Tool Calling and Plugins for Gene Therapy AAV Quality

Data Characteristics

Gene therapy AAV (adeno-associated virus) quality documents include batch reports, plasmid construction records, virus titer assays, purity analyses, genome integrity verification, host cell residue detection, biosafety evaluations, and stability study reports. These documents are typically PDFs, Word files, or structured database records. Data updates align with batch production cycles, generating or updating after each production batch. Document structures are highly standardized, adhering to GMP (Good Manufacturing Practice) and GLP (Good Laboratory Practice) requirements, with fixed sections, tables, and graphs. Fields like Batch Number, Titer (vg/mL), Purity (%), Genome Copies (GC/mL), and Endotoxin (EU/mL) have clear naming conventions and units.

Constraints from "Tool Calling and Plugins"

The highly structured and standardized nature of gene therapy AAV quality documents requires precise data extraction from specific locations in specific formats. Periodic batch report updates necessitate tool support for scheduled or event-driven data synchronization to ensure timely retrieval. Documents containing numerous graphs and tables demand tools capable of parsing complex layouts, such as OCR for text in images or extracting row/column data from tables. Strict unit requirements mean tools must maintain unit consistency during data processing and transfer to external systems, preventing errors from unit conversion. Biosafety threshold judgments depend on tools accurately identifying fields and performing numerical comparisons to trigger predefined business logic or alerts.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`model`	`gpt-4o` or `claude-3-opus`	High accuracy required for complex document structures and specialized terminology.
`maxContext`	`128k` tokens	A single quality report can be long, requiring full context understanding.
`tool_call_timeout`	`600 seconds`	External database queries or complex data processing may take time; avoid premature timeouts.
`Recall Count`	`Top 5`	Ensures retrieval results are concise, focusing on the most relevant information.
`Similarity Threshold`	`0.78`	Balances recall and precision, reducing interference from irrelevant documents.
`Database Connection Pool Size`	`10-20`	Handles concurrent query demands, ensuring responsive tool calls.

Common Pitfalls

tool_calls database connection error: Logs show 400 Messages with role errors. This usually indicates sensitive information in the database connection string is not correctly encoded or environment variables are not loaded.
No output after local tool deployment: Tool calls succeed but do not return expected data. This often results from network isolation between the FastGPT container and the database container, preventing FastGPT from accessing the database service port.
Variable passing to SQL statement error: SQL query execution fails with syntax errors. This is typically due to variables not being properly escaped in SQL templates or variable types not matching database field types.

Verification Steps

Select a typical AAV quality document. Use tool calling to query the titer value for a specific batch and verify the returned result matches the original document.
Simulate an external system data request by calling the FastGPT tool interface. Check if the returned structured data field names and units meet expectations.
Intentionally input a non-existent batch number. Observe if the tool call correctly returns "not found" or triggers predefined error handling logic.

The values provided are common starting points. Measure them against your 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.