Data Characteristics
Mental health policy and SOP documents originate from regulatory bodies (e.g., National Health Commission, National Medical Products Administration), and internal hospital guidelines covering diagnosis, nursing, ethical review, and clinical trial protocols. These documents are typically in PDF, Word, or structured web page formats. Updates are infrequent, usually coinciding with policy changes or new clinical techniques. Document structures are complex, containing specialized terminology, flowcharts, tables, and legal citations. Fields often include diagnostic criteria (e.g., ICD-10 codes), drug dosages (mg/kg), treatment durations (weeks/months), risk assessment scales (e.g., Hamilton Depression Rating Scale scores), and informed consent templates. Units are precise, often with multiple decimal places, and frequently accompanied by specific medical abbreviations.
Constraints Imposed on Document Parsing and Chunking
The complex structure of mental health policy documents demands advanced parsing capabilities. Flowcharts and nested tables require specific processing strategies to maintain information integrity and prevent critical logical breaks. The high density of specialized terminology and abbreviations can lead to poor performance from general-purpose tokenizers, affecting the accuracy of subsequent embedding vectors. Infrequent updates mean that initial parsing must be as complete and accurate as possible to minimize frequent adjustments later. Critical numerical information, such as diagnostic criteria and dosages, requires precise identification and retention of contextual associations during parsing to avoid misinterpretations of values or units. Additionally, the citation relationships of legal provisions must be effectively recognized by the parser to enable traceability during question-answering.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Size | 800–1200 characters | Balances contextual completeness with retrieval efficiency, preventing excessively long chunks. |
Chunk Overlap Size | 100–200 characters | Ensures key information across segments is linked, reducing semantic fragmentation. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates complex parsing requirements for large PDF or Word documents. |
Max File Size | 200 MB | Handles policy documents containing numerous charts and tables. |
Text Cleaning Rules | Remove headers, footers, table of contents, consecutive blank lines | Reduces irrelevant information interference and improves text quality. |
Enable Table Parsing | Yes | Ensures structured data within tables is correctly extracted. |
Common Mistakes
- After importing a web link to the knowledge base, parsing fails or the content is empty. This often occurs because the link points to a page requiring login or specific permissions, or the content is dynamically loaded by JavaScript, preventing the parser from directly accessing the HTML text.
- After parsing an imported PDF file, question-answering results regarding drug dosages or diagnostic criteria are inaccurate. This is frequently due to the PDF being a scanned image with poor Optical Character Recognition (OCR) quality, leading to errors in recognizing numbers or medical terms, which then affects subsequent vector embeddings.
- When calling the FastGPT API for document parsing, the HTTP request returns a 200 status code, but the knowledge base is not updated or the content is incomplete. This might be due to an improperly set
Content-Typeheader, preventing the backend service from correctly identifying and processing the file type, or file encoding issues.
Verification Steps
- Upload a policy document containing complex tables and flowcharts. Review the parsed chunks to ensure the completeness and logical coherence of table data and process descriptions.
- Query using specific medical terms or legal provisions from the document. Verify that the question-answering results accurately recall relevant chunks and provide correct contextual information.
- Compare the original document with the text preview in the FastGPT interface after parsing. Check for any obvious missing key information, garbled text, or formatting errors, especially concerning numbers and units.
- For documents containing multiple citations or internal links, test whether the question-answering system can identify and suggest related documents or sections to assess its traceability capabilities.
The values provided are common starting points and should be measured against your 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.