Deployment and Upgrade for Academic Promotion Quality

Data Characteristics for This Category

Academic promotion quality documents in the biomedical field primarily consist of Clinical Study Reports (CSRs), medical literature reviews, drug inserts, internal research data, and regulatory approval documents. These documents typically update several times a year, driven by new product launches, expanded indications, clinical data releases, or regulatory policy changes. Document structures are complex, often containing extensive specialized terminology, charts, statistical data, and references. Common formats include PDF, DOCX, and PPTX. Core fields include drug name, indication, dosage and administration, adverse reactions, clinical trial results (p-value, confidence interval), mechanism of action, reference DOI, and version number. Units include milligrams, milliliters, international units, percentages, and statistical significance levels.

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

The large volume and specialized nature of academic promotion documents demand advanced text extraction and semantic understanding capabilities from the knowledge base. The unpredictable update frequency requires efficient document version management and incremental update mechanisms to avoid redundant processing and data duplication. Charts and statistical data within documents increase text parsing complexity, potentially leading to loss or misinterpretation of critical information. The dense use of specialized terminology and abbreviations necessitates accurate domain vocabulary recognition by the model. These factors collectively dictate that deployment and upgrade processes require fine-tuned configuration of document parsing strategies, vectorization model selection, and retrieval mechanisms to ensure information accuracy and timeliness.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accommodates large clinical trial reports or literature reviews, which are typically substantial in size.
`Chunk size` (Chunk Size)	`800–1200 characters` (characters)	Balances contextual completeness with model processing efficiency, preventing information dilution in long chunks or insufficient context in short chunks.
`Overlap Size`	`100 characters` (characters)	Ensures contextual continuity between chunks, reducing the risk of critical information being truncated at chunk boundaries.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Provides ample time to parse complex PDF or DOCX documents, especially those containing numerous charts and tables.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Increases the threshold for highly specialized academic content to ensure precision in retrieval results and minimize irrelevant information.
`Recall count` (Number of Retrieved Chunks)	`Top 5–8 entries` (top 5–8 chunks)	Maintains information coverage while reducing the number of tokens processed by the model, improving response speed.

Three Common Mistakes

Knowledge base answer accuracy is low, with poor understanding of DOCX and Excel document content, manifesting as missing or incorrect key information. This usually occurs because default document parsers inadequately handle complex tables, charts, or specific formats (e.g., embedded objects), leading to incomplete content extraction.
After local deployment, the model and vector model ping successfully, but FastGPT reports errors and cannot be used normally. This might be due to incorrect configuration of ONEAPI_KEY or OPENAI_API_KEY, or the local LLM_MODEL and VECTOR_MODEL names do not match the names expected by FastGPT internally.
The content extraction module performs poorly with large models like Qwen2.5-72B, failing to effectively extract core data. This is often related to improper Chunk size (Chunk Size) and Overlap Size settings, leading to fragmented or redundant context input for the large model, affecting its comprehension and summarization abilities.

How to Verify Configuration

Upload typical documents (e.g., a multi-page clinical trial report PDF and a DOCX file containing tables). Check the chunk preview in the knowledge base to confirm that text content, table data, and chart titles are extracted completely and accurately.
Query using specialized terminology, drug names, or clinical trial numbers from the document. Verify that FastGPT's answers precisely cite the original document and that the data in the answers aligns with the original text.
Simulate a document update scenario by uploading a new version of a document. Observe whether the knowledge base identifies version differences and verify that queries for old and new versions correctly correspond to their respective content, confirming the version control mechanism is effective.

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.