HTTP API and External Systems for CMC Research Quality

Data Characteristics

CMC (Chemistry, Manufacturing, and Controls) research quality documents encompass data from the entire drug development and production lifecycle. Data sources are diverse, including laboratory analysis reports, manufacturing batch records, stability study data, raw material and excipient supplier qualification documents, and quality control standard operating procedures (SOPs). These documents update with relatively stable frequency, typically tied to project phase, regulatory requirements, or production batches. For example, stability data might update monthly or quarterly, while batch records generate with each production batch. Document structures are complex, often containing both structured data (e.g., analysis result tables, equipment parameters) and unstructured data (e.g., experimental descriptions, deviation report text). Fields and units are highly specialized, such as content percentage (%), impurity limits (ppm), pH values, and spectroscopic absorbance (AU), often accompanied by specific testing methods and instrument models.

Constraints Imposed by These Characteristics on HTTP API and External Systems

The complexity and specialized nature of CMC research documents demand robust HTTP APIs and strong data parsing capabilities. Diverse document structures mean the API must support various input data formats, such as PDF, Word, Excel, and specialized scientific data formats. The stable update frequency allows for periodic data synchronization strategies, reducing pressure on real-time API calls. However, critical batch data and change control documents require trigger-based update mechanisms. Specialized fields and units necessitate accurate named entity recognition and unit normalization after data extraction to avoid ambiguity. Furthermore, cross-referencing and version control among documents are common. When retrieving a single document, the HTTP API may need to retrieve associated documents or historical versions to ensure complete context. High demands for data consistency and accuracy also require more detailed error handling mechanisms in the API, such as defining response codes for data validation failures.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`8192`	Ensures complete context for long texts like key experimental batches and analysis reports.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing large PDF or Word documents can take considerable time; this prevents parsing failures due to timeouts.
`Chunk size`	`800–1200 characters`	Balances semantic completeness with recall efficiency, ensuring each segment contains sufficient information.
`Recall count`	`Top 5 entries`	Prioritizes the top 5 most relevant document segments for CMC research, reducing interference from irrelevant information.
`Similarity threshold`	`0.75`	Ensures recalled document segments are highly relevant to the query, filtering out vague matches.
`Rerank result count`	`3`	Reranks initial recall results to further improve relevance and focus on core information.

Common Pitfalls

Symptom: API calls return do_request_failed or 500 Internal Server Error, and logs indicate the request body is too large. Reason: The uploaded document file size exceeds the UPLOAD_FILE_MAX_SIZE limit configured for the HTTP server or FastGPT interface.
Symptom: In streamed conversational output, hyperlinks are not clickable and navigate within the current page instead of opening a new one. Reason: The frontend application's parsing and rendering logic for streamed output is flawed, failing to correctly process or recognize link attributes in the response.
Symptom: After a conversational API call, the detailed content in the conversation logs does not match the actual response, or always displays the same content. Reason: FastGPT versions v4.8.10 and earlier may have caching or synchronization issues with log recording, leading to discrepancies between the displayed details and the actual call results.

Verification Steps

Upload a typical CMC research report (e.g., a stability report with multiple tables and charts) via the FastGPT management interface. Observe if the number and content of its segments meet expectations.
Use tools like curl or Postman to simulate an external system calling FastGPT's conversational API. Provide a query about batch analysis results and check if the returned professional terms and data are accurate.
Within FastGPT or using external monitoring tools, verify if the parsing time for long documents is within a reasonable range and free of timeout errors after PARSE_FILE_TIMEOUT_SECONDS and other configurations take effect.

The values provided 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.