Data Characteristics
Bioequivalence quality documents originate from drug registration applications, pharmaceutical research reports, clinical trial reports, pharmacopoeia standards, and regulatory guidelines. These documents update infrequently, primarily when regulations change or new drug approvals occur. Document structures are complex, often containing numerous tables, figures, formulas, experimental data, and specialized terminology. Fields and units are highly standardized. For example, pharmacokinetic parameters (AUC, Cmax, Tmax) commonly use units like ng·h/mL, ng/mL, and h. Solubility and dissolution data involve units like mg/mL and %. Documents frequently include cross-references and attachments.
Constraints on Knowledge Base Retrieval and Recall
The complex structure and specialized terminology of bioequivalence documents require the knowledge base to maintain the integrity of professional content during segmentation. This prevents critical information from being fragmented. The presence of tables and formulas challenges document parsing capabilities; pure text retrieval may miss important context. Highly standardized fields and units necessitate precise matching and numerical range retrieval. Fuzzy matching can lead to inaccurate results. The low update frequency means the knowledge base must store and manage this data stably over the long term, while efficiently performing incremental training for minor updates. Cross-references and attachments require retrieval results to link related information effectively, providing comprehensive context.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters (characters) | Ensures the integrity of pharmaceutical and clinical discussions, preventing truncation of critical information. |
Overlap Length | 100–200 characters (characters) | Guarantees continuity of context at segment boundaries, improving retrieval recall rate. |
Recall count (Recall Count) | 8–12 entries (items) | Balances retrieval breadth with controlling the size of the result set, facilitating subsequent re-ranking and comprehension. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Balances precise matching with semantic relevance, filtering out low-relevance results and reducing noise. |
Rerank result count (Re-ranked Return Count) | 3–5 entries (items) | Focuses on the most relevant document snippets, improving the accuracy and conciseness of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Addresses long parsing times for large PDF documents, preventing parsing interruptions. |
Common Pitfalls
- After knowledge base document training, some document content is missing or formatted incorrectly. This occurs because the PDF parser fails to correctly recognize text within complex tables or embedded images.
- The debugging page retrieves relevant knowledge, but API calls for some questions do not find knowledge base results. This happens when API request parameters
similarityorlimitare set too strictly, filtering out valid results. - After document upload, the document remains in a "training" state for an extended period and eventually reports an error. This occurs when the document file size exceeds the
UPLOAD_FILE_MAX_SIZElimit or the document content is too large, causingPARSE_FILE_TIMEOUT_SECONDSto time out.
Verification Steps
- Upload a typical bioequivalence report. Check if the parsed document segments are logically complete, paying close attention to whether text near figures and formulas is correctly extracted.
- Conduct retrieval tests for specialized questions, such as key pharmacokinetic parameters or statistical analysis methods. Check if the recalled results include critical paragraphs from multiple relevant documents and verify the accuracy of their specialized terminology and data.
- Simulate actual business scenarios. Use questions with varying confidence levels for API call testing. Observe the
data.lengthandsimilarityvalues in the returned results to ensure that expected relevant information is recalled within reasonable thresholds.
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.