What the data for this category looks like
Chemical pharmaceutical research report data sources include CDE review databases, publicly disclosed financial reports of pharmaceutical companies, securities firm pharmaceutical industry research reports, clinical trial disclosure platforms, and professional pharmaceutical databases. Update cadence includes scheduled bulk updates and ad-hoc triggers, such as temporary updates when new drugs gain approval or quarterly financial reports are released.
Document structure mixes structured and unstructured content. Structured fields include generic drug names, approval numbers, clinical trial phases, and target information. Unstructured content includes market analysis and competitive landscape. Field units include approval numbers starting with the Guoyao Zhunzi H prefix, revenue units of ten-thousand yuan / hundred-million yuan, dosage units of mg/kg, and case count units.
What constraints do these characteristics impose on deployment and upgrade
Mixed structured and unstructured document structure requires configuring multimodal parsing and structured extraction plugins during deployment. This avoids professional field matching failures caused by full-text retrieval alone.
Multi-source and multi-cadence update requirements demand dual configuration support for incremental hot updates and manual trigger updates during upgrades. This enables synchronization of latest data without fully rebuilding indexes.
Fixed formats and units for professional fields require presetting field mapping rules during deployment. This avoids unit mismatches or missing fields during retrieval.
Volume differences across research report types require reserving sufficient file upload and parsing resources. This accommodates processing demands for large financial reports and long documents.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Chemical pharmaceutical research reports often contain long tables and clinical trial data, with longer parsing times than general documents |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Single pharmaceutical company financial reports or large research report PDFs exceed the size range of general documents, requiring adaptation to the storage requirements of professional research reports |
maxContext | 8000–12000 characters | Professional analysis paragraphs in chemical pharmaceutical research reports are relatively long, requiring sufficient context to support accurate question answering |
Recall count | Top 10–15 results | Relevant results for professional research reports need to cover multiple dimensions such as targets and approval information, avoiding missing key content due to too few recalls |
Similarity threshold | 0.75–0.85 | Semantic similarity requirements for chemical pharmaceutical technical terms are higher than general scenarios, requiring filtering of low-relevance non-professional content |
Reranked return count | Top 3–5 results | Filter redundant similar professional content and retain the most valuable research report fragments |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Each scenario requires individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Calling a locally privately deployed large model returns
500 Internal Server Error. Logs indicate the model output format does not meet preset requirements. Cause: The semantic complexity of chemical pharmaceutical technical terms is high. The local model is not adapted to pharmaceutical domain corpus, and cannot correctly handle structured field input and output. - Phenomenon: A
413 Request Entity Too Largeerror occurs when uploading research reports in bulk. Cause: TheUPLOAD_FILE_MAX_SIZEconfiguration has not been adjusted to a value suitable for large research reports, exceeding the default file upload limit. - Phenomenon: After the knowledge base index is completed, the approval number field for some drugs is empty. Cause: The pharmaceutical professional field mapping rule of the structured extraction plugin has not been enabled, so the format of approval numbers starting with the Guoyao Zhunzi H prefix cannot be recognized, resulting in failed field extraction.
How to confirm the configuration is correct
- Upload a chemical pharmaceutical research report containing clinical trial data and structured tables. Check whether the parsed fields completely extract professional content such as drug names and approval numbers, to confirm that the parsing configuration adapts to document characteristics.
- Trigger an incremental update. Wait for the update process to complete, and retrieve recently disclosed new drug review information, to confirm that the latest data has been synchronized to the knowledge base.
- Call the configured large model. Input a professional question about chemical pharmaceutical targets, and check whether the returned result is associated with the corresponding research report content, to confirm that the connection between the model and the knowledge base is normal.
- Adjust the recall count and similarity threshold of retrieval. Compare retrieval results under different configurations, to confirm that the parameter settings meet the accuracy and recall requirements of professional retrieval.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.