Data Characteristics
Monoclonal antibody regulations and Standard Operating Procedures (SOPs) typically exist as PDFs, Word documents, or structured text. These documents originate from regulatory bodies (e.g., FDA, EMA), industry association guidelines, and internal quality management systems of pharmaceutical companies. Data updates are infrequent, usually occurring with regulatory revisions or the introduction of new manufacturing processes, with cycles ranging from months to years. Document structures are highly standardized, including sections like introduction, purpose, scope, definitions, responsibilities, operating procedures, records, and appendices. Fields often include batch number, manufacturing date, expiry date, storage conditions, purity indicators, detection methods, and quality standards. Units frequently involve biomedical specific measurements such as μg/mL, IU/mg, and %, often accompanied by specific detection method abbreviations (e.g., ELISA, HPLC).
Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"
The low update frequency of monoclonal antibody regulation documents means data synchronization does not need to be overly frequent. However, each synchronization must ensure completeness and version control. The complex document structure and specialized fields require the HTTP interface to achieve high precision during data extraction, especially for recognizing professional terminology and units of measurement. For example, a subtle semantic difference may exist between purity indicators >95% and ≥95%, requiring precise parsing. Importing numerous PDF documents challenges the file parsing capabilities of external systems, which must support various PDF versions and embedded chart recognition. Furthermore, common cross-references and appendix links in regulatory documents require the interface to handle document interrelationships to provide context during Q&A. Queries for fields like batch number and expiry date require external systems to support structured queries and fuzzy matching.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 50 MB | A single SOP or regulatory document typically does not exceed this size, ensuring large file uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Complex PDF document parsing takes longer; this provides sufficient time to avoid timeouts. |
chunkSize | 800–1200 characters | Balances semantic completeness and search efficiency, preventing the splitting of critical information blocks. |
overlapSize | 100 characters | Ensures contextual continuity between adjacent text blocks, especially for cross-paragraph queries. |
similarityThreshold | 0.75 | Improves matching accuracy, filtering out results with low relevance to biomedical professional terms. |
maxContext | 3000 Tokens | Accommodates detailed steps or multi-condition judgments common in regulatory Q&A, providing sufficient context. |
Common Pitfalls
- An external system calling the FastGPT interface receives an
HTTP 400 Bad Requesterror. This typically occurs because themodelIdormessagesfields in the request body do not conform to the expected format, preventing FastGPT from parsing them correctly. - A knowledge base search node is configured in a workflow, but the
knowledgeBaseIdvariable passed via the API is not referenced correctly. This results in empty search results, usually due to a mismatch between the variable name and the actual configured receiving parameter. - A user asks "What are the storage conditions for antibody batch number X?", but the returned result fails to accurately extract the storage conditions, instead providing general information. This may be because file parsing did not effectively associate "batch number" with "storage conditions," or the knowledge base chunking granularity was too large, diluting the information.
Verification Steps
- Upload a monoclonal antibody SOP PDF file containing complex tables and specialized terminology. Verify that the file is successfully parsed into the knowledge base and that specific data within tables, such as "purity detection method" or "batch retention sample volume," can be retrieved via keyword search.
- Use FastGPT's API interface to perform a multi-conditional query (e.g., "ELISA detection standard for batch number
ABC123, with an expiry date after2025"). Verify that the system accurately returns the relevant regulatory clauses and that the returned JSON structure includes the requested fields. - Within the FastGPT interface, ask a complex question about the monoclonal antibody production process, such as "How to handle cell contamination during monoclonal antibody production, and in which document is it recorded?". Observe whether the response includes specific SOP numbers and operating steps, and whether the cited knowledge blocks accurately point to relevant regulatory sections.
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.