Data Characteristics in CMC Research
CMC (Chemistry, Manufacturing, and Controls) research data originates from experimental reports, production batch records, quality control documents, and registration submission materials. This data exists in both structured and unstructured forms. Examples include CSV files exported from laboratory LIMS systems, PDF reports written by researchers, MES system logs from manufacturing processes, and image and spectral data generated by QC instruments. Data update frequency varies significantly across project phases. Early research may see new data generated weekly or even daily, while late-stage stability studies might update monthly or quarterly. Document structures typically follow GLP/GMP guidelines, with fixed sections like experimental objectives, methods, results, and conclusions, but content expression can be diverse. Fields and units include compound purity (%), impurity content (ppm), stability data (months), solubility (mg/mL), batch numbers, and production dates. The standardization and consistency of units are critical for subsequent analysis.
Constraints Imposed by Data Characteristics on Workflow Orchestration
The diverse origins and mixed structures of CMC research data require workflows with robust file parsing and heterogeneous data integration capabilities. For example, unstructured content in PDF reports needs advanced OCR and natural language processing to extract key information, while CSV files exported from LIMS require precise column mapping and data type conversion. Inconsistent data update frequencies necessitate flexible workflow triggering mechanisms. Some tasks may require real-time responses to new data uploads, while others can be scheduled periodically. Adherence to GLP/GMP guidelines for documents means information extraction must accurately identify compliance-related fields such as experimental methods, key parameters, and batch information to ensure the reliability of pre-screening results. Field and unit standardization directly impacts data cleaning and validation. Workflows must incorporate unit conversion and numerical range validation logic to prevent pre-screening deviations caused by inconsistent units or anomalous data.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large experimental report PDFs require sufficient timeout for parsing. |
Chunk size (Segment Length) | 800–1200 characters | Balances semantic completeness with model processing efficiency. |
Recall count (Recall Count) | Top 10 | Ensures coverage of highly relevant CMC experimental results and batch data. |
Similarity threshold (Similarity Threshold) | 0.75 | Filters for CMC data highly relevant to clinical trial pre-screening conditions. |
Rerank result count (Rerank Return Count) | 5 | Refines information presented to the model, reducing noise. |
maxContext | 8192 | Accommodates more CMC experimental details and background information. |
Common Pitfalls
- Calling database tools results in an error when passing SQL parameters via variables, while manually entering the SQL statement executes normally. This typically occurs due to variable type mismatches or triggered SQL injection defense mechanisms.
- The workflow cannot call configured external tools (e.g., database connectors or custom APIs). This might happen if the deployment environment (e.g., Docker) network configuration restricts FastGPT's access to external services, or if the tool configuration lacks necessary authentication information.
- The system fails to dynamically switch or select knowledge bases in real-time during a conversation to respond to user queries. This usually happens because the knowledge base selection logic was not integrated into the conversation loop during workflow design, or the trigger conditions for knowledge base selection are not clear enough.
How to Verify Configuration
- Upload CMC experimental reports in various formats (PDF, CSV, DOCX). Check if corresponding segments are successfully generated in the knowledge base and verify that key fields (e.g.,
compound purity,batch number) are accurately extracted. - Perform simulated queries for core clinical trial pre-screening conditions. Verify that the workflow accurately recalls relevant CMC research document fragments and check if the recall count and similarity scores meet expectations.
- Use SQL statements with variables to query CMC production batch information via the workflow's database connection tool. Verify that variables are correctly passed and executed, and that the returned results match expectations.
- Integrate multiple knowledge bases into the workflow. Simulate users asking different types of queries during a conversation. Confirm that the workflow dynamically selects the appropriate knowledge base for retrieval based on the query content.
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.