FastGPT Deployment and Upgrade for Hematologic Oncology

Data Characteristics in This Domain

Hematologic oncology data comes from various sources: clinical trial reports, medical literature, gene sequencing data, drug inserts, and medical guidelines. Data updates frequently due to new drug approvals, clinical research advancements, and treatment protocol adjustments. Document structures typically include disease definitions, epidemiology, pathophysiology, diagnostic criteria, classification, treatment regimens (chemotherapy, targeted therapy, immunotherapy), and prognosis. Fields often involve gene mutation sites, chromosomal abnormalities, drug targets, dosage units (mg/kg, mg/m²), treatment cycles (days, weeks, months), and adverse event grades. Data types are diverse, including text, structured data, and semi-structured data.

Constraints Imposed by These Characteristics on Deployment and Upgrade

The high update frequency of hematologic oncology data requires the deployment environment to support rapid iteration and incremental updates, minimizing downtime for maintenance. The multi-source, heterogeneous nature of the data, especially the large volume of unstructured text and semi-structured reports, demands advanced data preprocessing and vectorization models that support various document formats. Structured information like gene sequencing data and drug dosages requires precise knowledge base field mapping and retrieval accuracy to ensure query result correctness. Complex treatment regimens and multi-stage disease progression make context management and reasoning chain design critical; the system must handle long dialogue histories and multi-turn Q&A. The volume and update frequency of data also exert continuous pressure on the computing resources and storage capacity of the deployment environment.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Clinical trial reports and drug inserts often contain many images and charts, leading to large file sizes.
`maxContext`	`8000 tokens`	Hematologic oncology treatment plans are complex, involving multi-turn conversations and lengthy literature citations, requiring a longer context window.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large medical literature and reports takes a long time; increasing the timeout prevents interruptions.
`Chunk size`	`800 characters`	Ensures the completeness of medical concepts and treatment steps, preventing critical information from being truncated.
`Recall count`	`Top 10 entries`	Improves the recall rate of relevant medical knowledge, covering more potential treatment paths and diagnostic bases.
`Similarity threshold`	Calibrate by measurement	Adjust based on actual query results for retrieval accuracy of medical terms and synonyms.

Three Common Mistakes

After Docker container startup, the m3e vector model service reports connection failure, with curl commands returning Connection refused or Failed to connect. This is often due to incorrect internal network configuration or port mapping within the container, preventing external requests from reaching the service.
Querying hematologic oncology treatment plans after deployment frequently returns outdated information. This occurs when the knowledge base data is not synchronized with the latest medical guidelines or clinical trial data, causing the model to answer based on old knowledge.
When accessing the FastGPT web interface, some functions display abnormally or fail to load, with browser console errors like SyntaxError: Unexpected token. This is typically due to the frontend resource compilation target version being too high, leading to incompatibility with older browser versions used by the user.

How to Verify Correct Configuration

Execute queries involving new drug information or the latest treatment plans. Verify that the system's results include the most current data and confirm their timeliness against medical literature.
Upload a hematologic oncology clinical report containing multiple formats (e.g., PDF, DOCX, TXT). Check that all content is correctly parsed and vectorized, with no parsing failures or content loss errors.
Simulate multi-turn Q&A scenarios, such as from disease diagnosis and treatment plan recommendations to adverse event management. Observe whether the system maintains contextual coherence and provides relevant information, and verify if the dialogue history length meets expectations.

Note: The values provided are common starting points and should be measured 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.