Data Characteristics
CDMO (Contract Development and Manufacturing Organization) quality documents cover the entire product lifecycle, from raw material receipt to finished product release. This includes batch production records, inspection records, validation reports, deviation handling, change control, and client audit reports. These documents are typically stored as PDFs, Word files, or Excel spreadsheets. Some more digitized companies maintain structured database records. Update frequency depends on production batches, regulatory revisions, and client requirements. Some documents, like batch records, are generated frequently, while others, like validation reports, are relatively stable. Documents often contain extensive technical terms, chemical formulas, process parameters, units of measurement (e.g., mg/mL, kPa, ℃), and charts.
Constraints on Knowledge Base Retrieval and Recall
The specialized nature, diversity, and update frequency of CDMO quality documents pose multiple challenges for knowledge base retrieval and recall. High volumes of batch records and inspection data require efficient segmentation and indexing strategies to prevent single recalls from being too broad or too narrow. Embedded charts and non-textual information, such as flowcharts and chromatograms, may require additional processing mechanisms to ensure retrievability. Specialized terminology and abbreviations demand domain knowledge from the model to accurately understand query intent. Furthermore, the strictness of regulations and client audit reports makes the accuracy and traceability of recall results critical; any mis-recall could lead to severe consequences.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters | Balances information completeness of individual knowledge blocks with retrieval efficiency, preventing overly long segments from diluting core information. |
Chunk Overlap Length (Segment Overlap Length) | 50–100 characters | Ensures contextual continuity, especially for specialized terms or process descriptions spanning multiple paragraphs. |
Recall count (Number of Retrieved Items) | 8–12 items | Controls the input length processed by the model while ensuring coverage, reducing interference from irrelevant information. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Adjust using test sets to balance recall precision and recall rate according to business scenario requirements. |
Rerank result count (Number of Reranked Items) | 3–5 items | Further refines recall results, placing the most relevant document snippets at the forefront to improve user experience. |
PARSE_FILE_TIMEOUT_SECONDS | 180 seconds | Addresses potentially long parsing times for large PDFs or complex Word documents. |
Common Mistakes
- Symptom: Image content is not retrievable during knowledge base Q&A. Reason: The file parser does not perform OCR on embedded images, or image content is not converted into retrievable text.
- Symptom: After configuring the knowledge base, initial loading or retrieval speed significantly slows down. Reason: The knowledge base is massive, indexing strategies are not optimized, or computational resources are insufficient to support real-time vector embedding and retrieval.
- Symptom: Retrieval results contain many irrelevant batch production records. Reason: Segmentation granularity is too fine or too coarse, leading to missing context in individual knowledge blocks, or similarity calculation does not adequately consider domain-specific semantics.
How to Verify Configuration
- Select a batch of test questions containing technical terms, process parameters, and key regulatory provisions. Observe the accuracy and relevance of the recall results.
- Verify whether the knowledge base can successfully parse and retrieve document snippets containing charts, chemical formulas, and other non-pure text content.
- Simulate high-concurrency query scenarios. Monitor retrieval latency and system resource utilization to ensure performance meets production requirements.
- Regularly review recall logs. Analyze queries with failed or low-relevance recalls. Adjust segmentation strategies and retrieval parameters based on this analysis.
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.