Data Characteristics
Cleaning validation R&D documents include analytical method validation reports, residue limit calculation reports, sampling plans, and inspection records. These documents are typically in PDF, Word, or scanned image formats, with varying degrees of structural organization. Data sources usually include R&D laboratories, manufacturing quality departments, and external partners. The update frequency is relatively low, primarily occurring during new product development, process changes, or regulatory updates. Documents contain extensive specialized terminology, chemical names, equipment models, batch information, test results (e.g., units like ppm, ppb, µg/cm²), calculation formulas, and regulatory citations. Fields are often semi-structured text, with tables and chromatograms also being common components.
Constraints Imposed by These Characteristics on Reference and Traceability
The low update frequency of cleaning validation documents means that initial knowledge base construction requires a comprehensive import of historical data. Subsequent incremental updates will have less pressure, but revisions to a single document may involve updates to multiple related contents. The extensive specialized terminology and units in the documents demand that the model accurately recognizes and understands context to avoid ambiguous references. The presence of semi-structured text and complex tables challenges document preprocessing and chunking strategies, requiring that critical information is not fragmented. Specifically, references involving residue limit calculations and regulatory clauses must be precisely traceable to specific paragraphs or table rows in the original reports to support the scientific validity and compliance of conclusions. Entity information such as batch numbers and equipment models within the data are key anchor points for traceability, requiring effective indexing and retrieval after structured analysis.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 500–800 characters (characters) | Balances context completeness with search recall efficiency, preventing long chunks from diluting key information. |
Recall count (Recall Count) | 8–12 entries (items) | Ensures multi-angle coverage of search results, addressing complex queries that require multiple knowledge points. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Ensures semantic relevance of recalled content, avoiding irrelevant or low-quality references. |
Rerank result count (Reranked Return Count) | 3–5 entries (items) | Focuses on the most relevant reference snippets, improving the precision of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Accommodates parsing time for large PDF or Word documents, preventing processing failures due to timeouts. |
embeddingModel | text-embedding-ada-002 | Balances accuracy and cost-effectiveness, meeting the embedding needs for specialized vocabulary in the biomedical field. |
Common Pitfalls
- The reference results contain a large amount of irrelevant or duplicate content. This occurs when the
Similarity threshold(Similarity Threshold) is set too low, leading to the recall of semantically unrelated content. - AI answers reference generic paragraphs at the beginning or end of documents, but the actual key information is in the middle of the document. This occurs when the
Chunk size(Chunk Length) is too long, diluting effective information, or when the chunking strategy does not consider the document's logical structure. - The
Reference Knowledge Base IDfield is empty or incomplete when calling the FastGPT conversation interface. This occurs when interface parameters are not correctly configured, failing to extract and pass reference information from the model output.
How to Verify Correct Configuration
- For typical cleaning validation queries, check the reference list below the AI answer. Ensure each reference traces back to a specific paragraph in the original document, and that paragraph indeed contains the key information cited in the answer.
- Randomly select 10 parsed cleaning validation documents. Check their structured data and chunking results to confirm that critical entity information such as batch numbers and detection units (e.g., ppm, µg/cm²) are correctly identified and retained.
- Use queries containing specific regulatory clauses or calculation formulas. Verify that the AI answer accurately references the corresponding regulatory section or formula statement in the original document and compare it with the original document content.
- Simulate multiple concurrent user queries. Monitor the success rate of document parsing under the
PARSE_FILE_TIMEOUT_SECONDSparameter setting to ensure stable provision of reference sources even under high load.
Note: The values provided are common starting points. They should be measured 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.