Deployment and Upgrade for Hematologic Oncology Protocols

Data Characteristics

Data for hematologic oncology protocols and SOPs typically originate from clinical pathways, treatment guidelines, medication specifications, and ethical review documents published by healthcare institutions and industry associations. This data updates frequently, especially in emerging fields like targeted therapy and immunotherapy, with new research and guideline revisions occurring annually or even quarterly. Documents are primarily in PDF, Word, and RTF formats, containing numerous tables, charts, flowcharts, complex nested headings, and cross-references. Fields include disease diagnostic criteria, staging, treatment plans, drug dosages, adverse event management, and follow-up requirements. Drug dosages often include units like mg/kg or mg/m², and treatment cycles use units such as days, weeks, or months.

Constraints on Deployment and Upgrade

The multi-source nature, high update frequency, and complex document structure of hematologic oncology protocol data impose several constraints on deployment and upgrade processes. First, multi-source data requires flexible data ingestion capabilities to integrate documents from various systems and formats. High update frequency necessitates automated or semi-automated content synchronization mechanisms for the knowledge base to ensure information timeliness and prevent answers based on outdated information. Complex document structures, particularly tables and charts, demand advanced file parsing capabilities. Traditional text extraction may lose critical information, affecting subsequent embedding quality. Furthermore, special units and dosage information in fields require accurate processing by tokenization and entity recognition modules to avoid ambiguity or errors in recall or answer generation. These characteristics collectively dictate the need for fine-tuned parsing parameter configuration during deployment and focus on data synchronization and parsing capability iteration during upgrades.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Hematologic oncology protocol documents often contain many images and charts, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Complex PDF and Word document parsing can be time-consuming; this prevents parsing failures due to timeouts.
`Chunk size`	`800–1200 characters`	Protocol clauses have strong contextual relevance; this ensures sufficient information in a single segment.
`Recall count`	`Top 8 entries`	Ensures coverage of multiple potentially relevant clauses, improving answer comprehensiveness.
`Similarity threshold`	Based on measurement, typically `0.75–0.85`	Hematologic oncology terminology is highly specialized, requiring a higher threshold for recall accuracy.
`Rerank result count`	`Top 3 entries`	Selects the most relevant few items for the model after a high number of initial recall results.

Common Pitfalls

In a Docker Compose environment, the ONEAPI_URL parameter is unconfigured or misconfigured, leading to API call failures. Logs show connection refused or invalid API key. This occurs because the OneAPI service address must be explicitly specified in newer versions and is no longer defaulted.
MinIO is deployed in an internal network but not exposed to FastGPT via reverse proxy or port mapping, causing file upload and access failures. The interface shows network error or object storage unreachable. This happens because FastGPT requires access to MinIO via a public or accessible address.
After file parsing, tabular data in protocols is ignored or parsed as incomplete text, preventing accurate answers to questions about dosages or cycles. This is because the default parser has limited support for complex table structures and requires specific optimization or enhanced parsing plugins.

Verification Steps

Upload a PDF file of a hematologic oncology clinical guideline containing complex tables and charts. Check the file parsing progress and segment details to ensure table content is extracted correctly.
Query the knowledge base about a specific drug dosage or treatment cycle. Verify that the recall results and generated answers accurately include numbers and units from the original text.
Simulate a protocol update scenario by uploading a new version of the guideline. Check if the knowledge base content refreshes promptly and if questions related to the old content are correctly updated.

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.