Data Characteristics
Market access R&D documents typically include clinical trial reports, pharmacokinetic data, toxicology studies, manufacturing process validation files, and regulatory communication records. Data sources are diverse, originating from internal R&D teams, external Contract Research Organizations (CROs), and regulatory authority guidelines. Document update frequency is relatively low, primarily concentrated during key R&D milestones and regulatory approval processes. Document structure is complex, often consisting of unstructured or semi-structured text such as PDFs, Word documents, and scanned images, containing numerous tables, charts, and embedded objects. Fields and units are highly specialized, for example, dosage (mg/kg), concentration (μg/mL), p-values, and confidence intervals. Terminology and abbreviations may not be entirely consistent across different files, and a large number of biomedical-specific technical terms are present.
Constraints on Database and Operations
The complex structure and specialized terminology of market access R&D documents require a database with robust unstructured data processing capabilities and support for flexible text parsing and rich text extraction. Low update frequency means relatively low database write pressure, but historical data version management and audit trail features are crucial. Embedded tables and charts within documents require specific parsers to ensure accurate extraction of structured information. Identifying specialized fields and units places high demands on vector database embedding models, which need to accurately capture semantic relationships in the biomedical domain. Furthermore, due to data sensitivity, operations must prioritize data security, access control, and compliance auditing to ensure data is not accessed or tampered with without authorization. High concurrent requests may occur when multiple users simultaneously retrieve documents related to a specific drug or indication. This requires FastGPT to have good concurrent processing capabilities when answering queries and writing to the database to avoid significant query delays.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Market access documents often contain many charts and scanned images, leading to large file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing complex PDF and Word documents can be time-consuming, requiring sufficient parsing time. |
Chunk size | 800 characters | Balances semantic completeness and vector embedding efficiency, avoiding redundant information in long paragraphs. |
Recall count | Top 10 entries | Ensures enough relevant context is retrieved from a large volume of specialized documents to improve accuracy. |
Similarity threshold | 0.75 | For precise matching of specialized terms and concepts, reducing interference from irrelevant information. |
MONGO_MAX_CONCURRENT_WRITES | Calibrate by actual measurement | Ensures database write concurrency performance, preventing excessive latency under high concurrency. |
Common Pitfalls
- Missing key specialized terms or incomplete context in query results: This is due to excessively short document segmentation or insufficient understanding of biomedical terminology by the embedding model.
- Significantly increased model response times, especially during multi-user operations: This is due to insufficient database concurrent connection limits or IOPS settings, unable to handle sudden high concurrent requests.
- Database link workflow errors, unable to extract data from specific document formats: This is due to improperly configured document parsers or lack of support for specific versions of PPT, complex tables, or embedded chart formats.
Validation Steps
- Select a test set covering various document types (PDF, Word, scanned images) and complex content to verify FastGPT's ability to accurately parse and extract all key fields.
- Simulate multi-user concurrent query scenarios, monitor FastGPT's response time and server resource utilization, ensuring response latency is below defined performance thresholds.
- Randomly sample original processed documents and FastGPT-generated structured data, comparing the consistency of key information (e.g., dosage, p-value, trial number) to verify data extraction accuracy.
- Check database logs for frequent connection timeouts, write failures, or slow index scans.
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.