HTTP API and External Systems for CDMO Quality Documents

Data Characteristics in This Domain

CDMO (Contract Development and Manufacturing Organization) quality documents cover the entire lifecycle of a drug, from R&D to manufacturing. Data sources are diverse, including raw experimental records, batch production records, inspection reports, deviation handling, change control, and supplier qualification files. These documents are typically stored in formats like PDF, Word, and Excel. Some data may reside in specialized systems such as LIMS (Laboratory Information Management System) and MES (Manufacturing Execution System). Document update frequency is high, especially during R&D and clinical stages, where protocols, reports, and batch records are continuously generated and revised. Document structures are complex, containing numerous standardized fields (e.g., batch number, product name, specification, production date, expiry date, test results, acceptance criteria) and unstructured descriptions (e.g., deviation descriptions, investigation conclusions). Units involve quality (mg, g, kg), volume (mL, L), concentration (% w/v, ppm), and time (min, h, day), with extremely high precision requirements.

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

The high update frequency and complex structure of CDMO quality documents pose challenges for HTTP API design. APIs must support frequent document uploads, updates, and version management, and handle various file formats. Structured data from external systems like LIMS or MES requires APIs to accurately map fields and support bulk imports. The specialized terminology and strict compliance requirements in documents mean that data extraction and knowledge graph construction need targeted processing strategies to avoid semantic deviations. The strictness of time precision and numerical units requires APIs to maintain consistency during data transmission and parsing, preventing errors caused by format conversion. Furthermore, due to data sensitivity, secure authentication and access control for APIs are core constraints.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Accommodates large files like batch production records or complex reports that may contain numerous charts and attachments, ensuring smooth uploads.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles situations where parsing complex PDFs with tables or scanned images takes a long time.
`maxContext`	`8000 characters`	Ensures a single recall can cover critical context from lengthy documents such as quality deviation investigations and change controls.
`Similarity threshold`	`0.75–0.85`	Balances accurate recall with avoiding interference from irrelevant documents, preventing false positives due to similar technical terms.
`HTTP_REQUEST_TIMEOUT`	`120 seconds`	Accounts for potential delays in external LIMS or MES system API responses, allowing sufficient time for data synchronization.
`EXTERNAL_API_RETRY_COUNT`	`3 times`	Addresses occasional network fluctuations or temporary unavailability of external systems, improving data synchronization robustness.

Common Pitfalls

Calling external system APIs returns HTTP 401 Unauthorized or HTTP 403 Forbidden: This typically indicates incorrect or expired API_KEY or ACCESS_TOKEN configuration, or insufficient API permissions.
After document upload, important fields are empty or numerical units do not match in search results: The file parser did not correctly identify specific field names or unit formats in the document. Adjust parsing rules or use a custom extractor.
After data synchronization from an external system, the initial query response time is too long, or hybrid search performance is poor: Vector index rebuilding or update strategies are inadequate, failing to reflect the latest data in a timely manner, or hybrid search query optimization is insufficient.

Verification Steps

Upload a typical batch production record PDF via the FastGPT management interface. Check if its parsing results are complete and if key fields like batch number, production date, and test results are correctly extracted.
Configure the HTTP API with the LIMS system. Manually trigger a data synchronization. Verify that the imported inspection report data in FastGPT matches the original data in the LIMS system, especially numerical values and units.
Perform a search using a query that includes a specific quality deviation description. Verify that the system accurately recalls relevant deviation reports and assess the completeness and relevance of the recall results.
Simulate a brief external system API failure (e.g., by temporarily shutting down the external service or configuring a proxy delay). Observe if FastGPT's HTTP API retry mechanism works as expected and ultimately successfully synchronizes data.

The values provided are common starting points and should be measured against the reader's 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.