Product Usage: HTTP Interface and External Systems for Smart

Data Characteristics for This Category

Biopharmaceutical product usage data originates from multiple channels. These include product manuals, adverse drug reaction reporting systems, clinical research reports, patient education materials, and online Q&A platforms. Data formats vary, encompassing both structured database records and extensive unstructured text like PDF documents, Word documents, and web content. Update frequencies differ; product manuals and official guidelines are relatively stable, but adverse reaction reports and clinical progress data may update daily or weekly. Document structures often include fixed sections in manuals (dosage, usage, contraindications, side effects), while patient consultation data exhibits free-form dialogue. Fields may involve drug name, batch number, production date, expiry date, patient age, gender, diagnosis, medication adherence, symptom description, and treatment outcomes. Symptom descriptions and adverse reaction reports are typically free text and lack standardized units.

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The diversity and update frequency of product usage data directly influence HTTP interface design and external system integration strategies. Large volumes of unstructured text require interfaces to handle multiple file types and support efficient text extraction and parsing. For example, extracting key information from PDF product manuals necessitates OCR or document parsing services. Data sources with varying update frequencies, such as adverse reaction reports, demand HTTP interfaces that support incremental updates and real-time synchronization mechanisms to ensure timely information delivery by smart customer service. The specificity of fields, especially free-text symptom descriptions, means external systems must perform further Natural Language Processing (NLP) upon receiving this data, such as entity recognition and symptom classification, to effectively support smart customer service Q&A logic. Furthermore, precise matching of fields like drug batch numbers and expiry dates imposes strict requirements on interface parameter validation and data accuracy to prevent misleading responses due to data errors.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`1200 Tokens`	Ensures coverage of patient questions and key information from product manuals, balancing depth of understanding with response speed.
`PARSE_FILE_TIMEOUT_SECONDS`	`180 seconds`	Accommodates parsing time for large PDF documents like biopharmaceutical product manuals, preventing timeouts.
`Chunk size`	`300 characters`	Balances semantic integrity of text with retrieval efficiency, avoiding excessive fragmentation or information redundancy.
`Recall count`	`Top 8 entries`	Increases coverage of relevant information, especially when dealing with complex product questions.
`Similarity threshold`	`0.78`	Ensures high relevance of retrieved results to patient questions, reducing interference from inaccurate information.
`HTTP Request timeout`	`60000 ms`	Accounts for potential network latency and processing time in external systems (e.g., drug databases or adverse reaction reporting systems).

Three Common Pitfalls

Calling external drug database interfaces returns empty content, resulting in the smart customer service being unable to answer specific drug information. This typically occurs because the drug name or batch number in the request parameters does not exactly match the database storage format, or the HTTP Request timeout setting is too short.
The request address configured in the model does not take effect, preventing the smart customer service from calling third-party large language model services. This might be due to an incorrect Request Address format, missing necessary protocol headers (e.g., https://), or an API Key that is incorrectly configured or expired.
Input parameters in HTTP responses are not recognized by the service, leading to subsequent logic interruption. This often happens because field names in the response body do not match expectations, or the JSON path expression is incorrect, failing to accurately extract the required data.

How to Verify Correct Configuration

Simulate patient inquiries covering product usage instructions, adverse reaction consultations, and drug dosage queries. Observe if the smart customer service's responses are accurate and complete, and verify the cited data sources.
Check FastGPT's HTTP request logs to confirm that all external system calls have status codes of 200 or 204, and that response times are within an acceptable range.
Configure a test HTTP interface in FastGPT, manually input all expected input parameters, and verify that it correctly parses and returns the expected results.
For frequently updated data sources, such as adverse drug reaction reporting systems, regularly trigger data synchronization tasks and sample some of the latest data to confirm that the smart customer service can access and utilize this updated information.

The values provided are common starting points; measure them 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.