Data Characteristics for ADC Quality Documents
ADC quality documents cover the entire lifecycle, from R&D to production, quality inspection, and release. Data sources vary. These include lab records, Batch Production Records (BPR), Batch Journal Records (BJR), stability study reports, supplier qualification documents, deviation reports, and change control documents. Documents often combine structured and unstructured formats. Examples include PDF lab reports, Word SOPs, and CSV or Excel data exported from LIMS. Update frequency depends on the drug's development stage and production batch. Early R&D stages might see weekly or even daily updates. Commercial production stages primarily rely on batch releases and annual reviews. Documents contain extensive specialized terminology, chemical structures, spectral data, units (e.g., mg/mL, ug/kg, %, AU), and key fields like batch numbers and expiration dates.
Workflow Orchestration Constraints from These Characteristics
The mixed structure of ADC quality documents challenges workflow orchestration. Parsing unstructured text requires strong semantic understanding to ensure accurate extraction of key information. High update frequency demands incremental processing capabilities in the workflow. This avoids reprocessing historical data and ensures real-time knowledge base updates. Specialized terminology and spectral data in documents require knowledge base retrieval and reranking modules to effectively handle domain-specific entities. This prevents retrieval failures due to vocabulary mismatches. Extracting units and batch numbers requires specific entity recognition models or rules. Additionally, the strict requirements for quality documents necessitate data traceability in the workflow. This includes recording document sources, parse timestamps, and processing versions to meet compliance. Parsing large volumes of documents can lead to long run times. Workflows need to consider timeout handling and intermediate state saving mechanisms.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Adapts to the moderate paragraph length and high information density of ADC quality documents. This avoids information overload or scarcity in a single segment. |
Recall count | Top 8–12 entries | The specialized nature of ADC quality documents requires initial retrieval to cover as much potentially relevant content as possible. This ensures sufficient candidates for subsequent reranking. |
Similarity threshold | 0.75–0.85 | Ensures retrieved results are highly relevant to the query intent. This reduces irrelevant noise and improves query accuracy. |
Rerank result count | 3–5 entries | After reranking, select the most relevant items to provide to the user. This reduces information overload and improves response efficiency. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | ADC quality documents may contain many pages or complex charts. Parsing time can be long. This allows sufficient time to prevent timeouts. |
maxContext | 32000 Word CNY | Addresses complex quality queries where context may need to include multiple document fragments and conversation history. This ensures completeness. |
Common Pitfalls
- Symptom: Model responses lack critical batch numbers or experimental data. Reason: The entity recognition module in the workflow failed to accurately identify specific data formats in the document. This led to information extraction failure.
- Symptom: A user queries the latest batch quality report, but the response contains outdated information. Reason: The document parsing module lacks an incremental update strategy, or the knowledge base did not synchronize the latest uploaded documents in time.
- Symptom: The workflow frequently errors or times out when processing PDF documents with many charts and scanned images. Reason: The document parser has insufficient capability to process non-text content, or insufficient processing time was configured. This caused parsing tasks to interrupt.
Configuration Verification
- Upload a typical new batch quality report. Check if the knowledge base contains the latest updated key fields and data. Perform query tests to verify the timeliness of retrieval results.
- Use query statements containing specific units and specialized terminology. Verify the accuracy and completeness of this information in the model's response. Ensure correct entity recognition and extraction.
- Perform parsing tests on ADC quality documents with different structures (e.g., plain text SOPs, experimental report PDFs with charts). Observe the workflow execution status. Confirm no timeouts or parsing failures.
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.