Cardiovascular Products: HTTP API and External Systems

Data Characteristics

Cardiovascular product data originates from clinical trial reports, drug inserts, medical literature, disease databases, and regulatory approvals. Data updates are generally stable, with concentrated bursts when new drugs launch or clinical guidelines update. Document structures typically adhere to standardized medical terminology and coding systems like ICD-10 and SNOMED CT. Core fields include indications, contraindications, dosage and administration, adverse reactions, pharmacological actions, pharmacokinetics, and clinical study results. Dosage units are commonly milligrams (mg), grams (g), or milliliters (ml), and time units are hours (h) or days (d). Complex interaction descriptions are also common.

Constraints on HTTP API and External Systems

The specialized and structured nature of cardiovascular product data requires HTTP APIs to support complex query conditions and data type validation. For example, querying drugs for specific indications or reagents with particular adverse reactions necessitates an API that can parse nested JSON or XML structures. Stable update frequencies make incremental synchronization a more efficient approach than full data pulls. The prevalent medical terminology and coding systems in documents require external systems to preprocess or map data before import, ensuring accurate recognition and association within the knowledge base. Additionally, the complex descriptions in fields like pharmacological actions and pharmacokinetics demand refined strategies for text segmentation and vectorization to preserve contextual semantics. The precision required for measurement units mandates strict adherence to predefined formats during data parsing to avoid unit confusion.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunkOverlapRatio`	`0.1`	Ensures sufficient overlap between segments to capture contextual relationships of medical concepts.
`maxContext`	`6000`	Cardiovascular product inserts and clinical reports are often long, requiring a larger context window.
`embeddingModel`	`text-embedding-ada-002` or higher	Improves vectorization accuracy for specialized terminology and complex concepts.
`pushData_batch_size`	`100`	Controls the amount of data pushed in a single operation to optimize performance, considering the average length and complexity of medical texts.
`timeout_seconds`	`600 seconds`	Large-scale medical data processing can be time-consuming, requiring ample API response time.
`http_headers`	Includes `Authorization: Bearer <token>`	Ensures secure data transfer and authentication between the external system and FastGPT.

Common Pitfalls

An HTTP request returning a 400 Bad Request status code typically indicates that the uploaded data structure does not conform to the expected JSON format of the FastGPT API, such as missing required fields or data type mismatches.
Drug names or indications imported into the knowledge base are not effectively retrievable because the data pushed from the external system was not standardized using unified medical terminology, leading to suboptimal vectorization.
After importing a large volume of data, some field content appears incomplete or truncated. This occurs when the maxContext parameter is set too low, failing to fully capture lengthy pharmacological action descriptions or clinical trial results.

Verification Steps

From the FastGPT management interface, randomly select 10 imported cardiovascular product data entries and verify that key fields (e.g., indications, dosage and administration) match the original data source.
Use FastGPT's knowledge base retrieval function. Input disease names or drug components to confirm accurate recall of relevant product information and check the semantic completeness of the retrieved content.
Review FastGPT API logs to confirm no 500 Internal Server Error or other critical errors occurred during data import, and that the data push API response times are within acceptable limits.
Perform simulated user queries asking about the usage or adverse reactions of specific cardiovascular products. Evaluate the AI's response accuracy and professionalism to determine if the knowledge base content is effectively utilized.

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.