Knowledge Base Retrieval and Recall for Stability Study

Data Characteristics

Stability study data originates from long-term stability reports, accelerated stability study reports, and storage condition documents for intermediates and active pharmaceutical ingredients. This data updates infrequently, typically in batches during drug development or after market launch. Documents are primarily structured reports, including batch information, observation time points, storage conditions (temperature, humidity, light), test items, test results, and judgment criteria. Fields cover physicochemical properties (e.g., content, dissolution, moisture, pH), microbial limits, and related substances. Units include percentages, ppm, µg/mL, ℃, %RH. Numerical precision is critical, often accompanied by charts and statistical analysis data.

Constraints on Knowledge Base Retrieval and Recall

Stability study reports are often lengthy, containing numerous tables and graphs. Pure text splitting and recall can lose critical context. Observation time points and storage conditions are core retrieval constraints, requiring precise matching. Field units and numerical ranges are crucial for recall accuracy; fuzzy matching can lead to incorrect judgments. Data updates are infrequent, but updates can involve subtle differences between batches. The knowledge base must support precise filtering by batch number or version number to avoid confusing stability conclusions from different batches. Identifying anomalous data points, such as a content decrease in a specific batch at a particular time point, requires the knowledge base to effectively extract this information from large amounts of normal data.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters`	Ensures each knowledge block contains complete observation time points and related test results, preventing context fragmentation.
`Chunk Overlap Length` (Segment Overlap Length)	`100 characters`	Maintains contextual continuity, especially between tabular data, reducing the risk of key information being truncated.
`Recall count` (Recall Count)	`Top 8`	Given the complexity of stability reports, increasing the recall count helps cover potential related information.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Stability data requires high precision; a high threshold helps exclude irrelevant reports or data fragments.
`maxContext`	`3000–4000 tokens`	Ensures sufficient capacity for data from multiple relevant observation time points, supporting complex query contexts.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Stability report documents can contain many charts and tables, requiring longer parsing times.

Common Pitfalls

After uploading to the knowledge base, some stability report files fail to generate question-answer pairs and are instead stored as raw text. This occurs when complex table structures or excessive image information within the file prevent the parser from effectively extracting structured data.
When retrieving stability data, the returned results exhibit insufficient relevance, showing reports that do not match the queried storage conditions or test items. This can be due to overly coarse segmentation strategies, confusing data under different storage conditions, or the retrieval model's inability to recognize units and numerical values.
System logs frequently show slow operation xxxxms warnings, leading to slow knowledge base updates or retrieval responses. This indicates insufficient indexing and query optimization in the underlying database for large stability reports, especially when handling multi-dimensional filtering conditions.

Verification of Configuration

Upload typical stability report files. Check the generated question-answer pairs in the knowledge base to ensure accurate extraction of key observation time points, storage conditions, test items, and results.
Perform retrieval for specific batches, storage conditions, and test items. Verify that the recalled results include all relevant report segments and check their accuracy.
Simulate high-concurrency retrieval scenarios. Use system monitoring tools to observe retrieval response times and confirm they are within acceptable limits.
Use query statements with explicit numerical ranges or units. Verify that the recall results can accurately identify and filter out data that does not meet numerical precision requirements.

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