Knowledge Base Retrieval and Recall for After-sales and

Data Characteristics in This Category

After-sales and warranty data in the biomedical field primarily comes from product manuals, repair guides, compliance documents, FAQs, and user feedback records. These documents often contain extensive technical terms, product models, batch information, error codes, and operating procedures. Data update frequency is relatively stable, with concentrated updates during new product releases or regulatory revisions. Document structures are mainly semi-structured or unstructured, commonly in PDF, Word, and HTML formats. Fields include product serial numbers, fault descriptions, diagnostic results, repair plans, component codes, and warranty periods. Units involve time (year/month/day), quantity (pieces/boxes), temperature (℃), and concentration (%).

Constraints on Knowledge Base Retrieval and Recall

Product model and batch information are critical for precise retrieval. The knowledge base must effectively identify and match these entities to avoid generalized recall. The density of technical terms requires high-quality word embedding models to understand domain-specific contextual semantics in medicine and engineering. The periodic nature of document updates demands incremental update capabilities for the knowledge base, ensuring timely inclusion of new product and regulation knowledge. Characteristics of semi-structured documents, such as information in tables and diagrams, pose challenges for document parsing, potentially requiring customized extraction strategies. Furthermore, accurate matching of error codes and operating procedures determines whether the smart customer service can provide precise fault diagnosis and repair guidance, requiring high precision and recall rates for retrieval.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`Chunk size` (Segment Length)	`300–500 characters` (characters)	Ensures each segment contains sufficient context like product models and fault descriptions, while avoiding excessive length that leads to information redundancy and reduced retrieval efficiency.
`Chunk Overlap Length` (Segment Overlap Length)	`50–100 characters` (characters)	Guarantees context continuity, especially in sequential information like troubleshooting steps, preventing critical information from being truncated.
`Recall count` (Recall Count)	`Top 5 entries` (top 5)	Balances recall accuracy and model processing load; the top few results usually cover highly relevant information.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Requires testing to determine based on the similarity distribution of specific products and fault descriptions, to filter out low-relevance results.
`Rerank result count` (Rerank Return Count)	`Top 3 entries` (top 3)	Further refines results, placing the most relevant repair solutions or fault diagnosis guidance at the forefront.
`maxContext`	`4096 tokens`	Accommodates the professional and detailed nature of biomedical documents, ensuring the large language model can process sufficiently long contexts.

Three Common Pitfalls

Phenomenon: The smart customer service cannot correctly identify product models or batches mentioned by the user, leading to generalized or empty retrieval results. Reason: The knowledge base did not effectively annotate or normalize critical entities like product models and batches during data import.
Phenomenon: When a user asks about a specific error code, the system fails to recall the corresponding section of the repair manual. Reason: The document segmentation strategy is too coarse, separating error codes from repair steps, or the codes were not independently indexed.
Phenomenon: During testing, using the gpt-4o-mini model for QA search results in the message "Current Group default For Model gpt-4o-mini No Available channel" (No available channel for model gpt-4o-mini under current group default). Reason: In the FastGPT backend channel configuration, the gpt-4o-mini model is not associated with a valid API Key or the channel is inactive.

How to Confirm Proper Configuration

Create test cases for core product models and common faults. Verify the smart customer service accurately recalls corresponding repair documents or FAQs.
Check the knowledge base's index status. Confirm all product manuals, repair guides, and other documents are successfully parsed and indexed.
Simulate user queries. Observe the Similarity threshold (similarity threshold) distribution of recall results. Ensure most accurate results have a similarity above the set threshold.
Regularly review smart customer service interaction logs. Analyze unresolved or incorrectly resolved cases to determine if the issue lies in the recall stage or subsequent processing.

The values given 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.