Deployment and Upgrade for Attenuated Live Vaccine R&D

Data Characteristics

Attenuated live vaccine R&D documents originate from multiple sources. These include clinical trial reports, strain screening records, production process specifications, quality control standards, animal experiment data, and regulatory submission materials. Document update frequencies vary. For example, clinical trial data may update with phased reports, while production process specifications revise during process optimization. Documents feature complex structures, containing both highly standardized tabular data (e.g., batch test results) and extensive unstructured text (e.g., experimental observation records, expert review opinions). Common fields include unique identifiers such as strain number, passage number, titer, immunogenicity indicators, adjuvant type, and cell line passage number. Units encompass International Units (IU), Tissue Culture Infectious Dose (TCID50), Neutralizing Antibody Titer (NAb Titer), and various concentration units (e.g., μg/mL). This data is typically distributed across multiple file formats, including PDF, DOCX, and XLSX.

Constraints on Deployment and Upgrade

The complex data characteristics of attenuated live vaccine R&D documents impose specific constraints on deployment and upgrade. Diverse data sources require FastGPT's file parsing module to possess strong format compatibility and robustness. This ensures handling of various information types, from standardized tables to free text. Inconsistent document update frequencies mean the system must support incremental updates and version management. This avoids redundant indexing and ensures knowledge base timeliness. For instance, when production process specifications revise, only the changed document sections require updates. The abundance of specialized terminology and biomedical-specific fields necessitates incorporating domain-specific dictionaries and ontological knowledge during model training and fine-tuning. This improves the accuracy of structured parsing. Precise extraction of critical fields, such as strain number and titer, directly impacts the effectiveness of subsequent R&D decisions. Therefore, deployment must focus on the recall and precision of the parsing model for these specific information items.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical trial reports or regulatory submission materials often contain numerous images and tables, resulting in large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large files and processing complex structures is time-consuming. Sufficient time is needed to prevent timeout interruptions.
`Chunk size` (Chunk Length)	`800 characters`	Ensures complete biological descriptions or experimental procedures are included, while maintaining contextual coherence.
`Overlap Length`	`100 characters`	Maintains semantic connections between paragraphs, especially when describing continuous experimental procedures.
`Recall count` (Recall Count)	`Top 8 entries` (Top 8)	R&D questions typically require synthesizing multiple pieces of information. Increasing recall covers more relevant context.
`Similarity threshold` (Similarity Threshold)	`0.75`	Balances recall and precision, preventing interference from irrelevant passages while ensuring specialized terminology matches.

Common Mistakes

Slow or unresponsive chat: Users experience long delays or network errors after asking a question. This occurs because the deployment environment's computing resources (CPU, memory) are insufficient to support FastGPT's internal embedding model and LLM inference load, especially when processing long texts or high-concurrency requests.
Failure to extract key information: Critical information, such as strain numbers or titers, appears empty or incorrect in structured query results. This happens when the model is not fine-tuned or dictionary-enhanced for attenuated live vaccine-specific terminology and entities, preventing general models from accurate recognition.
Knowledge base updates not synchronized: Newly uploaded document content is not retrievable, or old versions of information are retrieved. This indicates that the file indexing mechanism lacks an incremental update strategy, or the cache is not refreshed promptly, causing a discrepancy between the knowledge base index and source files.

Verification Steps

Upload an attenuated live vaccine R&D report containing complex tables and specialized terminology. Check if the File Parsing status shows "successful" (Success). Review the parsed chunk preview to confirm correct identification and chunking of key fields.
For the uploaded document, test with a query containing a strain number and specific experimental indicators, for example, "Query Strain MV-123 的 TCID50 值" (Query TCID50 value for strain MV-123). Verify that the returned results include relevant numerical values and context.
Simulate a document update process. Modify a key parameter in an already uploaded document, then re-upload it. Observe the knowledge base's update timestamp. Perform another query to confirm that the query results reflect the latest modifications.

The values given 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.