Page Scope
This documentation covers:
- Client industry: Property management
- Business direction: Investment research knowledge base construction and governance
- Capability area: Context and token
Data Characteristics of This Category
Property management investment research data sources include equipment inspection records for property projects, energy consumption operation reports, owner feedback ledgers, bidding documents, and industry regulatory policy documents. Update frequency varies by data type. Equipment inspection records are updated daily. Energy consumption reports are compiled weekly. Policy documents are released irregularly. Document structures include structured tables, semi-structured inspection reports, and unstructured policy files. Structured tables such as equipment operation parameter tables contain fields including equipment ID, inspection time, energy consumption value, and others. Units are kilowatt-hours and square meters.
Constraints on Context and Token Processing
Property management investment research data contains numerous structured fields and long text reports. Single documents have high token consumption. Strict control of total token count for segmentation and retrieval is required. Frequently updated inspection data requires context to carry latest operating parameters. This avoids deviations in investment research conclusions caused by outdated historical data. Investment research needs associated with multiple projects require retrieval of multiple document fragments at the same time. This easily exceeds the model's context window limit. Accurate balance between retrieval quantity and token usage is needed. Repeated occurrence of entity fields such as equipment numbers and project addresses increases redundant token counting. Reasonable configuration is required to reduce invalid consumption.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
segment length | 800-1200 characters | Property management documents are mostly inspection reports and operation ledgers. This range ensures complete single-segment semantics while keeping single-segment token count within a reasonable range |
retrieval count | top 3-5 items | Investment research scenarios require associating data from multiple equipment and projects. This quantity covers core information and avoids token overflow caused by excessive retrieval |
maxContext | 15000-20000 characters | Investment research conversations require carrying historical project comparisons and policy context. This range avoids model truncation caused by overly long context |
tokenLimitPerQuery | 4000 characters | A single round of investment research questions includes multi-project parameters and equipment information. This limit prevents model timeouts or excessive token consumption |
global.workerPoll.countGptMes | Count per conversation | Accurate tracking of token consumption for investment research conversations facilitates cost accounting and threshold adjustment |
rerank return count | top 2-3 items | Property management equipment fault and operation data have strong relevance. Reranking reduces redundant token usage |
Three Common Misconfigurations
- Symptom: In FastGPT version 4.6.7, setting the knowledge base
segment lengthto 5000 characters andretrieval limitto 1500 still retrieves complete chunked content. Cause: FastGPT'sretrieval limitperforms token truncation on the spliced context after retrieval, and does not limit the original length of knowledge base chunks. This does not match the short context requirements of investment research scenarios. - Symptom: Projects deployed on cloud platforms cannot find the token statistics field, and cannot view token consumption per conversation. Cause: The cloud platform's token statistics entry is located under the
Global Monitoringmenu in project settings. It was not found by following the standard path. - Symptom: A workflow is configured with trigger conditions to clear context, but the conversation history is not cleared. Cause: The correct conversation ID field was not bound in the workflow node, so the target context instance cannot be located.
How to Confirm Proper Configuration
- A typical property management inspection report is uploaded. Segmented details generated after knowledge base parsing are reviewed. The segment length is confirmed to match the configured setting.
- A round of investment research questions including multi-project parameters is initiated. Token consumption statistics on the conversation interface are viewed. The consumption value is confirmed to not exceed the
maxContextsetting threshold. - The trigger condition for clearing the workflow is tested. The conversation history list is reviewed. The target context is confirmed to have been cleared.
- Log output of
global.workerPoll.countGptMesis viewed. Token data for each conversation is confirmed to be correctly recorded and counted.
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. Specific issues require individual analysis. Testing on own samples is recommended prior to finalization.
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.