HTTP Interface and External Systems for CMC Research

Data Characteristics

CMC research regulation data originates primarily from internal pharmaceutical company quality management system documents, regulatory compliance guidelines, internal SOP (Standard Operating Procedure) files, and historical change records. These documents typically exist as PDFs, Word files, or structured text within internal knowledge base systems. Data update frequency is relatively low, usually triggered by regulatory revisions, new product development, or internal process optimizations, occurring quarterly or annually. Document structures are complex, containing extensive specialized terminology, charts, flowcharts, and cross-references. Key fields include, but are not limited to: SOP ID, Version Number, Effective Date, Revision History, Operating Steps, Quality Standards, Risk Assessment, Instrument Equipment ID, and Reagent Lot Number. Units involve common physical quantities (e.g., mg/mL, ℃, kPa) and time units (e.g., hours, days).

Constraints Imposed by "HTTP Interface and External Systems"

The complex structure and specialized terminology of CMC research regulation documents require external systems to handle long text inputs and precise semantic understanding during API calls. Due to infrequent updates, real-time interface requirements are lower than for transactional systems. However, data consistency and version management requirements are extremely high, ensuring query results consistently reflect the latest or specified version of the regulations. Cross-references and chart content within documents mean that pure text extraction may miss critical information, necessitating consideration of multimodal processing or enhanced text parsing capabilities. The specificity of fields and precision of units require targeted data preprocessing and post-processing when constructing queries and parsing responses, to avoid misunderstandings or ambiguities. For example, the Quality Standards field may require parsing its internal parameters and their respective thresholds.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`3000 Tokens`	CMC regulation texts are often long, requiring a larger context window to capture complete semantic information.
`Chunk size` (Segment Length)	`800–1200 characters`	Balances text completeness with retrieval efficiency, avoiding over-fragmentation or information dilution from excessively long segments.
`Recall count` (Recall Count)	`Top 5 entries` (Top 5)	Regulation Q&A demands high accuracy; increasing recall count improves relevance hit rate.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Ensures recalled results are highly relevant to the query, filtering out vague or inaccurate matches.
`Rerank result count` (Reranked Return Count)	`3 entries` (3 items)	After reranking, the top items typically contain the most core and direct answers, reducing redundancy.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF or Word documents is time-consuming, requiring a longer timeout to prevent parsing interruptions.

Common Pitfalls

API calls return HTTP 504 Gateway Timeout errors: This usually occurs when FastGPT's backend takes too long to process long texts or complex queries, exceeding the default timeout settings of the gateway or proxy server.
Answers omit critical numerical or unit information: This happens when the text processing stage fails to recognize or correctly extract specific measurement units and values embedded in the document, leading to information loss.
Querying a specific SOP version returns outdated content: This often indicates that the external system did not correctly pass the version_id parameter during the API call, or that different document versions in the FastGPT knowledge base are ambiguously indexed.

Verification Steps

Call the HTTP interface with typical CMC regulation questions and verify that the SOP ID and Version Number in the returned results match expectations.
Select PDF documents containing complex tables and charts, upload them to the knowledge base, and test the parsing effect. Confirm that key information (e.g., Risk Assessment Level) is correctly extracted.
Conduct concurrent tests to simulate multiple users querying simultaneously. Observe whether interface response times are within acceptable limits and check for rate limiting errors such as HTTP 429 Too Many Requests.

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.