Deployment and Upgrade for Stem Cell Therapy Quality

Data Characteristics

Quality documents in the stem cell therapy domain typically originate from clinical trial protocols, investigator brochures, manufacturing process instructions, quality standards, ethics review documents, and regulatory guidelines. These documents update slowly, usually in response to clinical trial phase progression, regulatory revisions, or manufacturing process optimizations, with cycles lasting months to years. Documents are primarily unstructured text, often containing numerous charts, flowcharts, and batch records. Common fields and units include cell count (units: cells/mL), cell viability (units: %), passage number, media lot number, and quality control indicators (e.g., endotoxin content, units: EU/mL). All data strictly adheres to GMP/GCP regulations.

Constraints from Data Characteristics on Deployment and Upgrade

The long update cycle for stem cell therapy quality documents means a large initial data ingestion during knowledge base deployment, but a low frequency of subsequent incremental updates. Complex charts and flowcharts within documents challenge file parser robustness, potentially requiring specialized preprocessing or more refined chunking strategies. Strict GMP/GCP regulations mandate accurate identification and indexing of critical fields like batch information and quality control data to ensure compliant and traceable retrieval. The unstructured nature and specialized terminology of the documents require vector models with strong semantic understanding to prevent loss of critical information due to improper tokenization. Deployment must also account for large document volumes, which demand significant storage and computational resources.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Stem cell quality documents often contain many images and charts, resulting in large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Complex PDF parsing can be time-consuming; this prevents upload failures due to parsing timeouts.
`Chunk size`	`800–1200 characters`	Ensures each chunk contains a complete semantic unit while balancing retrieval efficiency.
`Recall count`	`Top 10 entries`	Increases recall rate, covering related information that might be scattered across different sections.
`Similarity threshold`	`Calibrate by actual measurement`	Ensures result relevance; requires small-scale testing with specialized terminology.
`MONGO_VERSION`	`6.0.x`	Ensures compatibility with the latest plugin features, preventing errors caused by version mismatches.

Common Pitfalls

Knowledge base file upload fails, and logs show Request Entity Too Large. This occurs when the UPLOAD_FILE_MAX_SIZE parameter on the server or frontend is too small to handle large quality documents.
Key quality control indicators or batch information are missing from retrieval results or do not match the original text. This can happen if charts or specific data formats are not correctly extracted during document parsing, leading to incomplete vectorized information.
The system encounters a database connection error when creating a knowledge base or performing retrieval, with an error message like MongoServerError: The dollar ($) prefix is not allowed.... This typically indicates a MongoDB version incompatibility with FastGPT or its plugins.

Verification Steps

Upload a PDF of a stem cell therapy manufacturing process instruction containing complex charts and tables. Confirm successful upload and parsing.
Randomly select key quality control data or batch information from the document and perform a retrieval. Verify that the recalled results accurately include this information and link to the original source.
Simulate multiple users simultaneously retrieving information from the knowledge base. Observe system response speed and resource utilization to ensure stable performance under high concurrency.

Note: The values provided are common starting points. It is recommended to measure against specific samples to determine the optimal configuration for individual needs.

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.