Data Characteristics
Cold chain logistics product data originates from supplier product manuals, technical specifications, operation guides, compliance certification documents, and internal test reports. Document updates are infrequent, typically occurring with product iterations or regulatory changes, possibly every few months or annually. Documents are semi-structured, containing numerous tables, technical parameter lists, and diagrams. Key fields include product model, temperature control range, carrying capacity, power consumption, dimensions, weight, compliance standards (e.g., GSP, GMP), alarm mechanisms, and emergency procedures. Units vary, including Celsius (℃), cubic meters (m³), kilograms (kg), watts (W), and hours (h).
Constraints Imposed by Data Characteristics on Knowledge Base Retrieval and Recall
The semi-structured nature of cold chain logistics product documents, particularly the prevalence of tables and parameter lists, challenges knowledge base segmentation strategies. Traditional text segmentation might split key parameter pairs, leading to incomplete information. Infrequent updates mean initial data completeness is crucial during knowledge base construction, but subsequent incremental update pressure is low. Diverse fields and units require retrieval models to accurately identify and match specific parameters in user queries, for example, distinguishing "temperature" from "temperature control range." Strong reliance on compliance standards and emergency procedures necessitates high precision and contextual completeness in recall results to avoid misjudgments or omitted critical operational steps. Additionally, quotation documents are typically more structured but contain many numbers and specific items, demanding high recall speed and accuracy.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Balances the integrity of parameter lists and paragraph text within documents, preventing truncation of critical information. |
Chunk Overlap Length | 100 characters | Ensures semantic continuity between adjacent segments, especially around tables or lists. |
Recall count | Top 5–8 entries | Improves recall relevance, covers multiple potential match points, and reduces omissions. |
Similarity threshold | 0.75–0.85 | Guarantees high relevance of recall results, excluding irrelevant or ambiguous document fragments. |
Rerank result count | 3 entries | Balances recall quality with system response speed and user reading efficiency. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large product manuals or compliance documents with complex tables. |
Common Pitfalls
- Excessive response time or timeouts when querying quotations. This typically occurs when the knowledge base contains a large volume of high-dimensional vector data without effective index optimization, leading to high computational load during retrieval.
- Queries for specific product models return information about other models, or critical parameters are missing. This results from an inadequate segmentation strategy, where product models and their corresponding parameters are split into different segments, affecting recall precision.
- An agent answers a question by referencing only one knowledge base, while another relevant knowledge base is not used. This may be due to improper knowledge base weight configuration or a similarity threshold set too high, filtering out secondary but relevant information.
Verification Steps
- Perform diverse queries targeting core fields like product models and temperature control ranges. Verify that recall results include all expected associated document fragments.
- Upload standard product manuals containing complex tables and multiple pages. Observe if knowledge base parsing completes within
PARSE_FILE_TIMEOUT_SECONDSand if segmentation maintains table integrity. - Simulate user queries, inputting specific product inquiries (e.g., "What is the power consumption of model X cold storage at -20℃?"). Check if recall results precisely locate the document fragment containing the parameter and evaluate result ranking.
- Construct questions where multiple knowledge bases have answers. Check if the agent can synthesize information from different knowledge bases or prioritize answers from a specific knowledge base based on weights.
Note: The values provided are common starting points. Measure performance against specific samples to optimize configurations.
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.