What the Data for This Category Looks Like
Data for property management financial reports comes primarily from project operation ledgers, corporate financial accounting systems, and filing reports from local housing and construction authorities. Two update schedules apply: daily syncs update monthly operation detail data, while batch updates roll out quarterly and annual financial report data at the end of fiscal quarters and fiscal years. Most individual financial report documents follow a structured format, with five core modules: basic project information, revenue and expense details, energy consumption statistics, personnel costs, and accounts receivable ledgers. Included fields are project number, accounting cycle, total public water and electricity energy consumption, total personnel compensation, and maintenance supply expenses. Common units include yuan, person-times, and accounting cycle ranges.
Constraints on Tool Calling and Plugins
The data characteristics of this category impose three constraints on tool calling and plugin workflows: First, differing update schedules across multi-source data require plugins to support both incremental sync and full update trigger modes. This accommodates high-frequency sync for monthly operation data and periodic batch updates for financial report data. Second, fixed modules and fields required by structured documents mean plugins must include built-in parsing rules for corresponding fields. This prevents missing fields caused by unstructured parsing. Third, unified data units require tool calls to automatically adapt to standard units such as yuan and person-times. This enables summary calculations for financial report dimensions without additional conversion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Structured property management financial report documents often include multi-module details, leading to long parsing times. 600 seconds covers the full parsing process |
Recall count | Top 8–12 entries | Financial report data has many closely related fields. Too many recalled entries add redundancy, while too few miss core revenue and expense module information |
Similarity threshold | 0.75–0.85 | Similar fields within financial reports, such as energy consumption and supply expenses, need to be distinguished. A threshold that is too low causes incorrect field matching, while a threshold that is too high misses relevant data |
Rerank result count | Top 3–5 entries | Core financial report modules are concentrated in the first few related items such as revenue and expenses, energy consumption. Retaining these after reranking meets analysis needs |
UPLOAD_FILE_MAX_SIZE | 1000 MB | A single annual financial report may include detail data for multiple projects. 1000 MB accommodates multiple batches of uploaded financial report documents |
TOOL_CALL_TRIGGER_MODE | Triggered after document parsing completes | This ensures tool calls use fully structured parsed data, avoiding calls with unready data |
The parameter values provided on this page are general recommendations for establishing configuration baselines. Actual values are influenced by material format, data volume, and business rules. Individual cases require separate analysis. Testing using local test samples is recommended before finalizing values.
Three Common Configuration Mistakes
- Symptom: After configuring
TOOL_DEFAULT_RESPONSE, the tool still automatically generates responses that do not match the specified content. Cause:TOOL_CALL_TRIGGER_MODEis not set to only execute bound plugins, or the knowledge base automatic recall switch is not disabled. This causes the system to trigger both knowledge base recall and specified response logic. - Symptom: Tool calls return missing financial report fields, such as no maintenance supply expense data. Cause: Required financial report modules are not specified in plugin configuration. This leads the tool to only extract some core fields, without covering the full financial report structure.
- Symptom: When calling the reranking plugin, single GPU video memory usage quickly rises to a stable value and does not decrease significantly with subsequent calls. Cause:
reranker_batch_sizeis not set to a low range, or dynamic video memory allocation parameters are not enabled. This causes each call to load too many model computing nodes.
How to Verify Proper Configuration
- A properly formatted test financial report document is uploaded, and the parsed field list is reviewed to confirm it includes the preset core module fields.
- A tool call task is triggered, and system logs are reviewed to confirm the parsing timeout threshold matches the configured value, with no timeout error records.
- The
Recall countconfiguration value is adjusted, the tool call is re-triggered, and the number of returned related data entries is verified to change with the configuration, confirming the configuration takes effect. - A test scenario for specified responses is triggered, and the tool is confirmed to only return the preset specified content, without automatically generating additional AI response content.
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.