Data Characteristics for This Category
Retail chain R&D document data originates from internal product development departments, supplier technical specifications, market research reports, and compliance review documents. Data updates frequently; new product development or formula adjustments can lead to daily or weekly incremental documents. Document structures vary, including formula sheets, process flow diagrams, ingredient analysis reports, packaging design specifications, and user feedback summaries. Common formats include PDF, Excel spreadsheets, Word documents, and images. Fields and units are industry-specific, for example, gram weight (g), percentage (%), concentration (ppm) in formulas, and shelf life (days), storage conditions (℃). High precision is required.
Constraints on "Reference Source and Traceability" Imposed by These Characteristics
The high update frequency of retail chain R&D documents requires the knowledge base to support rapid indexing and real-time synchronization, ensuring the timeliness of referenced content. Diverse document formats mean structured analysis must support multimodal input and accurately extract key information. Failure to do so impacts traceability accuracy. Specific fields and units, especially in formula and process descriptions, demand higher semantic understanding and entity recognition. References must precisely point to specific values and units to avoid confusion. Furthermore, multi-turn conversations rely heavily on context, as R&D processes are often iterative and progressive. The system needs to link logical relationships between different documents to provide a complete reference chain.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters | Retail chain R&D documents are content-dense. An appropriate length ensures paragraph completeness while preventing information overload in a single segment. |
Recall count (Recall Count) | 8–12 items | Multiple source documents need coverage. This ensures relevant information can be recalled from different reports and specifications to handle complex queries. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | R&D documents demand high precision. A higher threshold filters noise and ensures reference relevance. |
Rerank result count (Rerank Return Count) | 5 items | This balances response speed and result quality. It re-filters recall results to present the most relevant content. |
maxContext | 4000 tokens | This supports multi-turn R&D discussions, retaining sufficient conversation history to ensure context continuity. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | This allows ample parsing time for large PDFs or documents containing complex charts. |
Three Common Pitfalls
- Symptom: After a user query, the answer lacks reference sources, or clicking a reference link does not navigate to the original text. Reason: In scenarios where the knowledge base shares links without requiring login, the reference and "view original" functions might be disabled due to incorrect permission configurations, preventing correct rendering on the frontend.
- Symptom: The system fails to maintain context, providing irrelevant answers during follow-up questions. Reason: The
maxContextparameter is set too low. This prevents the model from retaining sufficient multi-turn conversation history, hindering its ability to understand the context of subsequent questions. - Symptom: No reference identifier appears at the end of answer paragraphs, even when the reference function is enabled. Reason: Metadata (like page number, chapter) was not correctly extracted from text blocks during the file parsing stage, or the frontend rendering logic did not correctly match the reference information returned by the knowledge base.
How to Confirm Correct Configuration
- Upload typical R&D documents (e.g., product formula sheets, process flow specifications). Ask multi-turn questions. Check if each answer includes accurate reference source links and if they are clickable.
- Ask questions about key information in the document containing specific units and values. Confirm that the values cited in the answer match the original text and that units are correct.
- Simulate continuous follow-up questions from a user. Observe if the system can provide highly relevant answers based on the context of previous turns and offer corresponding references.
- Check the status of file parsing tasks in the system logs. Ensure that large R&D documents (e.g., PDFs over 50MB) are successfully parsed within the
PARSE_FILE_TIMEOUT_SECONDSconfiguration without timeout errors.
The values provided are common starting points. Measure them against your 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.