Knowledge Base Retrieval and Recall for High-Value

Data Characteristics for This Category

High-value consumable data originates from manufacturer-provided product manuals, technical white papers, clinical application guidelines, regulatory certification documents, and sales/after-sales service documentation. These documents are typically in PDF, Word, or structured database formats. The data update frequency is relatively low, primarily occurring with new product releases, product iterations, or regulatory policy changes. Document structures are highly standardized, including fields such as product name, model, specifications, scope of application, contraindications, main components, performance parameters, usage instructions, precautions, and registration certificate numbers. Performance parameters often involve specific units like millimeters (mm), milliliters (mL), Tesla (T), or Farads (F), with strict requirements for numerical precision.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The stable and structured nature of high-value consumable data allows for thorough data cleaning and standardization during initial knowledge base construction. Low update frequency means less pressure on incremental knowledge base synchronization, but each update may involve replacing or modifying a large number of documents, requiring support for batch processing and version management. Documents contain numerous specialized terms, product models, and numerical parameters, requiring the retrieval system to accurately match these specific identifiers to avoid incorrect recall due to semantic generalization. Strict requirements for numerical precision and units mean that retrieval must support numerical range queries and semantic understanding of unit conversions to ensure the accuracy of recall results. Furthermore, due due to regulatory and clinical risks, any recall error can have serious consequences, making the accuracy and traceability of recall results critically important.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters`	High-value consumable documents often have logically complete paragraphs; moderately increasing segment length maintains contextual coherence.
`Chunk Overlap Rate` (Segment Overlap Rate)	`0.1`	Ensures a small overlap between segments to cover key information spanning across paragraphs.
`Recall count` (Recall Count)	`Top 5–8 items`	Given the precision requirements for high-value consumables, a few high-quality results are preferred over many generalized results.
`Similarity threshold` (Similarity Threshold)	`Calibrate by actual measurement`	Determine the boundary that distinguishes effective from ineffective results through actual testing for different models and vector databases.
`Rerank result count` (Reranked Return Count)	`3 items`	Further select the most relevant and highly credible results from the initial recall.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Longer parsing time is needed when processing large PDFs or manuals containing complex tables.

Three Common Mistakes

Knowledge base retrieval displays "Retrieving" for an extended period because some high-value consumable documents are too large or structurally complex, leading to file parsing timeouts and failed ingestion.
Retrieval results contain many items with low semantic and full-text matching scores because an appropriate recall threshold was not set, causing the system to return low-relevance content.
When users query for specific product models or registration certificate numbers, recall results are inaccurate or missing because structured information in documents was not effectively extracted and indexed, leading to insufficient precise matching capabilities.

How to Confirm Proper Configuration

Check platform search logs to confirm that all file parsing tasks complete normally, with no PARSE_FILE_TIMEOUT_SECONDS related error logs.
Conduct multiple rounds of test queries for common high-value consumable questions (e.g., product specifications, indications, contraindications, usage methods), checking if the Similarity threshold (Similarity Threshold) and Full-text Matching Score of the recall results are within an acceptable range.
Randomly select more than 10 high-value consumable products and query their registration certificate numbers or specific performance parameters. Verify if the recall results contain these precise identifiers and check if the Recall count (Recall Count) meets expectations.
Simulate new product launches or regulatory updates by batch importing new high-value consumable documents. Observe the knowledge base synchronization status to ensure data is updated promptly and effectively.

The values provided are common starting points and should be measured 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.