Tool Calling and Plugins for Autoimmune Disease Regulations

Data Characteristics in this Domain

Autoimmune disease data comes from diverse sources. These include clinical trial reports, drug inserts, treatment guidelines, and internal hospital regulations and Standard Operating Procedures (SOPs). Document update frequencies vary. Drug inserts and treatment guidelines may change with new drug approvals or clinical evidence. Internal hospital regulations typically have fixed revision cycles, such as annually or biennially.

Document structures are complex. They contain extensive medical terminology, abbreviations, and tables. Topics include pharmacological mechanisms, clinical symptoms, diagnostic criteria, treatment plans, and adverse reactions. Field and unit specificities involve medical dosages (e.g., mg/kg), time periods (e.g., days, weeks, months), and biological indicators (e.g., autoantibody titers, inflammatory factor levels). Precise identification and processing of these elements are necessary.

Constraints on Tool Calling and Plugins from these Characteristics

Autoimmune data characteristics impose specific requirements on tool calling and plugins. First, varying document update frequencies require flexible indexing or incremental update mechanisms. This ensures information timeliness. For example, when new treatment guidelines are released, tools must quickly identify and update knowledge base content.

Second, extensive medical terminology, abbreviations, and complex table structures in documents demand advanced text parsing and structured information extraction capabilities from tools. This prevents tool call failures or inaccurate results due due to semantic misinterpretation. For example, tools must recognize various abbreviations for "rheumatoid arthritis" and extract specific drug dosages from tables.

Third, unit conversion and validation for medical dosages and biological indicators require strict type checking and range validation during parameter passing. This prevents invalid inputs from causing errors. For example, tools must ensure drug dosages are within a safe range and correctly handle values with different units.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	Regulations and SOPs contain long descriptive paragraphs. This length helps maintain contextual completeness.
`Recall count` (Recall Count)	`Top 10`	This ensures coverage of multiple key information points related to complex regulatory clauses.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Autoimmune domain terminology has high similarity. A higher threshold is needed to filter the most relevant regulatory content.
`Rerank result count` (Reranked Return Count)	`Top 5`	This further refines recall results, prioritizing regulatory clauses that best match the user query.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF clinical trial reports or regulatory documents can take a long time.
`Max Concurrent Tool Calls`	`Calibrate by actual measurement`	This prevents connection timeouts due to excessive backend service pressure. Adjust based on actual deployment environment and concurrent request volume.

Three Common Pitfalls

Tool call returns connect ECONNREFUSED 172.23.0.2:3001 error. This indicates the tool's backend service is not running or network configuration is incorrect.
Queries for specific drug dosages return empty results. The document parser may have failed to correctly identify dosage fields in tables.
Knowledge recall accuracy decreases after changing the model interface from embedding-ada-002 to embedding-3. This is due to vector space differences caused by the model version change. Re-evaluation and adjustment of the similarity threshold are necessary.

How to Confirm Proper Configuration

Upload a complex table-containing autoimmune disease treatment guideline. Check if the tool correctly extracts key drug dosages and treatment cycles from the table.
Test the incremental update function of the knowledge base with a revised hospital SOP document. Confirm new content is accurately recalled by the tool.
Simulate multiple concurrent users querying different autoimmune regulations. Observe tool call response times and success rates to ensure service stability.
Use queries containing specialized terms and abbreviations. Verify that recalled regulatory clauses are highly relevant to user intent and check the effectiveness of the Similarity threshold (Similarity Threshold).

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.