Deployment and Upgrade for Structured Parsing of Psychiatric

Data Characteristics

Psychiatric R&D document data originates from clinical trial reports, drug mechanism of action studies, patient medical record analysis, genomics and proteomics data, and relevant academic literature. Data updates are infrequent, typically occurring quarterly or annually with clinical trial phase results or new drug development progress. Documents have complex structures, containing extensive unstructured text like symptom descriptions, diagnostic criteria, treatment plans, adverse event reports, and scale scores. They also include semi-structured data such as trial design parameters and statistical analysis results. Fields and units are highly specialized. Examples include DSM-5 classification codes, HAM-D scores, pharmacokinetic parameters (e.g., Cmax, Tmax, AUC), and their units (e.g., ng/mL, h).

Constraints on Deployment and Upgrade

The complex structure and specialized fields in psychiatric R&D documents demand high accuracy for structured parsing. Semantic understanding of unstructured text requires more powerful models, which can increase inference resource requirements. Low data update frequency allows for longer model training and fine-tuning cycles, but each update may involve a large volume of data. Internal network deployment restricts direct access to external resources. All dependencies and model resources must be pre-downloaded and configured. For example, large models like deepseek must be accessible within the internal network, and the FastGPT deployment server must be able to call them via API. Document-specific fields like scale scores and diagnostic codes require customized parsing rules and entity recognition models to prevent general models from misinterpreting specialized domains. PgVector plugin upgrades require attention to compatibility with existing PostgreSQL versions and smooth migration of vector data.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	R&D documents are often large, containing charts and attachments, requiring support for large file uploads.
`Chunk size` (Segment Length)	`800–1200 characters` (characters)	Psychiatric texts have strong contextual relevance; sufficient context must be preserved.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	The domain is knowledge-intensive. A higher threshold ensures precision in recall results and reduces irrelevant information.
`Recall count` (Recall Count)	`Top 10 entries` (top 10)	Complex queries may involve multiple knowledge points; increasing recall helps ensure comprehensive coverage.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Parsing large PDF or Word documents is time-consuming. Extending the timeout prevents parsing interruptions.
`maxContext`	Calibrate by actual measurement	Ensures the large model can handle the complex semantics and long contexts of psychiatric documents and adapts to the actual capabilities of the internal `deepseek` model.

Common Pitfalls

redis image pull failure during docker-compose up -d: This occurs when the internal network cannot access Docker Hub. Configure a private image repository or use a domestic mirror address like Alibaba Cloud.
The deepseek channel is not selectable in the application: The API address or key configuration for FastGPT's AI model is incorrect. This prevents FastGPT from correctly identifying and calling the API service provided by the internal deepseek instance.
HAM-D score or DSM-5 code fields are empty in structured parsing results: General parsing models lack sufficient recognition capability for specific professional terms and scale formats. Introduce customized named entity recognition models or regular expression parsing rules.

Verification Steps

Upload a typical R&D document containing DSM-5 classification codes and HAM-D scores. Check if the corresponding fields are accurately extracted in the parsing results.
Select the configured deepseek channel model in the FastGPT application. Ask complex questions about psychiatric drug mechanisms of action and observe the accuracy and professionalism of the response.
Use the docker logs command to view redis container logs. Confirm it started normally and has no connection errors.
Check the PgVector plugin version in the FastGPT backend. Run small-scale vector retrieval tests to confirm that vectorization and retrieval functions operate correctly.

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.