HTTP Interface and External Systems for Neurodegenerative

Data Characteristics

Quality documents related to neurodegenerative diseases come from diverse sources. Core document types include clinical trial protocols, research reports, ethics committee approvals, and informed consent forms. These documents typically have strict structural requirements. For example, clinical trial protocols strictly follow ICH GCP guidelines, containing fixed sections like study objectives, design, inclusion/exclusion criteria, assessment endpoints, and statistical analysis plans. Data update frequency is relatively low, usually updated in stages as projects progress, such as annual reports or protocol amendments. Documents contain extensive specialized terminology, biomarker names, gene sequence information, drug molecular formulas, dosage units (e.g., mg/kg, µM), and complex statistical symbols. Fields have strong interdependencies; for instance, a gene mutation field directly links to specific disease phenotypes or drug targets.

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The structured nature of neurodegenerative quality documents requires HTTP interfaces to effectively identify and map the internal logical hierarchy of documents during data upload. This prevents information loss from flat processing. Low update frequency means real-time requirements for interface design are not high, but data integrity and version control demand more attention. The dense presence of specialized terminology and symbols in documents increases the difficulty for tokenization and embedding models, requiring more robust text preprocessing capabilities. Specific fields and units, such as disease names, gene IDs, and drug dosages, require HTTP request bodies to accurately carry this information and maintain its semantics during subsequent processing. This dictates that external systems must perform fine-grained preprocessing and structured encapsulation of source data when calling FastGPT interfaces, ensuring critical information is not diluted or misunderstood.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`chunkSize`	`800–1200` characters	Neurodegenerative document paragraphs are often long and contain complex medical concepts; this range ensures contextual completeness.
`overlapSize`	`100–200` characters	Ensures sufficient overlap between segments to capture cross-paragraph related information, especially when describing pathological mechanisms.
`similarityThreshold`	`0.75–0.85`	For precise matching of specialized terms and concepts, increasing the professional relevance of recall.
`maxContext`	`6000–8000` tokens	Queries for neurodegenerative documents often involve multiple entities and complex logical relationships, requiring a longer context window.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical trial reports or large research documents may contain numerous charts and text, requiring support for large file uploads.
`PARSE_FILE_TIMEOUT_SECONDS`	`600` seconds	Processing large PDF or DOCX documents can take a long time, avoiding failures due to timeouts.

Common Pitfalls

After an external system calls the upload interface, the knowledge base fails to correctly recognize the document's structure and sections, leading to a lack of contextual relevance during queries. This happens because the document's metadata or structural information was not passed to FastGPT via the metadata field during upload.
Uploading a large research report results in a 504 Gateway Timeout error. This occurs because document parsing time exceeds the server's or proxy's default timeout setting, indicating that the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low.
Queries for specific gene or drug interactions yield inaccurate or missing critical information. This can be due to overly large document segmentation granularity, causing relevant information to be scattered across different chunks during querying, or a similarityThreshold set too low, failing to effectively filter out irrelevant segments.

Verification Steps

Upload a multi-chapter neurodegenerative disease research report. Check if the number and content of chunks for that document in the knowledge base meet expectations, particularly if chapter titles and key paragraphs are correctly segmented.
Using FastGPT's debugging tools, perform complex queries containing specialized terminology. Observe the recalled chunk content to ensure it accurately relates to the corresponding medical concepts and data in the document.
Simulate an external system uploading a document close to UPLOAD_FILE_MAX_SIZE via the HTTP interface. Monitor the interface response time to confirm no timeout errors occur, and check logs to ensure the parsing process completes smoothly.

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.