Data Characteristics for This Category
CDMO (Contract Development and Manufacturing Organization) product data is typically highly structured. It originates from research and development reports, manufacturing batch records, quality control documents, and regulatory submission materials. Data update frequency varies by project stage. Early-stage R&D data might update weekly, while production batch data generates per batch. Document formats are diverse. Examples include PDF experimental protocols, Word analysis reports, Excel bills of materials and production parameters, and structured data exported from LIMS systems. Fields and units are highly specialized. For instance, "yield" might be expressed as a percentage (%), "purity" as HPLC area normalization percentage (% Area), and "culture medium component concentration" in milligrams per liter (mg/L). Documents often contain numerous charts, complex tables, and frequent cross-document references.
Constraints Imposed by These Characteristics on Model Integration and Configuration
The structured and specialized nature of CDMO product data requires refined data preprocessing and feature extraction during model integration. Diverse document formats necessitate robust document parsing capabilities, especially for complex tables within PDFs and text within images. High data update frequency can challenge knowledge base real-time capabilities, requiring consideration of incremental update mechanisms. The specialized fields and units demand higher semantic understanding from the model, particularly in associating numerical data with specific terminology. Cross-document references require the model to possess contextual correlation and multi-document retrieval capabilities. These constraints dictate that model configuration must prioritize improving document parsing accuracy, knowledge base update efficiency, and specialized terminology embedding representation.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | CDMO documents often contain high-resolution charts and large amounts of data, leading to large individual file sizes. |
maxContext | 8000 tokens | Ensures the model can process detailed reports containing complex experimental procedures and analysis results. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large file parsing and OCR recognition are time-consuming. This prevents parsing timeouts that lead to task failure. |
Chunk size | 800 characters | Balances semantic integrity with retrieval efficiency, preventing information fragmentation after segmentation. |
Recall count | 10 entries | Increases the likelihood of recalling relevant key information from a large volume of specialized documents. |
Similarity threshold | Calibrate by actual measurement | CDMO terminology has high similarity. This requires adjustment based on specific data to distinguish subtle differences. |
Three Common Mistakes
- A model is enabled in the model configuration interface but cannot be found in the workflow. The workflow model selection dropdown menu is empty or does not include the expected model. This occurs because the model channel configuration is not correctly linked to FastGPT's internal model list, or the model channel status is not updated to "Enabled."
- After uploading a large PDF document, the parsing task remains in "Processing" for an extended period and eventually fails with a timeout. Logs show
PARSE_FILE_TIMEOUT_ERROR. This might be because thePARSE_FILE_TIMEOUT_SECONDSconfiguration is too low and does not cover the parsing time for complex documents. - The model's response regarding the purity or yield data of a specific compound shows significant discrepancies or unit errors. This might occur because the document parsing stage failed to accurately extract numerical values and their corresponding unit information from tables, leading to inaccurate data input for the model.
How to Confirm Correct Configuration
- Upload multiple CDMO documents containing complex tables and specialized terminology (e.g., batch production records, analytical method validation reports). Check if the segmented content in the knowledge base is complete, especially whether table data and professional terms are correctly extracted.
- Through API or interface testing, query key production parameters and quality control indicators in the knowledge base. Verify if the model can accurately recall relevant document segments and provide answers with correct numerical values and units.
- Check the status of model channels. Ensure all configured models show as "Enabled." Confirm that these models can be selected and called normally within the workflow editing interface.
- Upload and parse a document known to be time-consuming to parse. Observe if the parsing task completes within the
PARSE_FILE_TIMEOUT_SECONDSsetting. Confirm no parsing timeout errors occur.
Note: The values provided are common starting points. Measure them against your 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.