Knowledge Base Retrieval and Recall for Neurodegenerative

Data Characteristics

Policy and Standard Operating Procedure (SOP) documents in the neurodegenerative disease field have unique characteristics. Data sources primarily include clinical trial guidelines, ethical review requirements, and Good Manufacturing Practice (GMP) regulations published by national drug administrations. They also include internal institutional documents such as clinical research protocols, patient informed consent forms, data management plans, and sample processing SOPs. These documents typically have a low update frequency; for example, national regulations might update every few years, while internal SOPs may be revised annually or as project needs dictate. Document structures are hierarchical, featuring chapters, clauses, and appendices. They often include non-textual information like flowcharts, tables, and diagrams. Fields and units are highly precise and consistent, involving dosages (mg/kg), time (hours, days, weeks), biomarker concentrations (pg/mL, nmol/L), and scoring scales (e.g., MMSE, UPDRS scores).

Constraints on Knowledge Base Retrieval and Recall

The hierarchical structure and high density of specialized terminology in neurodegenerative disease policy documents require knowledge base segmentation to preserve contextual completeness. For example, over-segmenting a clause about ethical review for Alzheimer's disease clinical trials could lead to a loss of its connection to informed consent forms or adverse event reports during retrieval. Documents containing flowcharts and tables mean that pure text parsing might miss critical information. This necessitates considering multimodal processing or enhanced text descriptions. The low update frequency but profound impact of these documents makes knowledge base content stability crucial. Once imported, ensure version consistency to avoid misunderstandings from subtle differences. Furthermore, the presence of various specialized fields and units demands high semantic understanding from text embedding models, which must distinguish specific dimensions for different diseases or drugs.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	Balances the completeness of policy clauses with avoiding excessively long segments, allowing embedding models to capture longer semantic dependencies.
`Recall count` (Number of Retrieved Items)	`top 8–12 items`	Ensures coverage of multiple potentially relevant policy sections, considering the complexity and cross-referencing in neurodegenerative disease policies.
`Similarity threshold` (Similarity Threshold)	Calibrate by measurement	Requires grayscale testing with a small set of standard Q&A pairs to ensure highly relevant passages are recalled while avoiding noise from low-relevance passages.
`Rerank result count` (Number of Reranked Items)	`top 5 items`	Re-ranks text segments using a more complex model after initial retrieval, improving the accuracy of the final result.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates PDF files with many charts or complex structures, ensuring sufficient time for parsing to complete and preventing parsing failures due to timeouts.
`maxContext`	`32000`	Ensures the large language model can understand the full context of policy clauses, especially those involving multi-step processes or detailed descriptions.

Common Mistakes

Retrieval results contain many irrelevant policy clauses. The response is generic or off-topic. This happens when the Similarity threshold (Similarity Threshold) is set too low, recalling document segments semantically distant from the query.
After importing PDF documents with images or complex tables, some critical information is not retrieved. The response lacks data from diagrams or tables. This occurs when the knowledge base's parsing capability cannot effectively process non-textual content. Consider converting images to text or structuring table extraction.
When customizing knowledge base selection variables in a workflow, retrieval results are empty or unexpected. This is because the variable's data type does not match the actual ID or name of the knowledge base, leading to an incorrect query target.

How to Verify Configuration

Select 10-15 typical questions covering different aspects of neurodegenerative disease policies. Manually assess the relevance of the top 5 document segments recalled by the knowledge base for each question to determine a reasonable range for the Similarity threshold (Similarity Threshold).
Import several PDF policy documents containing complex charts or multi-page tables. Use the knowledge base's preview function to check if the document content is completely and accurately parsed into text, ensuring the PARSE_FILE_TIMEOUT_SECONDS configuration is effective.
Construct policy questions involving cross-references or multi-level logic. Verify if the retrieval results cover interrelated clauses from different policy documents. This assesses the synergistic effect of Chunk size (Segment Length) and Recall count (Number of Retrieved Items).

Note: 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.