What Data Looks Like for This Category
Black goods investment research data sources include domestic home appliance industry association official websites, brand official technical white papers, raw material market data from commodity trading platforms, patent public databases, and e-commerce platform sales data. Update rhythms vary: industry dynamic documents are updated weekly, product parameter documents are updated in real time with new product launches, and raw material market documents are updated daily. Document structures include structured fields such as product model, rated power, appearance dimensions, launch cycle, and raw material composition, paired with unstructured industry analysis and technical patent texts. Units include kilowatt, millimeter, unit, and others.
Constraints Imposed on Context and Token Workflows
Black goods investment research requires associating three types of data simultaneously: product parameters, raw material market trends, and industry reports. A single-round query needs to recall multi-source documents, so overall context token consumption is higher than single-category investment research scenarios. Structured product parameter documents have dense details. Overly long segments lead to token waste, while overly short segments break parameter association logic. Frequently updated raw material market data requires frequent recall of latest content, further increasing context token usage. If recall rules are not adapted to category characteristics, effective information is easily truncated or invalid content occupies available space.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 12000–15000 tokens | Black goods investment research needs to recall three types of documents simultaneously: product parameters, raw material market trends, and industry reports. Single-round total context token consumption is higher than general-purpose categories, so sufficient space must be reserved to accommodate multi-source recalled content |
chunkSize | 800–1000 characters | Black goods product parameter documents contain multiple sets of structured indicators. Overly long segments lead to token waste, while overly short segments break parameter association logic |
recallCount | Top 6–8 entries | Black goods have a large number of SKUs, so documents matching investment research topics need to be recalled accurately. Excessive recall will exceed the context token upper limit |
similarityThreshold | 0.72–0.78 | Black goods product models and technical parameters have high similarity, so a reasonable threshold must be set to filter irrelevant recall results and avoid invalid token occupation |
rerankTopN | Top 3–4 entries | Retain the most relevant core documents after reranking, reducing subsequent context token consumption and adapting to the accurate information needs of investment research scenarios |
fileParseChunkOverlap | 100–150 characters | Black goods technical documents have parameter associations across segments. Overlapping segments can preserve context coherence and avoid key information truncation |
The parameter values provided on this page are standard starting recommendations. Actual values are affected by material format, data volume, and business rules. Each scenario requires individual analysis, and testing with relevant samples is advised before finalizing configurations.
Three Common Misconfigurations
- Phenomenon: After adjusting context parameters, investment research conversations do not reflect expected historical memory, and recalled historical content is irrelevant to the current question. Cause: The
recallCountandsimilarityThresholdparameters were not adjusted synchronously. Excessive low-similarity historical conversations occupy context token space, causing valid content to be truncated. - Phenomenon: When batch uploading black goods product parameter documents, the system restarts repeatedly, and logs prompt token encoder initialization failure. Cause: A reasonable
chunkSizeparameter was not set. The token count of split single long documents exceeds the encoder's preset upper limit, causing initialization failure. - Phenomenon: Calling the knowledge base query interface returns
400 Bad Request, prompting that the token count exceeds the limit. Cause: ThemaxContextparameter was not adjusted for the combined recall of black goods multi-source documents. The total single-round context token exceeds the model's supported range.
How to Confirm Proper Configuration
- Upload 3–5 typical black goods documents (product parameters, industry reports, raw material market data), perform segment parsing, check the matching degree between the number of parsed segments and the
chunkSizeandfileParseChunkOverlapparameters, and confirm that no key information is truncated. - Run an investment research conversation that requires recall of multiple types of documents, view the context display panel, confirm that the number of recalled documents matches the settings of
recallCountandrerankTopN, and the total token count does not exceed the preset value ofmaxContext. - Simulate a batch upload scenario, check the system logs for token encoder-related error prompts, and confirm that there are no initialization failures.
- Adjust the
similarityThresholdparameter, initiate multiple queries with similar topics, confirm that the relevance of recall results meets expectations, and no excessive irrelevant content occupies context space.
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.