Data Characteristics
Small molecule drug R&D documents originate from diverse sources, including experimental records, analysis reports, patent literature, and preclinical research data. Update frequencies vary, from dozens of experimental data points daily to several periodic reports monthly. Document formats are predominantly PDF, DOCX, and XLSX, with some data embedded in images or scanned documents. Structurally, these documents often contain extensive specialized terminology, chemical structures, reaction equations, diagrams, and data tables. Fields and units are highly specific, such as IUPAC names for compounds, CAS registry numbers, molar mass (g/mol), purity (%), yield (%), pharmacokinetic parameters (e.g., Cmax, Tmax, in ng/mL, h), and detection/quantitation limits for various analytical instruments.
Constraints from these Characteristics on Deployment and Upgrade
The diversity and specialized nature of small molecule drug R&D documents impose specific requirements on FastGPT's deployment and upgrade processes. First, non-textual information like chemical structures and diagrams in documents necessitates image recognition and OCR capabilities for comprehensive structural analysis. Second, the abundance of specialized terminology and units requires customized dictionaries and entity recognition models to prevent parsing errors or information loss. Varying document update frequencies mean the system needs to support flexible incremental update mechanisms to efficiently process new data. Furthermore, high data sensitivity demands strict requirements for deployment environment security, data isolation, and access control. During upgrades, compatibility with older data formats, model stability, and parsing accuracy are core considerations. This is especially true when underlying large language models or parsing algorithms are updated, requiring thorough regression testing.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates comprehensive report files with numerous diagrams and high-resolution scans. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Ensures sufficient parsing time for complex PDF files (with multi-layered nested objects, vector graphics). |
Chunk size (Segment Length) | 800–1200 characters | Balances contextual coherence with large model processing efficiency, suitable for segments dense with specialized terminology. |
Recall count (Recall Count) | Top 15 items | Increases the probability of recalling relevant segments, addressing queries with strong conceptual associations in small molecule drug R&D. |
Similarity threshold (Similarity Threshold) | 0.78 | Ensures recalled results are highly relevant to the query intent, filtering out generalized content with weak specificity. |
OPENAI_API_KEY | sk-xxxxxxxxxxxxxxxxxxxxxxxx | Ensures FastGPT can access external large model services, such as Alibaba Cloud Model Service. |
Common Pitfalls
- Model call failures, returning a 400 error code, may indicate an incorrect or expired
OPENAI_API_KEYconfiguration, leading to authentication failure with external model services. - After uploading files via API, the agent backend service may become slow or unresponsive. Symptoms might include blank workflow and knowledge base interfaces. This typically results from a backlog in the file parsing task queue or
PARSE_FILE_TIMEOUT_SECONDSbeing set too short, causing numerous parsing tasks to fail and retry. - After some time following Docker Compose deployment, knowledge base content may be lost or appear empty. This can occur if data volumes are not correctly mounted or persistence configuration is incorrect, preventing data from being retained after container restarts.
Verification Steps
- Upload a PDF document containing chemical structures and experimental data tables. Verify that the structural analysis accurately identifies key fields and their values.
- Perform an incremental update operation by uploading a new experimental report. Confirm the system can identify and process new information without affecting existing data.
- Use a query containing specific small molecule drug specialized terminology. Verify the accuracy and relevance of the recalled results and check if the recall count meets expectations.
The values provided are common starting points and should be measured against the reader's 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.