FastGPT Deployment and Upgrade for SMO Quality Documents

Data Characteristics for SMO Quality Documents

SMO (Site Management Organization) quality documents include Standard Operating Procedures (SOPs), work instructions, training records, instrument calibration records, internal audit reports, deviation and CAPA (Corrective and Preventive Action) documents, and various study protocol-related documents. These documents originate from diverse sources: some are internally developed by the SMO, others are acquired from sponsors or CROs (Contract Research Organizations) and localized. Update frequency varies by document type. SOPs are typically revised annually or in response to regulatory or process changes. Deviation records and training records are generated and updated in real-time.

Document structure is primarily hierarchical, usually including version numbers, effective dates, revision histories, approval records, main text, and attachments. Date formats, version number naming conventions, and medical measurement units (e.g., mg, ml, μg/mL) require strict standardization.

Deployment and Upgrade Constraints Imposed by These Characteristics

The hierarchical structure and strict version control requirements of SMO quality documents necessitate refined document segmentation and metadata extraction during FastGPT knowledge base construction. Frequent revisions and real-time document generation demand a robust incremental update mechanism and efficient index rebuilding to ensure timely and accurate retrieval results.

The prevalence of medical measurement units and specialized terminology requires the model to possess strong domain-specific semantic understanding during embedding and retrieval. This prevents retrieval failures caused by tokenization or semantic drift. The complexity of document sources requires deployment solutions to flexibly parse various file formats (PDF, Word, Excel, etc.) and provide a stable file upload mechanism, especially for PDF files containing numerous images or complex tables.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Individual SMO documents can be large, containing images and complex layouts.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Ensures sufficient time for large PDF files to complete parsing.
`Chunk size`	`500 characters`	Balances context completeness and retrieval granularity; SOP logical paragraphs typically do not exceed this length.
`Overlap Length`	`50 characters`	Ensures semantic continuity between segments, preventing critical information from being split.
`Recall count`	`Top 8 entries`	Improves retrieval accuracy by covering more highly relevant document segments.
`Similarity threshold`	`0.75`	Ensures only highly relevant results are returned, filtering noise and preventing misleading information.

Common Pitfalls

Uploading large PDF files results in an "offset out of range" error. This occurs when the file parser fails to correctly process the PDF's internal structure or the file is corrupted.
Retrieval results do not reflect the latest revisions after a knowledge base update. This happens when the incremental indexing mechanism is not correctly triggered or index rebuilding fails.
The Text Processing module is missing after deploying local FastGPT. This is due to incomplete dependency installation or the use of non-standard deployment scripts.

Verification Steps

Upload a PDF-formatted SOP containing complex tables and medical measurement units. Verify successful parsing and the generation of retrievable segments.
Revise an existing SOP in the knowledge base. Upload the new version, then use queries to confirm that retrieval results reflect the latest content.
Use queries containing medical specialized terminology. Verify accurate recall of relevant document segments and compare the recall effect against the set Similarity threshold.

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.