Data Characteristics for This Category
Quality documents in Chemistry, Manufacturing, and Controls (CMC) research include raw data, analytical reports, batch production records, validation protocols and reports, stability study data, and regulatory submission documents. These documents are typically in formats such as PDF, Word, and Excel. Some raw data may be stored as binary files or images from specific instruments. Data update frequency correlates closely with the research phase; early research phases involve frequent updates, while later regulatory submission phases are more stable. Document structures are highly standardized, adhering to guidelines like the ICH (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) Q series. Fields include batch number, production date, expiry date, analytical method, test results (e.g., content, purity, dissolution), units of measurement (e.g., mg/mL, %), and equipment parameters. Precision and traceability requirements are extremely high.
Constraints Imposed by These Characteristics on Workflow Orchestration
The high standardization and data sensitivity of CMC quality documents impose strict requirements on workflow orchestration. Document update frequency dictates the knowledge base synchronization strategy. Frequently updated raw data requires faster indexing cycles, while stability reports can use periodic updates. The structured nature of documents allows for extracting key information using predefined parsing rules, such as locating batch numbers and test results via regular expressions or table recognition technology. The demands for precision and traceability mean that during knowledge extraction and question answering, model hallucination must be strictly limited to ensure the accuracy of cited content and traceability to original document page numbers or paragraphs. Furthermore, the presence of multiple document formats requires the workflow to have robust file preprocessing capabilities to uniformly convert them into indexable text formats.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
Chunk size | 500–800 characters | Balances semantic completeness and recall efficiency, preventing individual segments from being too large and introducing irrelevant information. |
Recall count | Top 5 entries | Ensures the model obtains sufficient context while controlling input length and reducing inference costs. |
Similarity threshold | 0.75–0.85 | Filters out low-relevance content, improves recall accuracy, and reduces misleading information. |
Rerank result count | Top 3 entries | Further refines the most relevant content from recall results, enhancing final answer quality. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles large PDFs or documents containing complex tables, preventing parsing timeouts. |
maxContext | 8000 tokens | Accommodates multiple high-quality recalled segments and provides sufficient space for model inference. |
Three Common Mistakes
- After document upload, knowledge base search fails to correctly reference the latest document content. This occurs because the knowledge base update strategy is not set to real-time or is insufficiently periodic, leading to indexing lag.
- In answers generated by the workflow, some key fields (e.g., test results) are blank or inaccurate. This happens when document parsing rules fail to precisely match specific fields in different document formats, or when regular expressions are flawed.
- Workflow execution encounters timeout errors, especially when processing a large volume of historical batch production records. This may be due to a lack of parallel optimization in file preprocessing or vectorization, or because
PARSE_FILE_TIMEOUT_SECONDSis set too low.
How to Confirm Correct Configuration
- Upload a batch of typical CMC quality documents. Verify that the knowledge base index status shows "completed" and confirm through knowledge base preview that segment content is correct and not truncated.
- Ask questions about specific batch numbers, test results, and other key information within the documents. Cross-reference whether the model's answer accurately cites original text snippets and can be traced back to the original document page number.
- Simulate multiple document uploads and queries under high concurrency. Monitor workflow execution time to ensure average response times are within an acceptable range and no timeout errors occur.
The values provided are common starting points. They 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.