What this category’s data looks like
Black goods investment research data sources include industry association public monthly/quarterly operation briefs, brand annual/half-year financial reports, SKU sales ledgers from mainstream e-commerce platforms, shipment details from upstream supply chain manufacturers, and home appliance technology patent database documents. Data update cadence has tiers: industry reports are updated quarterly, e-commerce sales data is synced daily, financial report documents are updated per disclosure cycles, and patent documents are added in real time.
Single documents include standardized fields: SKU code, energy efficiency rating, cooling/heating power (unit: watt), external dimensions (unit: millimeter), launch date, test report number, and more. Some technical documents contain long-form circuit design descriptions and performance test data.
What constraints do these characteristics impose on conversation logging and audit
Black goods investment research data comes from multiple sources with varying update cadences. Conversation logs must be linked to data source version identifiers and timestamps to enable traceability of data source update cycles during audits.
Individual documents contain multiple standardized fields and long-form technical descriptions. Conversation logs must record the referenced SKU code, field name, and specific value to facilitate locating the basis for investment research conclusions during audits.
Frequently updated e-commerce sales data can lead to differences in dataset versions called during conversations. Audit processes must verify the match between the dataset build time recorded in logs and the actual data update time.
Long-form technical documents are prone to length limit issues. Logs must retain the start and end positions of segmented cuts to help troubleshoot conversation deviations caused by lost context.
How to set up configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Most single black goods technical documents range from 5000–10000 characters. This setting retains full core context and avoids semantic breaks caused by segment cutting |
segment_length | 1500–2000 characters | Core paragraphs of black goods investment research documents typically range from 1000–1800 characters. This value preserves paragraph semantic integrity while reducing the probability of index limit violations |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing long-form technical documents and multi-page financial reports typically takes 300–500 seconds. This setting prevents indexing failures caused by parsing timeouts |
UPLOAD_FILE_MAX_SIZE | 200 MB | Typical size of single black goods industry reports and technical manuals does not exceed 200 MB. This value covers most document upload requirements |
retrieve_count | Top 10 | Black goods investment research requires linking multi-dimensional data. 10 retrieved results cover core reference bases and avoid missing key SKU parameters or sales data |
similarity_threshold | 0.75–0.85 | Fields and parameters in black goods investment research data have relatively high similarity. This threshold filters irrelevant documents while retaining highly relevant investment research reference content |
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, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Issue: An "document length exceeded" error pops up during indexing, or conversation response content is truncated. Cause: The
segment_lengthsetting was not adjusted to a value compatible with black goods long-form technical documents, causing individual text segments to exceed the model context limit. - Issue: The conversation details page only displays user and assistant interaction text, and cannot view internal execution records such as dataset retrieval and model calls. Cause: The corresponding logging switch was not enabled, or necessary parameters for log storage were not configured.
- Issue: No conversation history records are found in the MongoDB database, or the
chat_historyfield is empty. Cause: The connection string parameters for log storage were not configured correctly, or database read/write permissions were not granted, causing logs to fail to write properly.
How to confirm correct configuration
- Navigate to the dataset management page, view the segment preview of uploaded black goods documents, and confirm whether the segment length matches the expected setting.
- Initiate a conversation targeting specific SKU parameters, enter the conversation details page, and confirm whether the document ID and call parameters retrieved from the dataset are displayed.
- Log in to the MongoDB database, query the
fastgpt_chat_historycollection, and confirm whether corresponding conversation records and thesource_datafield exist. - Simulate the indexing process for a long-form document, check for abnormal errors, and verify the rationality of the configuration.
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.