Data Characteristics
Small molecule pharmaceutical quality documents originate from R&D, pilot production, manufacturing, and quality control. These include analytical method validation reports, stability study reports, process validation reports, batch production records, inspection reports, deviation records, and change control records. Documents update frequently, especially during R&D and pilot phases, leading to rapid data iteration. Document structures are highly standardized, adhering to GMP/GLP regulations. They contain significant structured or semi-structured data, such as test items, methods, results, units, batch numbers, production dates, and expiration dates. Fields often involve specialized information like IUPAC names, CAS numbers, molecular formulas, structural formulas, chromatograms, and mass spectra. Common units include concentration (mg/mL, ppm), purity (%), time (hours, days), and temperature (℃).
Constraints on Deployment and Upgrade
The standardized and specialized nature of small molecule pharmaceutical quality documents requires FastGPT to optimize text segmentation and vectorization strategies during deployment. The abundance of specialized terminology and structured data means default general segmentation methods might truncate critical information or lose context, impacting recall accuracy. High update frequency demands robust data synchronization and incremental indexing capabilities to ensure knowledge base timeliness. Documents containing images (e.g., chromatograms, mass spectra) and tabular data require advanced file parsing to effectively extract and interpret key information. Deployment environments must also meet data security and compliance requirements, such as on-premise or private cloud deployment, to prevent sensitive R&D data leaks. During upgrades, pay close attention to model version compatibility. Ensure new models maintain understanding of specific professional terms and support smooth migration of historical data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Small molecule pharmaceutical quality documents can be large due to numerous charts and scanned images. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing complex PDFs and scanned documents can be time-consuming; this prevents parsing timeouts. |
Chunk size (Segment Length) | 800–1200 characters | Retains sufficient context to prevent truncation of specialized terms and structured data, while managing segment information density. |
Recall count (Recall Count) | Top 5 entries (Top 5) | Quality document queries typically require precise matches; increasing recall count improves coverage. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Ensures recalled results are highly relevant to the query, filtering out low-quality matches. |
Rerank result count (Reranked Return Count) | Top 3 entries (Top 3) | The top few results after reranking are typically the highest quality, reducing presentation of irrelevant information. |
Common Mistakes
- Knowledge base query results contain excessive irrelevant information. This happens when stop words or synonyms for small molecule pharmaceutical terminology are not configured, leading to generalized recall.
- After on-premise deployment, workflow nodes fail to output results, indicating no output. This might be due to incorrect
OPENAI_API_KEYorCUSTOM_LLM_URLconfiguration, causing LLM call failures. - Uploading large PDF documents results in a file parsing failure. This might be because the
PARSE_FILE_TIMEOUT_SECONDSparameter is set too low, not allowing enough time for the parser to handle complex document structures.
Verification
- Upload a small molecule pharmaceutical batch production record containing complex tables and specialized terms. Check if parsed segments are complete and if critical data (e.g., batch numbers, test results) are correctly extracted.
- Query with a question about a specific test method and result. Verify if FastGPT's returned document snippets accurately pinpoint relevant paragraphs and if the answer is accurate.
- Simulate high-concurrency upload and query scenarios. Monitor system logs to observe if parameters like
PARSE_FILE_TIMEOUT_SECONDStrigger timeouts. Check if themaxContextparameter meets query context length requirements. - Regularly use the latest FastGPT deployment package for small-scale testing. Verify the new version's compatibility and performance when handling small molecule pharmaceutical document types.
Note: The values provided are common starting points. Measure them 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.