What the data for this category looks like
Professional chain intelligent due diligence data mainly comes from store operation ledgers, supply chain fulfillment records, membership management systems, industry compliance documents, and public disclosure information. Data update frequency varies by type: store operation data updates weekly or monthly, supply chain fulfillment data updates daily, and financial report data updates quarterly or annually. Document structures include structured reports and unstructured notes. Fields cover store ID, business district type, per-square-meter efficiency, repurchase rate, fulfillment timeliness, and more. Common units include square meters, ten thousand yuan, and person-times.
What constraints these characteristics impose on the knowledge base retrieval and recall process
The multi-store, multi-dimensional data characteristics of professional chains impose multiple constraints on the knowledge base retrieval and recall link. First, individual due diligence documents contain a large number of detailed entries. Too long text blocks destroy cross-store business logic connections. Too short text blocks split the integrity of associated information. Second, there are many fields with strong business correlations. Low-precision recall introduces irrelevant store or supply chain data, interfering with the accuracy of due diligence conclusions. Third, frequently updated data requires regular synchronization with the knowledge base. Otherwise, retrieval results lag behind actual operating conditions. Fourth, the mixed document structure of structured reports and unstructured notes requires adapted chunking and recall strategies.
How to set configurations
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Professional chain documents include store details and supply chain entries. This range balances context integrity and retrieval accuracy |
Recall count | Top 10–15 entries | Due diligence reports need to cover multi-store, multi-dimensional associated data. Too few entries cannot support complete analysis |
Similarity threshold | 0.72–0.80 | Professional chains have many fields with tight business correlations. This range filters irrelevant data while retaining valid associated information |
Rerank result count | Top 3–5 entries | Due diligence reports need to focus on core associated data. Too many entries increase context length beyond model processing limits |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading large files such as single-store operation summaries and batch supply chain reports, adapting to professional chain document sizes |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Batch multi-store document parsing takes longer. Extending the timeout period avoids parsing interruptions |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: The number of chunks after splitting knowledge base documents exceeds 3000, and retrieval and recall results are missing or abnormal. Cause: Super large files are not split into small files that meet the segment length requirements, and the number of chunks per single file exceeds the system processing limit.
- Phenomenon: Retrieval result reranking returns false, and the interface has no valid reranking data. Cause: API call parameters are not correctly configured after the reranking model is deployed, or the key permission is insufficient.
- Phenomenon: The target knowledge base cannot be selected during tool call. Cause: The data source of the corresponding knowledge base is not bound in the tool configuration, or the knowledge base does not have tool call permission enabled.
How to confirm the configuration is complete
- Upload a single professional chain operation report, check if the number of parsed text chunks matches the set range of the segment length configuration.
- Initiate a retrieval request containing store identifiers or supply chain keywords, check if the reranking mark of the returned results is true, and the reranking results conform to business correlation logic.
- Configure the tool call node, confirm that the created professional chain-specific knowledge base can be selected from the drop-down menu.
- Upload an image file containing storefronts or operation identifiers, verify that the retrieval results are associated with the parsed content of the corresponding image.
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.