Tool Calling and Plugins for Real-World Evidence Research

Data Characteristics for This Category

Real-World Evidence (RWE) regulatory documents include research protocols, ethics approvals, data management plans, statistical analysis plans, and study reports. These documents are typically PDF files. They contain extensive unstructured text, tables, and figures. Data sources are mainly research institutions, hospitals, and regulatory bodies. Update frequency varies from months to years, depending on research progress and regulatory requirements. Document structures are complex. For example, a research protocol includes multiple levels such as background, objectives, design, inclusion/exclusion criteria, and observation indicators. A study report may contain an abstract, introduction, methods, results, discussion, and conclusion. Fields and units involve medical terminology, drug dosage units (e.g., mg, mL), time units (e.g., weeks, months), statistical indicators (e.g., P-value, OR value), and specific coding systems (e.g., ICD-10).

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The complexity of RWE regulatory documents imposes several constraints on tool calling and plugins. First, complex tables and figures in PDFs make it difficult for traditional text parsing tools to accurately extract structured information. This requires more advanced parsing capabilities. Second, inconsistent document update frequencies demand that plugins support version management and incremental updates. This ensures that retrieved regulations are current and valid. Third, regulatory documents contain many specialized terms and abbreviations. This requires dedicated medical dictionaries or ontologies to avoid semantic misunderstandings. Additionally, patient privacy and sensitive information are involved. Tool calls must meet strict data security and compliance requirements. Finally, different research protocols may have cross-references and associations. Plugins must identify and link these internal references to enable cross-document knowledge navigation.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	RWE regulatory documents, especially reports with many figures, can have large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing complex PDF files, especially table and figure recognition, is time-consuming.
`Chunk size` (Segment Length)	`800–1200 characters`	RWE regulatory paragraphs are often long. Retaining sufficient context aids semantic understanding.
`Recall count` (Recall Count)	`Top 8`	Regulatory Q&A demands high accuracy. Recalling more relevant context improves precision.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures retrieved regulatory segments are highly relevant to the query. Avoids introducing irrelevant information.
`Rerank result count` (Reranked Return Count)	`Top 5`	Further refines recall results. Improves the quality and conciseness of the final answer.

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

Three Common Mistakes

Plugin call fails with 404 Not Found or Connection refused in logs. This typically indicates that the plugin service is not deployed correctly or network configuration is improper. FastGPT cannot connect to the plugin's API endpoint.
Table content is garbled or missing after PDF parsing. This usually means the PDF parser's ability to recognize complex table structures is insufficient. It cannot correctly extract cell data, or parsing parameters are not optimized for tables.
The model's answer cites an outdated version of a regulation. This typically happens when the knowledge base is not updated promptly. The plugin fails to identify and call the latest version of the regulatory document. This leads to the retrieval of old information.

How to Confirm Correct Configuration

Upload RWE regulatory PDF files with complex tables and figures. Check the completeness and accuracy of tables and text in the parsing results.
Ask questions about specific clauses and technical terms in the regulations. Verify that the model's answer accurately quotes the original regulatory text and complies with the latest regulations.
Simulate a regulation update scenario. Upload a new version of the document and ask questions again. Confirm that the model correctly cites the new version and identifies differences between old and new versions.

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.