, bank credit ledgers, and regular submission documents from project owners. Data is updated every working day; no new data is added on holidays. The core format uses structured tables as the main content, with unstructured explanatory attachments such as project background and approval progress. Core fields include project unique ID, construction content overview, total investment amount, current financing application amount, financing party qualification level, approval status, and release date. Amount fields use a uniform unit of ten thousand yuan, and date fields follow the YYYY-MM-DD format.
How These Characteristics Impose Constraints on Model Access and Configuration
Multi-source heterogeneous data sources require the model access link to adapt to format differences across different platforms, so multi-source data parsing adapters must be configured. The daily update rhythm for working days requires scheduled model calls to adapt to scenarios with no data on non-working days, to avoid invalid model calls and resource consumption. The mixed structure of structured fields and unstructured attachments requires model configuration to distinguish between field-level parsing and full-text semantic understanding requirements, to avoid confusion between structured data and supplementary explanatory content. Fixed unit formats for amounts and dates require clear specification of parsing rules during model calls, to prevent unit conversion errors or missing fields.
How to Set Configuration Parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the total length of structured fields and attachment descriptions for a single infrastructure financing daily report, to avoid truncation of critical approval status and financing data |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Documents from multi-source platforms may contain large attachment scanning content, requiring sufficient time to complete full parsing |
embeddingModel | dengcao/Qwen3-Embedding-8B:F16 | This model meets the field association requirements of infrastructure data with semantic matching accuracy for engineering structured text, and is suitable for local deployment scenarios |
rerankModel | dengcao/Qwen3-Rerank | The reranking effect for engineering terms adapts to the multi-field association query requirements of financing daily reports |
tokenLimitPerCall | 16000 tokens | The total token consumption for a single infrastructure financing daily report usually does not exceed this value, to avoid failure of single calls due to token limit exceeded |
SCHEDULE_CRON | 0 9 * * 1-5 | Adapts to the rhythm of infrastructure financing daily reports only being updated on working days, triggers data pulling and model processing at 9:00 daily |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: The financing amount field returned by model calls lacks unit information. Cause: No parsing rule for amount fields is specified in the model configuration, causing the model to ignore the preset ten thousand yuan unit requirement and return only pure numerical values or incorrect units.
- Symptom: No data is returned after a scheduled task triggers. Cause:
SCHEDULE_CRONis configured to trigger daily, without adapting to the rhythm of infrastructure financing daily reports only being updated on working days, causing calls to empty data sources on holidays. - Symptom: Locally deployed Ollama models cannot access normally. Cause: No correct local address and port are configured in the FastGPT model access interface, or the model's API access permission is not enabled, causing connection timeouts.
- Symptom: Token consumption statistics are abnormally high. Cause:
tokenLimitPerCallis not configured, causing redundant project historical data to be repeatedly pulled for model processing and increasing invalid token consumption.
How to Verify Successful Configuration
- Manually pull a locally saved infrastructure construction project financing daily report, upload it to the FastGPT test document library, and check if the parsed fields fully match the core content of the original document such as project ID and investment amount.
- Initiate a custom query for the financing daily report, check if the returned results include correct unit parsing and project status descriptions, to verify the model's ability to understand engineering terms.
- View the FastGPT model call logs, confirm that the model access configuration has taken effect, and that locally deployed models can normally return embedding and reranking results.
- Run the scheduled task test script, confirm that data pulling and model processing workflows are only triggered on working days, to avoid invalid calls on non-working days.
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.