Database and Operations for GMP-Compliant R&D Document

Data Characteristics

GMP-compliant R&D document data originates from internal pharmaceutical R&D management systems, quality management systems, and various laboratory record systems. Data update frequency is relatively low, typically aligning with project milestones, batch production records, or regulatory revision cycles. Document types are diverse, including experimental protocols, raw data, analysis reports, batch production records, deviation investigation reports, and change control documents. These documents are often in PDF, Word, or scanned image formats. Fields and units are highly standardized and regulated, such as batch number, production date, expiration date, test results (numerical values, units like %, mg/mL), instrument calibration data, and operator signatures. Documents contain extensive tabular data, charts, and structured descriptive text.

Constraints Imposed by Data Characteristics on "Database and Operations"

The low update frequency of GMP-compliant documents means the database does not require frequent full synchronization or real-time index updates. The focus is on stable storage and efficient retrieval of historical data. Document diversity requires the parsing module to handle different formats and extract key fields. The high standardization of fields and units necessitates designing the database with sufficient structured fields. Unit identification and standardization, such as unifying all concentration units to mg/mL, must be achievable through regular expressions or specific rules. The large amount of tabular data and charts demands strong structured parsing capabilities to accurately recognize table content as structured data and extract key information (e.g., trends, peak values) from charts into descriptive text. These constraints collectively require the database to possess robust text processing, structured data storage and retrieval capabilities, and an efficient parsing service operations strategy.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`50 MB`	GMP documents often contain numerous charts and scanned images, resulting in large file sizes. Support for uploading large files is necessary.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	OCR and structured parsing of complex PDFs or scanned images can be time-consuming. This prevents parsing timeouts.
`Chunk size`	`800 characters`	Retains sufficient contextual information while preventing excessively long segments that hinder comprehension. Suitable for regulatory clauses and experimental procedures.
`Recall count`	`Top 5 entries`	Ensures queries cover multiple relevant regulations or experimental records, improving accuracy.
`Similarity threshold`	`0.75`	Strictly filters irrelevant content, ensuring retrieved results are highly relevant to GMP compliance requirements.
`MONGODB_URI`	`mongodb://user:password@host:port/database?authSource=admin`	Explicitly specifies authentication information and database name, ensuring FastGPT can correctly connect to the MongoDB instance.

Common Pitfalls

FastGPT cannot connect to MongoDB. This is due to incorrect specification of the database name or authentication information in the MONGODB_URI configuration string, leading to connection failure or insufficient permissions.
After document parsing, key fields like batch number and expiration date are empty in the knowledge base. This typically occurs because parsing rules are not optimized for the specific format of GMP documents, failing to accurately extract these standardized fields.
When users query GMP compliance issues, the returned results have poor relevance or lack critical information. This may stem from excessively long document segments or a Similarity threshold set too low, diluting semantic information or recalling a large amount of irrelevant content.

Verification Steps

Upload a typical GMP batch production record PDF file. Check if the parsed knowledge base accurately identifies and stores key structured fields such as batch number, production date, and expiration date.
Perform parsing of an experimental report containing complex tabular data. Verify that the table content in the knowledge base is correctly converted into retrievable structured data or descriptive text.
Query a document containing specific regulatory clauses. Verify that the AI platform can accurately cite relevant clauses and provide GMP-compliant answers. Also, check if Recall count and Similarity threshold yield the expected number of retrieval results.

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.