Data Characteristics
Data for OA process initiation in internal office assistants, particularly in the biopharmaceutical sector, primarily originates from internal enterprise systems like ERP, CRM, and OA systems. This data typically exists in structured or semi-structured formats, such as approval documents, application forms, and project initiation documents. Data update frequency is relatively stable, usually updating in real-time as transactions occur or through daily/weekly batch synchronization. Document structures often include clear field definitions, such as applicant, application time, approval node, approval comments, associated project number, and budget amount. Field types are diverse, including text, numbers, dates, and enumerated values. Units consistently align with actual business scenarios; for example, monetary units are "yuan," time units are "hours" or "days," and quantity units are "boxes" or "bottles."
Constraints on Reference and Traceability
The structured nature of OA process initiation data imposes high demands on knowledge base segmentation strategies and recall accuracy for reference and traceability. For instance, critical fields in approval documents (e.g., "rejection reason" or "approval comments") must be accurately identified and cited to avoid misinterpreting process status. The stability of data update frequency means that knowledge base index updates do not need to be overly frequent, but timely synchronization of new process statuses is crucial. The specific business fields and units contained in documents require the model to understand their semantics and perform effective matching, especially when involving cross-system data correlation. Furthermore, since process initiation involves sensitive information, the traceability mechanism must precisely point to original documents and specific fields to meet compliance requirements.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500-800 characters | Ensures the completeness of individual process nodes or key information, preventing the splitting of semantically related fields. |
Recall count (Recall Count) | Top 5 entries | OA processes typically have clear steps and fields; too many entries introduce irrelevant information. |
Similarity threshold (Similarity Threshold) | 0.75-0.85 | Guarantees high relevance between recall results and the user's OA process intent, reducing false positives. |
Rerank result count (Reranked Return Count) | 3 entries | Focuses on the most critical process information and approval records, improving efficiency in obtaining key information. |
PARSER_MODE | Structured Parsing | OA form data is usually clearly structured; structured parsing enhances field recognition accuracy. |
Reference Link Template | https://oa.example.com/workflow?id={{doc_id}}&step={{step_id}} | Ensures traceability links can directly jump to the specific process or step in the original OA system. |
Common Pitfalls
- Garbled characters or unexpected characters in reference results typically stem from a mismatch between the knowledge base encoding and the original OA system data encoding.
- Model responses lack specific process details, providing only generic replies. This occurs when knowledge base segments are too long or the recall count is insufficient, diluting or omitting critical field information.
- When initiating a process, certain fields (e.g., "application amount") are empty or incorrectly formatted. This happens when the model fails to correctly extract numerical or date-type fields from the original document during citation or fails to perform necessary format conversions.
Verification Steps
- For typical process initiation scenarios, simulate user queries and check if the cited knowledge base content in the response precisely points to key fields and process steps in the original OA document.
- Verify the reference links provided in the response, ensuring they correctly navigate to the corresponding page in the enterprise OA system and display information consistent with the cited content.
- Select OA forms containing various data types (text, numbers, dates, enums) for testing to confirm the model accurately identifies and cites values from these different field types.
Note: The values provided are common starting points and should be measured against specific 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.