Vector Models and Indexing for Pharmacovigilance in

Data Characteristics

Biopharmaceutical equipment pharmacovigilance data originates from equipment operation manuals, maintenance records, fault reports, software update logs, and post-market surveillance reports. This data updates frequently, especially software version iterations and fault reports, typically on a quarterly or monthly basis. Document structures vary, including detailed technical documents in PDF format, event reports in XML format, and equipment parameters and alarm information in structured databases. Specific fields include device serial number, batch number, firmware version, calibration date, operating mode, and sensor readings. Data also contains numerous specialized acronyms. Units involve physical quantities like voltage, current, pressure, temperature, and flow, as well as operational indicators like time and count.

Constraints on Vector Models and Indexing

The diverse nature of biopharmaceutical equipment data challenges vector models, requiring them to handle multimodal information, particularly combining text and structured data. High update frequency demands efficient incremental update capabilities from the indexing system to avoid lengthy full rebuilds. Complex document structures, with many charts, illustrations, and tables in PDFs, require advanced document parsing to accurately extract and segment text content. The use of specialized terminology and acronyms necessitates high vocabulary coverage for embedding models, potentially requiring domain-specific fine-tuning. The precision of equipment parameters and units requires the vectorization process to differentiate numerical values and recognize the semantic impact of units, preventing inaccurate recall due to unit conversion or recognition errors.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size`	`500–800 characters`	Balances detailed descriptions in equipment documentation with contextual completeness, avoiding segments that are too long or too short.
`Chunk Overlap Length`	`100 characters`	Ensures contextual continuity and reduces semantic breaks caused by segment boundaries.
`Recall count`	`Top 10 entries`	Balances recall precision with computational overhead, covering potentially relevant information.
`Similarity threshold`	Calibrate based on measurements	Finds a balance between precision and recall based on actual business needs and test results.
`Rerank model`	`BGE-Reranker-large`	Improves the ranking quality of recall results, especially for complex queries.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles the parsing time for large equipment manuals or complex reports, preventing timeout errors.

Common Pitfalls

The knowledge base fails to index critical chart descriptions in equipment operation manuals. This occurs because the default document parser lacks sufficient capability to recognize embedded text within images or complex table layouts.
The Embedding model reports a Connection refused error when connecting to OneAPI. This typically indicates the OneAPI service is not running or network configuration is incorrect, preventing port access.
The Rerank model is configured but does not take effect during online recall testing, with no noticeable change in the ranking of recall results. This may be due to the reranking function not being enabled during knowledge base indexing, or the Rerank model configuration being incorrect and failing to load properly.

Verification Steps

Upload an equipment fault report containing complex charts and specialized terminology. Check the knowledge base content preview to ensure all text information, including chart titles and annotations, is fully extracted.
Use queries containing structured information such as device serial numbers and firmware versions. Test whether the recall results accurately match relevant document segments and verify the precision of matched fields.
For a document known to contain adverse event descriptions, perform multiple queries. Adjust the Similarity threshold to observe changes in the number of recall results and their relevance, determining an appropriate threshold range.
Through the FastGPT administration interface, verify that the Embedding model and Rerank model status show "Connected" or "Running normally," and check relevant log outputs for any anomalies.

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.