Data Characteristics
Stability study data primarily originates from internal pharmaceutical quality management system documents. These include stability study protocols, stability test reports, change control documents, and deviation reports. Documents are typically in PDF, Word, or scanned image formats, with varying degrees of structural organization. Update frequency varies: new drug development phases may see monthly updates, while post-market updates occur annually or are triggered by changes. Documents contain specialized terminology, charts, batch information, time points, temperature/humidity conditions, and specific test data (e.g., content, dissolution, impurities). Units are diverse; for example, content is often in %, impurities in ppm or g/L, time in months/days/hours, and temperature in ℃.
Constraints from "Vector Models and Indexing"
The diversity and specialized nature of stability study documents impose specific requirements on vector models and indexing. Charts and scanned images within documents require OCR technology for text extraction to prevent information loss. The update frequency dictates the index's rebuild or incremental update strategy to maintain knowledge base timeliness. Extensive specialized terminology and acronyms, such as API (Active Pharmaceutical Ingredient) and ICH (International Council for Harmonisation), demand that vector models possess strong domain-specific semantic understanding, enabling them to disambiguate terms based on context. Furthermore, the ability to identify key entities like batch numbers and time points is crucial for precise recall of specific stability batch information. The mixture of structured data and unstructured text also requires indexing strategies that can effectively handle the fusion and retrieval of different information types.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 500-800 characters | Balances contextual completeness with the precision of vector representation, preventing overly long chunks from diluting key information or overly short chunks from fragmenting context. |
overlap_size | 100-150 characters | Ensures sufficient contextual overlap between chunks, improving the coherence of information recall across chunks. |
embedding_model | Domain-optimized model or general large model | Stability studies involve extensive specialized terminology; domain-optimized models better capture semantics. General large models offer advantages in contextual understanding. |
recall_top_k | 5-10 items | Balances recall efficiency and relevance, ensuring coverage of potentially relevant information and providing enough candidates for subsequent reranking. |
similarity_threshold | Calibrated by empirical testing | Adjust based on actual recall effectiveness and false positive rates, typically between 0.75-0.85, to ensure result relevance. |
parser_config.ocr_enabled | true | Many stability reports are scanned images; enabling OCR is critical for extracting text content. |
Common Pitfalls
- Query results are irrelevant after index construction. The returned text snippets lack a direct connection to the query. This occurs when chunking granularity is too large or too small, diluting key information or fragmenting context, failing to effectively capture the core semantics of stability studies.
- Some stability report content is not retrievable. Queries for specific batch or date data yield no results. This happens when the document parsing stage does not adequately process text in scanned images or pictures,
parser_config.ocr_enabledis not correctly configured, or OCR recognition performance is poor. - Queries return outdated information after data updates. This indicates that the knowledge base's incremental update mechanism was not triggered, or the update frequency does not match the actual data change frequency, preventing the index from synchronizing the latest stability study data in a timely manner.
Verification Steps
- Select several representative stability study questions, such as querying the impurity trend of a specific drug batch. Observe whether the recall results include key batch numbers, test dates, and relevant data.
- Randomly select a batch of stability reports containing scanned images. Upload and index them. Then query for unique text content within these reports. Check if it can be accurately recalled to verify OCR functionality.
- Modify the content of an already indexed stability study protocol (e.g., update a test standard). Then re-trigger an index update and query the modified content. Confirm whether the knowledge base's timeliness threshold is reasonable.
- Use the FastGPT debugging interface to check vector similarity scores and the recalled raw text blocks. Determine if
chunk_sizeandoverlap_sizeconfigurations are appropriate, and adjustsimilarity_thresholdbased on actual semantic relevance.
Note: 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.