Data Characteristics
Patient Assistance Program (PAP) policy documents in the biopharmaceutical sector originate from official publications by pharmaceutical companies, foundations, hospitals, or third-party organizations. These documents are typically updated infrequently (quarterly, annually, or upon policy changes), but updates often involve comprehensive revisions of terms. Document formats are primarily PDF or Word files. Content structure is complex, encompassing numerous legal clauses, approval processes, medication scopes, eligibility criteria, and funding standards. Common fields include patient name, ID number, disease diagnosis, drug name, medication cycle, funding amount, approval status, application date, and effective date. Units such as "milligrams," "cycles," and "yuan" require precise identification and processing. Some documents also contain complex tabular data, listing assistance ratios for different drugs or funding caps for various disease stages.
Constraints Imposed by These Characteristics on Workflow Orchestration
The complex structure of PAP policy documents places specific demands on workflow orchestration. Infrequent but extensive document revisions necessitate version management during knowledge base construction. This ensures each document parse corresponds to a specific version, preventing confusion. Unstructured formats like PDF and Word require efficient document parsing nodes to accurately extract text content and tabular data. For legal clauses and approval processes, workflows need multi-turn question-answering logic to ensure complete and accurate information delivery to users. For example, eligibility criteria for a specific patient may require multi-step verification. Precise field identification, such as drug names and funding amounts, requires unit standardization after information extraction (e.g., converting "ten thousand yuan" to "yuan") to avoid calculation errors. Additionally, due to the sensitive nature of the information, data anonymization and permission control nodes are essential within the workflow.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters (characters) | Balances context completeness and retrieval efficiency, suitable for clause-based text. |
Recall count (Recall Count) | 5 entries (items) | Ensures coverage of relevant clauses, avoiding information omission. |
Similarity threshold (Similarity Threshold) | 0.78 | Improves retrieval accuracy, filtering out irrelevant content. |
Rerank result count (Rerank Return Count) | 3 entries (items) | Refines the final result, focusing on the most relevant information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Accommodates the time required for parsing large PDF documents, preventing timeouts. |
maxContext | 16000 tokens | Accommodates the contextual needs of complex policy clauses. |
Three Common Mistakes
- After tool invocation, results do not display on screen. This is due to a missing "output results" node in the workflow or results being overwritten by other nodes.
- A 404 error occurs when the document parsing node processes a file uploaded in a server environment. This typically indicates incorrect file path or access permission configuration after frontend packaging, preventing the server from accessing the uploaded file.
- Inability to select the required knowledge base. This happens when global variables are not correctly assigned or the conditional judgment node logic is flawed, failing to route user requests to the matching knowledge base.
How to Verify Correct Configuration
- Upload different types of PAP policy documents and verify that the document parsing node successfully extracts all key fields. Compare the extracted data against the original document content.
- Use test questions containing specific clauses and eligibility criteria to verify that the workflow accurately retrieves relevant knowledge snippets and generates expected answers.
- Simulate various complex query scenarios, such as questions involving multiple conditional judgments or multi-turn interactions. Check that the workflow's logical branches trigger correctly and lead to the final result.
The values provided 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.