What the data for this category looks like
Consumer construction materials investment research data primarily comes from industry association monthly monitoring reports, listed companies’ periodic financial reports, upstream raw material ex-factory price databases, terminal retail monitoring systems, and local housing and urban-rural development policy documents. Update cadences vary significantly: raw material quotes are updated daily, industry reports are released quarterly, and financial reports and policy documents are updated irregularly. Document structures include structured data tables, unstructured analysis paragraphs, and policy clauses. Most fields include specific units, such as tile unit price in yuan per square meter, cement production capacity in ten thousand tons per year, and pipe specifications using DN values.
What constraints do these characteristics impose on conversation logs and auditing
The multi-source, multi-update cadence, and multi-field characteristics of consumer construction materials data impose multiple constraints on conversation logs and auditing. Multi-source data requires complete recording of parsing sources and timestamps in logs to ensure audit traceability of data authenticity. Differentiated update cadences require logs to be retained for a sufficiently long period to cover traceability needs for quarterly/annual reports. Multi-field and specific unit characteristics require recording call parameters in logs to avoid unit mixing or field association breaks during audits. The combination of long documents and structured tables requires logs to record segmentation and recall nodes to ensure traceability of context calls.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
LOG_RETENTION_DAYS | 180 days | Consumer construction materials investment research data requires retention for quarterly/annual report traceability; 180 days covers at least two complete report cycles |
maxContext | 8000–12000 characters | Consumer construction materials documents often contain long paragraphs and structured tables; sufficient context preserves complete product specifications and regional parameters |
PARSE_SEGMENT_MAX_LEN | 600 characters | Adapt to parsing of short fields such as construction material product models and unit prices, avoiding field association breaks caused by long paragraph truncation |
RECALL_TOP_N | Top 6–8 entries | Consumer construction materials data has multiple dimensions; additional recall covers cross-query needs for different regions and specifications |
SIMILARITY_THRESHOLD | 0.72–0.78 | Ensures accuracy in distinguishing similar construction material products, avoiding confusing recalls of tiles or pipes with different specifications |
AUDIT_TRIGGER_CONDITION | Triggered when sensitive fields are called | Triggers auditing when sensitive fields such as product unit prices and production capacity are called, aligning with compliance requirements for investment research data |
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 case-by-case analysis, and testing against local samples prior to finalization is recommended.
Three common mistakes
- Phenomenon: A
400 Bad Requesterror appears in conversation logs, and the console prompts vector dimension mismatch. Cause: Consumer construction materials have many structured data fields, resulting in generated vector dimensions that do not match FastGPT’s default 2560, and theVECTOR_DIMENSIONparameter was not calibrated in advance. - Phenomenon: Records without marked call sources appear in audit logs. Cause: The
LOG_INCLUDE_SOURCEparameter was not enabled, and specific construction material category, region, or unit parameters called by the user were not recorded. - Phenomenon: A loop error is triggered during conversation, with context recall entries repeating repeatedly in logs. Cause: A reasonable upper limit for
maxContextwas not set, causing context expansion for long documents to exceed thresholds and triggering loop logic.
How to confirm configurations are correct
- Access the FastGPT backend log configuration page, verify the configured value of
LOG_RETENTION_DAYSto confirm alignment with the preset retention period. - Initiate a query including specific construction material product specifications and unit prices, and confirm that call parameters, parsing processes, and returned results are fully recorded in conversation logs.
- Trigger a query involving sensitive fields such as production capacity and ex-factory prices, and confirm that the audit system generates corresponding audit records.
- Check the vector database’s dimension configuration to confirm it matches the
VECTOR_DIMENSIONparameter in FastGPT.
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.