Data Characteristics in Retail Chains
Retail chain enterprises preparing registration documents primarily source data from internal product management systems, supplier qualification documents (e.g., drug registration certificates, medical device registration certificates, health food approvals, production licenses), internal compliance audit reports, and regulations issued by various regulatory bodies. Data update frequencies vary; regulations might update quarterly or annually, while product qualification documents update with approval validity or changes. Document structures are diverse, including scanned images, PDFs, Word, and Excel files. Excel files are often used for bulk product information, ingredient lists, and batch number management. Fields and units include drug generic names, specifications, dosages, registration certificate numbers, approval numbers, expiration dates, manufacturers, and approval dates. Units include milligrams, milliliters, tablets, boxes, and batches.
Constraints on Knowledge Base Retrieval and Recall from Data Characteristics
The complex data characteristics of retail chain registration documents impose multiple constraints on knowledge base retrieval and recall. First, heterogeneous data sources make data standardization difficult, impacting retrieval accuracy. Second, the timeliness of product qualification documents requires the knowledge base to have efficient update and version management capabilities to ensure the accuracy and compliance of recall results. Third, a large volume of mixed structured and unstructured documents necessitates robust compatibility in text parsing and information extraction, especially for critical fields within Excel files, which directly affects subsequent associated retrieval. Furthermore, different batches and specifications of products may share some information but also have subtle differences, requiring retrieval results to accurately distinguish these variations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters (characters) | Balances contextual completeness for long documents with retrieval efficiency for short texts, preventing excessive fragmentation. |
Chunk Overlap Length (Segment Overlap Length) | 50–100 characters (characters) | Ensures contextual continuity and reduces loss of critical information due to segmentation. |
Recall count (Recall Count) | 8–12 entries (items) | Controls model processing load and improves response speed while ensuring coverage. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Balances recall rate and accuracy, reducing interference from irrelevant information. |
Rerank result count (Rerank Return Count) | 3–5 entries (items) | Focuses on the most relevant results, improving the precision of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Addresses parsing requirements for large PDF or complex Excel files, preventing parsing timeouts. |
Three Common Mistakes
- Knowledge base query results are empty or incomplete. This manifests as missing key fields or an inability to retrieve relevant documents. This occurs when the knowledge base fails to effectively parse multi-dimensional data in Excel during construction or when OCR recognition rates for scanned PDFs are low.
- Retrieved product approval information is expired, but the system does not provide an effective alert. This happens when the knowledge base lacks an automatic identification and tagging mechanism for qualification document validity periods or when a regular update strategy is not configured.
- After restoring a knowledge base backup in the FastGPT backend, some document content cannot be retrieved. This may be because index data was not synchronized during file backup, leading to inconsistencies between the index and actual file content after restoration.
How to Verify Correct Configuration
- Select a batch of typical queries, including product registration numbers, generic names with specifications, and regulatory clauses. Check if the recall results include all relevant and up-to-date documents and verify the accuracy of key fields.
- Upload Excel files containing complex tabular data and scanned PDF files. Verify that the knowledge base can correctly parse critical fields (e.g., registration certificate number, validity period, manufacturer) and can retrieve information using these fields.
- Simulate a scenario where qualification documents expire. Upload old and new versions of files and observe if the knowledge base prioritizes recalling the latest version during retrieval. Evaluate its ability to filter out expired information based on threshold settings.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.