Data Characteristics
Cleaning validation data primarily originates from internal quality management system documents, validation protocols, validation reports, deviation records, and change control documents. These documents are typically in PDF, Word, or scanned image formats. Some data may exist in Excel spreadsheets. Document update frequency is relatively low, with revisions mainly occurring during regulatory updates, equipment changes, production process adjustments, or periodic reviews. Document structures are rigorous, containing extensive technical terminology, regulatory citations, and detailed experimental data. Fields often include equipment names, cleaning agent batch numbers, sampling points, residue limits, detection methods, and test results. Units include ppm, μg/cm², mg/L, and may also include units specific to pharmacopoeia or industry standards.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The heterogeneous nature of cleaning validation data requires the workflow to support parsing and structuring various file formats during data ingestion. Low document update frequency means real-time synchronization is not a high priority for knowledge base construction, but initial import must ensure data completeness. The specialized terminology and regulatory citations in documents demand high model comprehension. This requires refined preprocessing and domain-specific vocabularies to enhance semantic understanding. Furthermore, critical numerical fields like residue limits and test results must be precisely extracted and compared during Q&A to avoid misjudgments due to incorrect numerical recognition. The workflow needs dedicated validation nodes to verify the format and range of extracted numerical data. Lengthy, structured documents mean a single text chunking strategy might be insufficient. Intelligent segmentation, combined with document structure, is necessary to improve recall accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 32000 | Addresses the long-text nature of cleaning validation reports, ensuring sufficient context length to cover complete content. |
Chunk size (Chunk Length) | 800–1200 characters (characters) | Balances semantic completeness and recall efficiency, avoiding excessive fragmentation or overly large single-chunk information. |
Recall count (Recall Count) | Top 5 entries (top 5) | Balances relevance and processing cost, covering core information points. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Based on the semantic similarity distribution of domain documents, prevents false recalls or missed recalls. |
Rerank result count (Reranked Return Count) | Top 3 entries (top 3) | Focuses on the most relevant key information, reducing user reading burden. |
Parsing Timeout | 600 seconds (seconds) | Addresses the complex parsing requirements of large PDFs or scanned images, preventing processing failures due to timeouts. |
Common Mistakes
- After workflow deployment, external API calls return an HTTP 400 error code. This typically occurs when parameters in the API request body do not match the input parameters defined in the workflow, such as misspelled field names or data type mismatches.
- Audio and video tags fail to render in specified reply nodes. This happens because Markdown renderers do not support HTML
videooraudiotags by default. Custom components or specific syntax extensions are required for implementation. - Key fields, such as cleaning agent batch numbers, appear as empty values in Q&A results. This may be due to incorrect recognition of specific field formats during document parsing, or the absence of corresponding regular expressions or entity recognition rules during structured extraction.
How to Verify Correct Configuration
- Trigger the workflow via API calls. Check if the returned JSON structure and field values meet expectations, especially the precision of numerical fields.
- Upload a typical cleaning validation report document. Observe the knowledge base segmentation effect, ensuring important paragraphs are not truncated and technical terms are correctly identified.
- Ask specific cleaning validation questions. Compare the model's answers with the original document content to evaluate the accuracy of key information recall and extraction.
- Simulate abnormal inputs, such as requests missing critical parameters. Verify if the workflow's error handling mechanism responds effectively and provides clear prompts.
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.