Model Integration and Configuration for CDMO Quality

Data Characteristics in the CDMO Domain

CDMO (Contract Development and Manufacturing Organization) quality documents include batch production records, inspection records, validation reports, SOPs (Standard Operating Procedures), change controls, and deviation handling. These documents typically use PDF, Word, and Excel formats. Content is highly structured but also contains extensive unstructured descriptive text. Data sources vary, including production equipment logs, laboratory analysis reports, and QA audit records. Document update frequencies differ; SOPs and validation reports might update quarterly or annually, while batch production records generate in real-time per batch. Fields and units strictly adhere to GMP (Good Manufacturing Practice) and GLP (Good Laboratory Practice) requirements. Examples include batch numbers, product names, production dates, expiration dates, test items, results, and units of measurement (mg/mL, ppm, °C), all requiring precise accuracy.

Constraints on Model Integration and Configuration from these Characteristics

The mixed structured and unstructured nature of CDMO quality documents requires models to possess strong semantic understanding for text parsing, accurately extracting key information. Inconsistent update frequencies mean the knowledge base must support incremental updates and version management, preventing outdated document information from interfering with new document retrieval. Strict field and unit requirements demand high accuracy from models in identifying entities and numerical values. Incorrect identification can lead to severe compliance risks. Extensive specialized terminology and abbreviations necessitate models with domain knowledge or adaptation through pre-training. Furthermore, document sensitivity dictates that data integration must consider access control and data isolation to ensure information security and prevent the disclosure of critical production information during model inference.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`maxContext`	4096 tokens	Ensures inclusion of key contextual information from long documents like batch production records, improving model comprehension completeness.
`Chunk size` (Segment Length)	500 characters	Balances retrieval granularity with segment completeness, preventing truncation of critical information while reducing the complexity of processing single segments.
`Recall count` (Number of Retrieved Items)	8 items	Considering quality documents may involve multiple associated SOPs or batch records, increasing retrieval volume enhances coverage.
`Similarity threshold` (Similarity Threshold)	0.78	Ensures retrieved document segments are highly relevant to the query, filtering out noise and improving answer accuracy.
`Rerank result count` (Number of Reranked Items)	Top 3 items	Focuses on the most relevant few segments through a reranking mechanism, building on a high initial retrieval volume to improve user experience.
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Accommodates parsing times for large PDFs or complex Word documents, preventing file processing failures due to timeouts.

Three Common Pitfalls

The model interface displays API Key is invalid or authentication error: This typically indicates incorrect oneAPI or model provider key configuration, or incorrect OPENAI_API_KEY and similar parameters in FastGPT's config.json file.
Uploading large documents results in a long wait or processing failure: This might relate to a PARSE_FILE_TIMEOUT_SECONDS parameter set too low, causing the parser to exceed the time limit when processing complex or large files.
Model responses contain irrelevant information or lack critical details: This could be due to a Similarity threshold (Similarity Threshold) set too low, leading to the retrieval of many irrelevant document segments, or a Chunk size (Segment Length) that is too short, causing critical context to be split.

How to Verify Correct Configuration

Upload typical batch production records, SOPs, and other documents. Check if the knowledge base successfully generates document segments and verify that segment content is reasonable and untruncated.
Conduct question-and-answer tests for specific quality issues (e.g., "deviation handling record for batch XX"). Verify if the model accurately retrieves relevant documents and provides correct answers.
Use FastGPT's debugging interface to observe the actual retrieval results for Recall count (Number of Retrieved Items) and Similarity threshold (Similarity Threshold) when processing queries. Confirm retrieved content meets expectations.
Regularly check system logs to ensure no timeout or parsing error messages related to file processing appear.

Note: The values provided are common starting points. Measure performance against specific samples to determine optimal settings.

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.