What the data for this category looks like
Black goods intelligent due diligence report data mainly comes from brand factory inspection documents, national energy efficiency label databases, official e-commerce platform parameter pages, and after-sales maintenance records. Updates occur when new products launch. Regular energy efficiency data updates every quarter alongside sampling results. Each report has a fixed document structure, including fields such as product model, energy efficiency rating, rated power, circulating air volume, body dimensions, and compliance certification number. Units use international standards, such as watts, cubic meters per hour, and millimeters.
What constraints do these characteristics impose on vector models and indexing
Multi-source heterogeneous data sources lead to inconsistent field formats. Complete field standardization mapping before accessing the vector process to avoid semantic misalignment during indexing. Fixed document structures include multiple business fields. Split chunks by modules such as cooling, energy efficiency, and after-sales to prevent cross-module semantic mixing that reduces recall accuracy. Quarterly batch updates and sudden new product updates coexist. Indexing must support incremental synchronization, so full reindexing is not needed to cover new data. Unstructured after-sales maintenance records and structured parameter data coexist. Adopt a hybrid vector indexing strategy to meet retrieval needs for both data types.
How to set the configuration
| Config Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Black goods reports have many fields with moderate single-field length. This range ensures semantic integrity while avoiding overly long chunks |
chunk_overlap | 50–80 characters | Prevents semantic breaks across chunks, and adapts to the connection needs of multi-module fields |
embedding_model | bce-embedding-v1 | Adapts to semantic encoding of multi-source heterogeneous data, and supports mixed structured and unstructured input |
index_batch_size | 200–300 items/batch | Balances indexing speed and server load, and adapts to batch update scenarios |
recall_top_k | Top 10 results | Meets the multi-dimensional retrieval needs of due diligence reports, and avoids too few or too many redundant recall results |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Adapts to parsing time for long documents, and prevents timeout errors during batch uploads |
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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: After using chunk mode to call the pushdata API to upload, the task remains in the indexing state for a long time. Cause: The
index_batch_sizesetting exceeds the server's concurrent processing limit, and no task retry mechanism is configured, causing indexing tasks to pile up. - Phenomenon: Calling
text-embedding-3-largereturns a token unauthorized error. Cause: No valid access key for this model is configured, or the key's permissions do not include the model's call scope. - Phenomenon: Cannot directly call the specified embedding model, only can call it through a proxy channel. Cause: No direct access key for the model is configured on the platform, only the proxy channel is enabled.
How to Confirm the Configuration Is Correct
- Upload a single standard black goods due diligence report, check the parsed chunk splitting results, and confirm that the semantic coverage of each chunk corresponds to the business module.
- Initiate a vector indexing task, check if the
embedding_modelparameter in the task log matches the configured value, and confirm that the model call link is normal. - Retrieve the parameters of a specified black goods model, verify that the number of recall results matches the
recall_top_kconfiguration, and the similarity meets the business requirement threshold. - Batch upload multiple new product reports, confirm that the indexing task can be completed within the preset time, with no long-term pileup state.
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.