Data Characteristics
Retail chain quality documentation comes from various sources: internal SOPs, product specifications, supplier qualifications, store inspection reports, and user feedback records. These documents update frequently, especially after new product launches, policy changes, or quality incidents. SOPs and product specifications are typically structured or semi-structured, with clear headings, paragraphs, and lists. Store inspection reports often mix unstructured text and images. Common data fields and units include batch numbers, production dates, expiration dates, quality inspection report numbers, store IDs, inspection scores, and descriptions of non-conformities, encompassing dates, text, and numerical data types.
Constraints on Deployment and Upgrade
The large volume and frequent updates of retail chain quality documentation require high efficiency in data synchronization and indexing. Diverse document structures necessitate support for parsing multiple file formats and effective handling of unstructured content. Rapid business iterations, such as new product listings or compliance changes, demand flexible configuration adjustments to quickly expand and update the knowledge base. The wide distribution of chain stores means system deployment must consider network bandwidth and stability for remote access, ensuring all store users can access and use the quality knowledge base smoothly. For model selection, given the high volume of details and specialized terminology, the model needs strong contextual understanding and the ability to integrate professional knowledge.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 50 MB | Store inspection reports and product specifications may include images, leading to larger file sizes. |
maxContext | 3000 Tokens | Accommodates long texts in SOPs and product specifications, maintaining contextual completeness. |
Chunk size (Segment Length) | 800 characters (characters) | Ensures each segment contains sufficient information while avoiding excessive length that could reduce retrieval accuracy. |
Recall count (Number of Retrieved Items) | Top 5 entries (top 5) | Improves retrieval efficiency and covers common quality issue scenarios. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances accuracy and coverage, filtering out irrelevant content. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles complex PDF documents, preventing parsing timeouts. |
Common Pitfalls
- Knowledge base data insertion fails, with logs showing
document parse failed. This usually indicates file encoding or format incompatibility, preventing the parser from correctly reading content. - The model provides irrelevant answers to long text questions. This often occurs because a small local model lacks the semantic processing capability for complex queries, fails to accurately understand key information in long texts, or the
maxContextparameter is set too low, leading to context truncation. - Voice functionality becomes unavailable after upgrading API interfaces to HTTPS. This might be due to incorrect SSL certificate configuration or the reverse proxy failing to correctly forward HTTPS requests, preventing secure access to voice services.
Verification
- Upload various document formats (e.g., PDF, DOCX, TXT) to verify the knowledge base correctly parses and indexes them. Confirm content is retrievable by searching keywords.
- Test with quality questions of varying complexity to assess the model's answer accuracy and relevance, especially for specialized terminology and long texts.
- Simulate different store network environments to test knowledge base access speed and response times, ensuring a good experience for remote users.
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.