Deployment and Upgrade for Bispecific Antibody Registration

Data Characteristics for This Category

Bispecific antibody registration dossiers involve extensive structured and unstructured data. Data sources include clinical trial reports, non-clinical study reports, manufacturing process and quality control documents, pharmaceutical research data, and regulatory guidelines. Update frequency typically aligns with drug development phases; preclinical phases have lower update rates, while clinical trials see more frequent updates, especially for safety and efficacy data. Document structures are complex. For example, clinical study reports often contain sections like introduction, methods, results, and discussion, along with numerous tables, figures, and appendices. Common fields include CAS number, target name, affinity constant (KD), half-life (t1/2), administration route, and adverse event code (MedDRA). Units involve nM, μg/mL, hours, days, and often require precision to multiple decimal places.

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

The data characteristics of bispecific antibody dossiers impose specific requirements on FastGPT's deployment and upgrade processes. First, diverse and frequently updated data sources necessitate efficient data ingestion and synchronization capabilities to maintain knowledge base timeliness. Second, complex document structures, along with numerous specialized terms and biological units, demand sophisticated text chunking strategies and embedding models. This prevents critical information fragmentation or semantic loss. The mix of structured and unstructured data means that vector retrieval alone might be insufficient; keyword search or metadata filtering may also be necessary. Additionally, data often contains sensitive information, requiring careful attention to data security and access control during deployment. During upgrades, model iterations may necessitate re-embedding large datasets, requiring the deployment environment to have sufficient computing resources and stable storage performance.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Individual files in dossiers (e.g., full clinical study reports) can be large; this ensures successful uploads.
`Chunk size` (Chunk Length)	`1000–1500 characters` (characters)	Paragraphs describing bispecific antibody pharmacology, toxicology, and pharmacokinetics are long. This maintains context integrity and prevents critical information from being cut off.
`Recall count` (Retrieval Count)	`Top 10 entries` (top 10)	Complex queries may involve multiple related concepts. Increasing the retrieval count improves coverage of relevant snippets.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	The domain uses many specialized terms, requiring high semantic similarity to prevent interference from irrelevant information.
`maxContext`	`6000–8000 tokens`	Drug dossier information is highly context-dependent, requiring a sufficiently long context window to understand complex biological mechanisms and clinical data.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Processing large PDF or Word documents can take a long time. This prevents parsing timeouts that lead to file processing failures.

Three Common Mistakes

Knowledge base index creation fails, with logs showing Document parsing error: Timeout. This occurs when the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, causing large clinical reports or pharmaceutical files to exceed the parsing time limit.
Key drug dosage or mechanism of action information is missing from retrieval results, even though it exists in the original document. This can happen if Chunk size (Chunk Length) is set too small, causing paragraphs describing complete concepts to be split and context to be lost.
After a system upgrade, some users cannot log in or access specific knowledge bases. This often results from incorrect database or volume mounting, or improper permission configuration in a docker-compose deployment environment, preventing the new container version from recognizing user data or knowledge base indexes.

How to Confirm Proper Configuration

Upload a typical bispecific antibody clinical trial report (e.g., a 50 MB PDF file). Check if the file uploads completely and parses successfully into the knowledge base.
Query a document containing complex pharmacological data and clinical endpoint descriptions from multiple angles. Observe the completeness and relevance of retrieved snippets to ensure Recall count (Retrieval Count) and Similarity threshold (Similarity Threshold) are set appropriately.
Simulate concurrent multi-user access. Test the knowledge base's response speed and stability, especially when performing complex retrieval tasks, to verify the impact of parameters like maxContext on system performance.
Within the FastGPT container environment, confirm the versions of dependencies like mongoose using npm list mongoose or by checking the package.json file. This ensures compatibility with recommended official versions.

The values provided are common starting points. Measure against your own samples to determine optimal settings.

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.