Database and Operations for Cardiovascular R&D Document

Data Characteristics

Cardiovascular R&D documents include clinical trial reports, drug mechanism studies, disease pathway analyses, imaging diagnostics, and genomic reports. Data sources are diverse, including authoritative medical journals, clinical databases, pharmaceutical company internal research, and public datasets. Document updates are frequent, especially during clinical trials, with data potentially updating weekly or monthly. Document structures vary, encompassing both standardized structured reports (e.g., CRF forms) and extensive unstructured text (e.g., researcher experimental notes, meeting minutes). Fields and units are complex. For example, blood pressure uses mmHg, heart rate uses bpm, and drug dosage uses mg/kg. These often include modifiers like upper/lower limits and confidence intervals.

Constraints on Database and Operations

The diversity of cardiovascular R&D documents requires flexible database storage to accommodate mixed structured and unstructured data. High update frequency necessitates efficient incremental updates and version management to maintain knowledge base timeliness. Complex fields, units, and specialized terminology demand high accuracy in text parsing, requiring more refined preprocessing and entity recognition models. The wide range of data sources leads to large data volumes, challenging database storage capacity and retrieval performance. Operationally, robust data synchronization mechanisms and error handling processes are necessary to manage data source changes or parsing failures, while ensuring data security and compliance.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`MONGODB_URI`	`mongodb://user:password@host:port/fastgptdb?authSource=admin`	Ensures FastGPT connects to the MongoDB instance. `authSource` specifies the authentication database.
`maxContext`	`1000–1500 characters`	Cardiovascular document paragraphs are information-dense. Increasing context length helps retain the integrity of key medical concepts.
`Chunk size` (Segment Length)	`300–500 characters`	Balances semantic completeness and vector retrieval efficiency, preventing information loss from segments that are too long or too short.
`Recall count` (Recall Count)	`top 8–12 items`	Increases recall quantity to cover more potentially relevant medical facts and clinical data, improving answer comprehensiveness.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Balances recall and accuracy, reducing the risk of mis-recalling low-relevance medical information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Cardiovascular documents often contain many charts and complex layouts. Parsing can take longer, preventing timeout failures.

Common Pitfalls

Authentication failed error when connecting to the database. This usually indicates incorrect username, password, or authentication database configuration in MONGODB_URI.
File parsing timeout after uploading large clinical trial reports. This occurs when PARSE_FILE_TIMEOUT_SECONDS is set too short for the document's complex structure and content volume.
Missing units or values for key medical metrics in knowledge base answers. This often happens when Chunk size (Segment Length) is too short, separating associated unit information and values into different segments.

Verification Steps

Check FastGPT backend logs for MongoDB connection error or MongooseError: connection timeout messages.
Upload a cardiovascular PDF document containing complex tables and specialized terminology. Verify its parsing status shows "success" and check if generated segments contain complete key information.
Perform knowledge base queries using precise numerical questions, such as those involving specific cardiovascular disease symptoms or drug dosages. Verify the accuracy of numerical values and units in the returned results.
Monitor CPU, memory, and disk I/O usage on the database server. Ensure system resources remain healthy during high-concurrency queries and data imports.

The values given 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.