Data Characteristics
Antibody-Drug Conjugate (ADC) regulation and SOP documentation primarily originates from internal pharmaceutical quality management systems, R&D and production departments, and regulatory guidelines. This data typically exists as unstructured documents in formats like PDF and DOCX. Content covers drug development, manufacturing processes, quality control, clinical trials, and regulatory submissions. Document update frequency is high; SOPs may be revised monthly during R&D and clinical phases, while regulatory documents might update annually.
Documents have a strict structure, including chapters, sections, and appendices. Fields include batch numbers, expiration dates, formulation types, dosage units (e.g., mg/kg), stability data, operating procedures, and responsible parties. Data often contains complex charts, flowcharts, and chemical structures. These non-textual elements pose challenges for Retrieval Augmented Generation (RAG) systems.
Constraints on the HTTP Interface and External Systems
The highly unstructured nature of ADC regulation and SOP documents requires the HTTP interface to effectively process multiple file formats during data extraction. High update frequency necessitates efficient document synchronization mechanisms in external systems to ensure knowledge base timeliness.
The strict internal document structure, such as chapter headings and appendices, must retain its hierarchical relationships after data extraction for subsequent semantic understanding and question answering. Specific fields, like batch numbers, expiration dates, and mg/kg units, require the HTTP interface to accurately identify and extract them during parsing, preventing misinterpretation. Complex charts and chemical structures cannot be directly extracted as text. This limits the RAG system's ability to answer questions involving these non-textual elements, potentially requiring additional image recognition or specialized knowledge base integration.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 500 characters | ADC documents have complex structures; overly long chunks reduce semantic precision, while overly short ones lose context. |
Recall count (Recall Count) | 8 entries | Ensures coverage while avoiding irrelevant information, balancing retrieval efficiency and relevance. |
Similarity threshold (Similarity Threshold) | 0.78 | Ensures precision of recalled content, filtering out low-relevance results. |
Rerank result count (Rerank Return Count) | 3 entries | Focuses on the most relevant content, reducing model processing load. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | ADC documents often contain many pages and complex structures, requiring sufficient parsing time. |
CHUNK_OVERLAP_SIZE | 50 characters | Ensures contextual continuity between chunks, especially suitable for procedural SOPs. |
Common Pitfalls
- The HTTP interface returns a
401 Unauthorizederror. This typically occurs because external system authentication credentials (e.g.,API Keyortoken) have expired or are misconfigured. - After knowledge base synchronization, some document content is missing or formatted incorrectly. This happens when the HTTP interface's parser fails to correctly recognize complex tables or charts in PDF or DOCX files.
- In RAG query results, values for certain fields (e.g.,
batch numbers,dosage units) are empty. This occurs because the data extraction stage did not apply regular expression matching or specific parsing rules for field patterns unique to ADC documents.
Verification Steps
- Using the external system's API interface, manually upload and parse an ADC regulation document containing charts and special units (e.g.,
mg/kg). Observe its content completeness in the knowledge base. - For a revised SOP document, trigger an update via the HTTP interface. Verify that the update time and version of the corresponding content in the knowledge base are consistent.
- Test the RAG question-answering system with queries containing specific fields like
batch numbersandexpiration dates. Confirm that the results accurately include this key information and that units are correct.
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.