Data Characteristics for This Category
Registration and declaration documents for small molecule chemical drugs primarily originate from pharmaceutical research, pharmacology and toxicology studies, and clinical trial data. This data typically exists in structured forms (e.g., analytical reports, batch records, stability data) and unstructured forms (e.g., research protocols, raw records, expert reports, literature reviews). Update frequency is highly correlated with R&D progress. New batch production, periodic stability study reports, and interim clinical trial summaries trigger document updates. Document structure is rigorous, adhering to guidelines like ICH M4E, and includes modular structures such as the CTD (Common Technical Document) format. Fields and units are highly specialized. For example, the pharmaceutical section involves molecular weight, purity percentage, melting point in Celsius, and spectral absorbance. Pharmacology and toxicology involve dosage in milligrams per kilogram and plasma concentration in nanograms per milliliter. Clinical data involves patient IDs, adverse event codes, and statistical P-values.
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The specialized nature and strict structure of small molecule chemical drug registration and declaration documents place high demands on deployment. Large amounts of structured data require precise parsing to ensure semantic accuracy. This requires FastGPT's parser to recognize specific fields and units, necessitating custom parsing rules when needed. Key information extraction from unstructured documents, such as adverse event descriptions or research conclusions, relies on more powerful semantic understanding capabilities. Frequent data updates mean the knowledge base must support efficient incremental synchronization and version management to avoid declaration risks due to outdated data. Strict compliance requirements, such as data isolation, access control, and audit logs, must be fully considered during initial system deployment. Private deployment is standard practice, ensuring data remains within the local network. This directly impacts integration methods with external services; for example, connecting to enterprise communication tools may require additional security measures.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Registration and declaration documents often contain large scanned images and multimedia attachments. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing complex PDF documents or reports with many charts requires longer parsing times. |
maxContext | 800–1200 characters | Ensures sufficient context is captured during RAG retrieval, especially for lengthy research reports. |
Chunk size | 400 characters | Balances semantic completeness with model processing efficiency, suitable for documents with high density of specialized terminology. |
Recall count | Top 5 entries | Improves relevance and reduces interference from irrelevant information; declaration document queries require high accuracy. |
Similarity threshold | 0.75 | Requires higher matching accuracy for highly specialized and rigorous declaration documents. |
Three Common Mistakes
- Knowledge base query results include irrelevant historical version information. This occurs when the knowledge base is not configured with effective timestamps or version fields for filtering.
- After private deployment of FastGPT, it cannot connect to internal enterprise instant messaging tools. Connection requests time out or are rejected, often due to firewall policies restricting outbound or inbound ports, or a lack of necessary proxy configurations.
- Data migration fails during a FastGPT upgrade. Logs show database connection errors or table structure mismatches. This often happens when pre-scripts from the upgrade guide are not executed, leading to database schema incompatibility with the new version's code.
How to Confirm Correct Configuration
- Upload a pharmaceutical research report in PDF format containing complex tables and charts. Confirm that its parsed content is complete and accurate, and that key fields such as molecular structure descriptions and purity percentages are correctly identified.
- Retrieve specific clinical trial data from the knowledge base, such as the incidence rate of adverse events for a particular drug. Verify the accuracy and completeness of the returned results, and check if the number of retrieved items meets the expected threshold.
- Simulate user queries through the FastGPT client, for example, by asking for stability data for a specific batch of medicine. Observe response speed and content relevance, and check logs for abnormal requests or parsing errors.
Note: 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.