Data Characteristics
Policy retrieval data for the biopharmaceutical industry originates from internal company regulations, standard operating procedures (SOPs), quality management system documents, compliance files, and training materials. These documents are typically stored in PDF, Word, Excel, or Markdown formats within internal knowledge management systems, document servers, or compliance platforms. Data update frequency is relatively low, with revisions usually occurring quarterly or annually. However, policies related to core business areas such as new drug development, clinical trials, and production quality may undergo more frequent revisions and additions. Document structures commonly include metadata such as titles, chapters, clause numbers, revision history, effective dates, and issuing departments. Text content primarily uses normative language, often containing specialized terminology, abbreviations, and referenced clauses. Beyond regular text content, fields may also include version numbers, publication dates, scope of application, and links to related policies.
Constraints Imposed by Data Characteristics on Deployment and Upgrade
The low update frequency of policy documents means a large initial data ingestion is required during deployment, but subsequent incremental update pressure is low. This allows for a reduced frequency of index rebuilding. Chapter and clause numbers in document structures highlight the importance of segmentation granularity. The system must recognize and preserve this structural information to ensure accurate retrieval results. Specialized terminology and abbreviations demand strong domain understanding from the model, requiring consideration of pre-trained model selection or domain-adaptive fine-tuning during deployment. The prevalence of PDF and Word documents necessitates stable and compatible file parsers, particularly regarding memory consumption and timeout issues when parsing large or complexly formatted documents. Furthermore, metadata such as version numbers and publication dates can filter for the latest or specific historical policy versions during retrieval. Therefore, this metadata must be correctly extracted and stored during data ingestion.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates uploading large policy manuals or compliance document packages |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Ensures sufficient parsing time for complex PDF or Word documents |
Chunk size (Segment Length) | 800–1200 characters | Balances policy clause completeness and retrieval efficiency |
Recall count (Recall Count) | 10 entries | Ensures comprehensive coverage and avoids missing critical policy clauses |
Similarity threshold (Similarity Threshold) | Calibrated by actual measurement within 0.75–0.85 | Balances retrieval precision and recall rate, reducing irrelevant results |
Rerank result count (Reranked Return Count) | 5 entries | Focuses on the most relevant policy content, improving user reading experience |
Common Pitfalls
- Symptom: Knowledge base creation or file upload fails with the message
worker terminated due to reaching memory limit. Reason: The file parser consumes excessive memory when processing large or complex documents, causing the process to be terminated by the system due to insufficient memory configuration. - Symptom: After upgrading FastGPT, some policy documents cannot be uploaded or previewed correctly, displaying
Failed to create post presigned url. Reason: Changes in the new version's file storage or signing mechanism require updating related configurations or bucket permissions. - Symptom: Retrieval results contain numerous irrelevant policy clauses or lack critical information. Reason: The segmentation strategy fails to effectively identify the logical structure of policy documents, leading to over-segmentation or over-merging of context.
Verification Steps
- Upload representative multi-format policy documents (e.g., PDFs with charts, multi-chapter Word documents) and verify successful parsing and knowledge base generation.
- Query specific policy clauses and check if the retrieval results include the clause and its context. Verify the accuracy of referenced policies.
- Simulate real user query scenarios, evaluate the relevance and completeness of the returned results, and adjust
Similarity threshold(Similarity Threshold) based on user feedback.
The values provided are common starting points. Measure them 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.