What Data for This Category Looks Like
Professional chain industry research report data draws from public industry research reports, operational announcements disclosed by chain brands, and survey data from third-party retail consulting institutions. The update cycle follows a fixed quarterly schedule. Some monthly operational data updates in sync with real-time brand disclosures. Document structures include sections such as core operating indicators, regional market analysis, supply chain optimization plans, and risk warnings. Fields include store count, sales per square meter per month, regional market share, supply chain cost structure, and more. Units include yuan/square meter/month, count, ten thousand yuan, and other detailed measurement standards. The data also includes unstructured market analysis content.
Constraints Imposed on Knowledge Base Retrieval and Recall
The mixed structure of professional chain research reports requires retrieval and recall to support both numeric indicator matching and semantic analysis matching. Using only a single matching logic leads to result bias. The quarterly-dominant update cycle requires the knowledge base to support incremental synchronization. This reduces resource consumption from full reindexing, while adapting to some real-time updated operational data. Multi-dimensional segmented fields require retrieval to support filtering by dimensions such as region, store type, and supply chain link. This accurately matches users' targeted query scenarios. Some documents contain long associated analysis paragraphs. Segmentation must retain the contextual connection between indicators and analysis to avoid semantic fragmentation.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale | |
|---|---|---|---|
chunk_size | 800–1200 characters | Professional chain research reports contain structured indicators and associated analysis. Segmentation length must retain contextual connection between the two to avoid splitting core information | |
chunk_overlap | 150–200 characters | Regional analysis and store data in research reports often appear consecutively. Overlapping segmentation preserves semantic connections across blocks, improving retrieval accuracy | |
top_k | Top 8–12 results | Targeted scenario queries for professional chain research reports often require matching multi-dimensional indicators. Too many results increase context processing load. Too few results fail to cover relevant content | |
similarity_threshold | 0.72–0.80 | Terminology in professional chain research reports is highly specialized. A threshold that is too low will include irrelevant industry reports. A threshold that is too high may miss accurately matched valid content | |
incremental_sync | Triggered by file modification time | Updates to professional chain research reports include fixed quarterly updates and real-time brand disclosure updates. Triggered synchronization adapts to both update scenarios | |
parse_field_separator | ` | ` | Structured data in some third-party research reports uses vertical bars to separate fields. Using this separator accurately extracts structured operating indicators |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: An HTTP request node and a knowledge base retrieval node are configured in the workflow. After a query is triggered, the HTTP request is not executed. The large language model generates a response directly. Cause: The context retrieved by the knowledge base is not correctly passed to the large language model call node, or the pre-judgment logic for triggering the HTTP request is not set.
- Symptom: The documents retrieved by the knowledge base include a large number of non-professional chain category research reports. It is impossible to accurately match the target segmented scenario. Cause: Multi-field filtering rules are not configured, or the
similarity_thresholdvalue is too low, leading to the retrieval of irrelevant content. - Symptom: The large language model output includes redundant markers such as "Citation: [1]", which do not meet output requirements. Cause: No instruction to remove citation markers is added to the large language model prompt, or the automatically generated citation format is not cleaned up in the post-processing link.
How to Verify Successful Configuration
- Upload a professional chain research report document. Check the parsed segmentation results. Confirm that the segmentation retains the contextual connection between indicators and associated analysis. Adjust the corresponding configuration items to values that meet business requirements.
- Enter a query that includes professional chain segmented indicators. Check the number and relevance of retrieved results. Adjust
top_kandsimilarity_thresholdto values within the range matching the scenario. - Trigger a knowledge base synchronization task. Confirm that only modified documents are updated, and no full reindexing is triggered. Verify that the incremental synchronization configuration is active.
- Trigger a complete query process. Check the large language model output results. Confirm that no redundant citation markers are present. Verify that the prompt and post-processing rules are configured correctly.
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.