Deployment and Upgrade for Autoimmune R&D Document

Data Characteristics

Autoimmune disease R&D documents draw from diverse sources. These include clinical trial reports, research papers, genomics data, proteomics data, metabolomics data, and disease model study records. Documents update frequently, especially during ongoing clinical trials or after new research is published. Document structures vary, ranging from unstructured text descriptions to semi-structured tabular data and structured database exports. Fields and units are highly specific. Examples include disease activity scores (e.g., DAS28, SLEDAI), biomarker concentrations (e.g., TNF-α, IL-6, typically in pg/mL or ng/mL), gene mutation sites (e.g., SNP numbers), drug dosages (e.g., mg/kg), and patient follow-up times (e.g., weeks, months).

Constraints on Deployment and Upgrade

The data characteristics of autoimmune R&D documents impose specific deployment and upgrade constraints. High update frequency requires efficient data synchronization and incremental parsing capabilities. This avoids resource waste from full re-processing. Diverse document structures mean the parsing module must support multiple formats, including PDF, DOCX, TXT, and JSON. It must also adapt flexibly to mixed table and text layouts. Specific fields and units require the structured parsing model to accurately identify and extract key information. It must also standardize units to ensure data consistency and usability. For example, for concentration units like pg/mL and ng/mL, the system needs to convert them to standard units or provide conversion functionality. Additionally, sensitive research data requires the deployment environment to meet strict data security and compliance requirements, such as intranet deployment, data encryption, and access control.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Autoimmune R&D documents often contain many images and charts, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Large documents take longer to parse. A longer timeout is needed for complex structured parsing.
`Chunk Length`	`800–1200 characters`	Ensures each chunk contains sufficient context. Avoids information overload from overly long chunks, aiding model understanding of disease mechanisms or trial results.
`Similarity Threshold`	`0.78`	Concepts in the autoimmune field are highly related. A higher threshold ensures the relevance of retrieval results.
`maxContext`	`4096`	Complex disease mechanisms and trial designs require a longer context window to understand relationships.
`Rerank Return Count`	`Top 8`	Ensures that reranking provides enough highly relevant key evidence or data points.

Common Pitfalls

Symptom: After uploading large documents, the system becomes unresponsive for an extended period or returns a 504 Gateway Timeout error. Reason: The PARSE_FILE_TIMEOUT_SECONDS configuration is too low. It does not account for the complex parsing time required for autoimmune R&D documents.
Symptom: After intranet deployment, the channel model cannot connect to the company's internal Ollama or other local model services. Reason: Network configuration in the docker-compose.yml file or CHANNEL_MODEL_URL does not correctly point to the internal model service address, or firewall restrictions exist.
Symptom: In parsed document content, biomarker concentration units are inconsistent (e.g., pg/mL and ng/mL are not unified). Reason: The structured parsing model was not specifically trained or configured with rules for unit expressions unique to the autoimmune field, leading to unit standardization failure.

Verification Steps

Upload and parse an autoimmune clinical trial report containing various structures (text, tables, charts). Verify the accuracy of key field extraction (e.g., DAS28 score, TNF-α concentration) and unit standardization in the parsing results.
Use the FastGPT chat interface to query the parsed document. Verify the model's ability to correctly understand document content and integrate information from different chunks to answer complex questions, such as inquiring about specific drug efficacy data at different disease stages.
Simulate high-concurrency file uploads and parsing. Observe system resource usage (CPU, memory) and response times. Ensure the system remains stable under high load and check logs for 504 or 502 errors.
Verify that FastGPT successfully connects to the configured internal language model service in an intranet deployment environment and can call it for text generation or understanding tasks. This can be confirmed by checking docker logs output or the FastGPT backend Model Management interface for model status.

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.