Vector Models and Indexing for Pharmacoeconomics R&D

Data Characteristics

Pharmacoeconomics R&D documents include clinical trial data reports, cost-benefit analysis reports, Health Technology Assessment (HTA) documents, evidence-based medicine reviews, and pharmacoeconomic model development reports. Data sources are diverse, encompassing medical journals, government guidelines, and internal pharmaceutical company research reports. Update frequency is relatively low, primarily occurring with new drug launches, expanded indications, or major policy changes. Document structures are complex, containing numerous tables, charts, statistical data, specialized terminology, and abbreviations. Fields cover disease epidemiology, clinical efficacy indicators, drug costs, healthcare resource utilization, quality of life scores (e.g., EQ-5D), discount rates, and sensitivity analysis parameters. Units vary, such as USD, EUR, person-years, and QALY (Quality-Adjusted Life Year).

Constraints on Vector Models and Indexing

The complex and multi-source nature of pharmacoeconomics documents challenges vector chunking strategies. Data within tables and charts requires special handling to prevent information loss or semantic fragmentation. Low update frequency means higher initial indexing costs but lower subsequent maintenance. Extensive specialized terminology and abbreviations necessitate pre-trained models with domain knowledge; otherwise, vector representation accuracy suffers. Diverse fields and units require differentiation of information types during vectorization, such as numerical values versus descriptive text. Documents often contain sensitivity analysis results with strong contextual relevance, requiring larger chunk lengths to maintain complete semantics. Failure to do so can lead to critical conclusions being incorrectly segmented.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`800–1200 characters`	Balances semantic completeness and recall efficiency, especially for sensitivity analysis and multi-paragraph arguments.
`Chunk Overlap Rate`	`0.1`	Ensures contextual continuity between paragraphs, preventing critical information from being cut off.
`Recall Count`	`10–15 items`	Pharmacoeconomics reports have strong interconnections; increasing recall helps capture comprehensive information.
`Similarity Threshold`	Calibrate based on actual measurements	Balances recall and precision based on specific datasets and model performance.
`Rerank Return Count`	`5 items`	Selects the most relevant results, reducing the processing burden on downstream language models.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides sufficient parsing time for large reports and complex tables.

Common Pitfalls

After uploading large knowledge base files, some chunk vectorization fails, displaying "Vectorization Anomaly." This occurs because a single file is too large or contains complex structures, leading to parsing timeouts, or locally deployed language models have insufficient processing capacity.
After a FastGPT version update, existing knowledge bases cannot perform vector retrieval, resulting in no search results. This happens when the new version upgrades the vector model or index format, making old indexes incompatible with the new system.
After uploading knowledge base files, the index status remains "Not Ready" or "Processing" for an extended period. This indicates an internal error during file parsing or vectorization, failing to trigger an automatic retry mechanism, or server resource exhaustion.

Verification Steps

Upload a pharmacoeconomics report containing typical tables and charts. Check the parsed chunk content to ensure table data and chart descriptions are correctly extracted and segmented.
Conduct multiple rounds of questioning on the knowledge base. Verify the recall results for specialized content, such as cost-benefit analysis, QALY calculations, and sensitivity analysis, match the original documents.
Monitor backend logs for chunk vectorization failure error messages. Check if the PARSE_FILE_TIMEOUT_SECONDS parameter causes parsing interruptions.
Use FastGPT's retrieval testing feature to adjust the Similarity Threshold. Observe changes in recall count and relevance until results meet business requirements.

Note: The values provided are common starting points and should be measured against specific 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.