Deployment and Upgrade for CMC Research Registration and

Data Characteristics for This Category

CMC research registration and submission documents typically contain large volumes of structured and unstructured data. Data sources are diverse, including raw laboratory records, batch production records, quality standards, stability study reports, and analytical method validation reports. These documents can exist in various formats such as PDF, Word, Excel, or images. The data update frequency is relatively low, primarily occurring during phased R&D reports, process changes, or before submitting registration documents. Document structures are complex, often with nested hierarchies. For example, a batch production record might contain multiple sub-modules, each with detailed process parameters, quality control points, and test results. Fields and units are highly specialized. For instance, "assay" results might be expressed as "% (w/w)," and "dissolution" as "%." Identifiers like batch numbers, serial numbers, and instrument numbers require precise recognition.

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

The complexity of CMC data requires FastGPT deployments to have strong file parsing capabilities and data indexing strategies. Multi-format documents necessitate configuring robust file preprocessing plugins and OCR recognition. The low update frequency means a large initial data import, requiring attention to parameters like UPLOAD_FILE_MAX_SIZE and PARSE_FILE_TIMEOUT_SECONDS to ensure complete file upload and parsing. The complex document structure and specialized fields mean knowledge base segmentation strategies and recall mechanisms need high optimization to maintain information context integrity and professional terminology accuracy. Deployment environment stability is also crucial to prevent data parsing interruptions or incomplete indexing due to system failures, which would affect subsequent question-answering accuracy.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	CMC reports often have large file sizes; this ensures complete document upload.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large files takes longer; this prevents timeouts during parsing.
`Chunk size`	`800–1200 characters`	Maintains the contextual integrity of professional descriptions in CMC reports, avoiding semantic fragmentation.
`Recall count`	`Top 5 entries`	Improves the recall rate of relevant information, covering multi-dimensional CMC data.
`Similarity threshold`	Calibrate by actual measurement	Requires balancing precise matching of professional terms with semantic relevance, avoiding false positives.
`Rerank result count`	`Top 3 entries`	Prioritizes the most relevant key information for engineers to quickly locate.

Three Common Mistakes

After docker-compose up, the AI platform interface shows no models available. This usually indicates incorrect BASE_URL or API_KEY configuration, preventing FastGPT from connecting to the upstream large language model service.
After uploading a large PDF document, knowledge base parsing progress stalls for a long time or reports an error. This might be due to PARSE_FILE_TIMEOUT_SECONDS or UPLOAD_FILE_MAX_SIZE parameters being set too low, causing file upload or parsing to time out.
Questions about specific batch numbers for production processes do not receive accurate answers. This could be due to an inappropriate knowledge base segmentation strategy, where batch information and process details are in different segments, affecting semantic correlation.

How to Confirm Correct Configuration

Through the FastGPT administration interface, verify successful connection to the configured large language model service and correct recognition of its name.
Upload a typical CMC report PDF file. Observe if the file uploads and parses completely, and if corresponding segmented content appears in the knowledge base.
For the uploaded CMC data, try asking complex questions involving specific batch numbers, experimental parameters, or quality standards. Verify that FastGPT can recall and generate accurate answers from the knowledge base, and that the answers include the correct professional terminology and units.

Note: The values provided are common starting points and should be measured against specific samples and requirements.

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.