Data Characteristics
Academic promotion policy documents originate from internal compliance, medical affairs, or marketing departments. These documents are typically in PDF, Word, or internal knowledge management system page formats. Update frequency is low, usually annually or when major regulatory changes occur. Document structures are rigorous, containing numerous clauses, definitions, flowcharts, and case descriptions. Chapter headings have clear hierarchical levels. Content covers drug research and development, clinical trials, market access, sales conduct guidelines, academic conference organization, expert collaboration, and data disclosure. Fields and units often include approval numbers, effective dates, revision numbers, and cited regulatory article numbers. They may contain medical terminology, drug names, disease classification codes (e.g., ICD-10), specific time periods (e.g., 30 days, 6 months), and monetary units.
Constraints on "Vector Model and Indexing"
The low update frequency of academic promotion policy documents means less pressure for incremental updates after initial full indexing. However, each update requires ensuring document completeness and consistency. The rigorous document structure and clear chapter hierarchy demand a chunking strategy that maintains logical integrity, preventing the severance of critical clauses or definitions. The large volume of specialized terminology and regulatory articles requires high semantic understanding from the vector model. The model needs to accurately capture relationships between clauses and contextual meanings, distinguishing subtle differences in similar phrasings. Time periods, monetary units, and specific approval processes require precise matching or range matching during retrieval. Documents may contain sensitive information, necessitating data security and access control during indexing and retrieval to ensure only authorized users can access relevant content.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 800–1200 characters | Balances semantic integrity and vectorization efficiency. Prevents critical clauses from being truncated and reduces the processing complexity of individual chunks. |
Chunk Overlap Length (Overlap Length) | 100–150 characters | Ensures contextual continuity, especially in clause definitions or complex process descriptions, improving retrieval accuracy. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Accommodates policy documents that may include numerous charts or appendices, ensuring large files can be uploaded successfully. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Policy document parsing is complex; this allows sufficient time for processing, preventing parsing failures due to timeouts. |
Recall count (Recall Count) | Top 5 entries (Top 5) | Policy Q&A demands high precision. Increasing the recall count improves the probability of hitting critical information. |
Similarity threshold (Similarity Threshold) | Calibrate by actual measurement | Based on actual test results, balances recall and precision, preventing irrelevant clauses from being recalled. |
Common Pitfalls
- After uploading knowledge base files, some chunk vectorizations fail, leading to incomplete or missing retrieval results. This often occurs due to complex file content, including special characters or formats, causing text extraction or vector model processing errors.
- After updating the FastGPT version, the knowledge base with the original index cannot support vector retrieval, resulting in no search results. This may stem from changes in the vector model or underlying index structure after the version upgrade, requiring re-indexing or adaptation of the old knowledge base.
- After uploading a large batch of files, the knowledge base fails to automatically become ready and index, remaining in a stuck state. This typically indicates insufficient server resources (e.g., memory, CPU), preventing simultaneous processing of numerous file parsing and vectorization tasks, leading to a blocked processing queue.
Validation Steps
- Upload multiple representative policy documents and observe their chunking to ensure critical clauses, definitions, and flowchart descriptions maintain semantic integrity within a single chunk.
- For core policy clauses, simulate user queries and check if retrieval results accurately recall relevant sections. Evaluate the completeness and relevance of the recalled content.
- Monitor backend logs to confirm no abnormal errors occurred during file upload, parsing, and vectorization, especially errors related to
PARSE_FILE_TIMEOUT_SECONDSor vectorization.
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.