Data Characteristics
Academic promotion policy data in the biomedical field originates from internal compliance, medical affairs, and market access departments. This data exists in various document formats, including internal training materials, compliance guidelines, codes of conduct, medical representative conduct norms, academic conference management procedures, gift management regulations, and clinical research support policies. Common document formats are PDF, DOCX, XLSX, or HTML pages exported from internal knowledge management systems.
Policy updates occur quarterly or semi-annually due to regulatory changes or internal strategy adjustments. However, critical compliance documents may be revised at any time. Document structure varies: some are well-structured with clear chapter titles and tables of contents, while others are detailed implementation guidelines with extensive tables and figures. Common fields include policy number, effective date, revision version, scope, specific clauses, approval processes, and violation handling. Units may involve time periods (e.g., "monthly," "quarterly"), monetary limits (e.g., "not exceeding X yuan per instance"), and event counts (e.g., "annual academic conference count").
Constraints on Knowledge Base Retrieval and Recall
The mixed structure of academic promotion policy documents challenges knowledge base segmentation. Normative documents require clause integrity, while detailed guidelines with tables and figures need additional processing to extract structured information.
Unpredictable update frequency demands efficient incremental updates and version management from the knowledge base. This ensures retrieval results are always based on the latest effective policies. Extensive use of specialized terminology and legal provisions requires high semantic understanding from embedding models to accurately capture logical relationships and compliance points between clauses.
Field information, such as effective dates, may serve as retrieval filters, impacting recall precision. For example, when users query policies within a specific effective date range, the knowledge base must identify and apply these temporal constraints. The presence of monetary and quantity units requires careful handling of numerical queries to avoid misinterpretations due to unit confusion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 800–1200 characters | Ensures policy clause integrity, prevents key information from being split, and maintains semantic coherence. |
Overlap Length | 100–200 characters | Maintains contextual continuity, helping the model understand logical relationships across segments. |
Recall count (Recall Count) | Top 5–8 entries | Balances coverage while reducing interference from irrelevant information, improving retrieval efficiency. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 (calibrated by actual measurement) | Balances recall rate and accuracy, preventing the retrieval of irrelevant policy clauses. |
Rerank result count (Reranked Return Count) | 3–5 entries | Further optimizes result relevance through secondary sorting, providing the most accurate answers. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient parsing time for large compliance documents or detailed guidelines with complex tables, preventing timeout errors. |
Common Pitfalls
- Stuck at the indexing step when creating a new knowledge base: This is typically due to
PARSE_FILE_TIMEOUT_SECONDSbeing set too low, causing timeouts when processing large or complex documents. - Answer content inconsistent with knowledge base settings: The embedding model's semantic understanding of specialized terminology and legal provisions may be insufficient, leading to retrieved text segments that do not precisely match user intent.
- Search test errors after adding a new embedding model: The
Similarity threshold(Similarity Threshold) might be set too strictly, causing all recall results to fall below the threshold and be filtered out, or the model may not have loaded correctly.
Validation Steps
- Select typical compliance questions and test FastGPT's accuracy in recalling policy clauses. Evaluate whether the recalled results include all relevant provisions.
- Upload policy documents with the latest revision dates. Query questions related to older versions to confirm the knowledge base correctly identifies and prioritizes the latest effective version.
- For queries involving monetary amounts, time, and other units, verify the accuracy of numerical information in FastGPT's answers and check for unit consistency.
- Simulate high-concurrency query scenarios to check knowledge base retrieval response times, confirming stable service under load.
Note: 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.