Data Characteristics
Phase I clinical research quality documents include study protocols, ethics approvals, informed consent forms, case report forms (CRFs), drug management records, laboratory test reports, and adverse event (AE) records. These documents typically originate from research centers, sponsors, or contract research organizations (CROs). Update frequency varies: protocols and informed consent forms may be revised during a study, while CRFs, drug management records, and AE records are generated and updated in real-time as the study progresses. Document structures also vary. PDF protocols and approvals usually contain structured text. CRFs often exist as spreadsheets (e.g., .xlsx), containing extensive tabular data. Fields include dosage, administration time, vital signs, and various test values, with diverse units such as mg, mL, mmHg, and mmol/L.
Constraints on Knowledge Base Retrieval and Recall
The structural diversity of Phase I clinical documents demands robust document parsing capabilities from the knowledge base. Accurate text extraction is required for PDF documents. For .xlsx CRFs, the knowledge base must support structured table parsing, preserving row and column relationships. This ensures precise queries for specific subject metrics. High-frequency updates for record-type documents necessitate an efficient incremental update mechanism to avoid full re-imports. The specificity of fields and units, such as drug dosages and test results, means that vectorization and retrieval processes must effectively recognize numbers and units to prevent semantic confusion (e.g., distinguishing 10mg from 100mg). Furthermore, retrieving adverse event reports requires attention to medical terminology and symptom descriptions within the text, demanding that the model possesses a strong understanding of medical vocabulary.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters (characters) | Phase I clinical protocols and reports contain lengthy background and methodology descriptions. This length helps maintain contextual completeness. |
Chunk Overlap Length (Segment Overlap Length) | 50–100 characters (characters) | Ensures information at segment boundaries is not lost, improving retrieval accuracy. |
Recall count (Recall Count) | Top 5 entries (top 5 items) | Phase I clinical queries typically require a small number of highly relevant key pieces of information. Too many items can introduce noise. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Similarity for medical terms and numerical values requires fine-tuning to avoid incorrect recalls. |
Rerank result count (Rerank Return Count) | 3 entries (3 items) | Reranking further improves the ranking of the most relevant information from the recall results, reducing the model's processing burden. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Accommodates the parsing time for large Phase I clinical study protocols or .xlsx files containing extensive data. |
Common Pitfalls
- Symptom: During knowledge base conversations, the model cannot reiterate specific row data from an
.xlsxfile or claims it cannot read the file content. Reason: The knowledge base may not have correctly parsed the table structure when importing the.xlsxfile. Content might be treated as plain text, or only partial text information extracted, losing row and column correspondence. - Symptom: When querying drug dosages or laboratory test results, the model returns results significantly different from expectations or provides irrelevant document snippets. Reason: The knowledge base's vectorization model lacks sufficient semantic understanding of numbers and units. It fails to effectively distinguish the meaning of different values or units, leading to similarity calculation deviations.
- Symptom: After uploading a revised study protocol or new adverse event records, queries still return old versions or answers that do not include the new information. Reason: The knowledge base's incremental update mechanism was not correctly triggered, or the file version management strategy is incomplete, causing the system to fail to prioritize the latest documents during retrieval.
Validation Steps
- Upload an
.xlsxfile containing multi-page tabular data. Then, query specific cells or rows through conversation to verify if the model accurately returns the corresponding content. - Upload a document describing different drug dosages (e.g.,
5mgand50mg). Then, query these dosage details separately and observe if the model's returned results can distinguish numerical differences and point to the correct context. - Upload an initial version of a study protocol and perform queries. Subsequently, upload its revised version and query the same questions again to confirm that the model returns the updated information.
- Check the knowledge base backend's document processing logs to confirm that
.xlsxfiles were imported without parsing errors or timeout warnings, and that segmenting results are as expected.
The values provided 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.