Data Characteristics for This Category
Academic promotion quality documentation typically includes clinical study reports, drug package inserts, medical guidelines, expert consensuses, presentations (PPTs), and training materials. Data sources are diverse, covering internal pharmaceutical company departments like R&D, medical affairs, and market access teams, as well as external clinical research organizations and professional medical societies. Document update frequencies vary; for instance, drug package inserts might update annually, while clinical study reports are published once after a trial concludes. Document structures are highly specialized, containing extensive medical terminology, dosage units (e.g., mg/kg, IU), statistical indicators (e.g., p-value, CI), and charts. Fields commonly include study design, subject characteristics, trial results, adverse events, indications, contraindications, and dosage and administration.
Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"
The specialized nature and update frequency of academic promotion documents impose specific requirements on HTTP interfaces and external systems. Due to the large volume of specialized terminology and complex structures within documents, FastGPT's file parsing capabilities require high optimization to accurately identify and extract key information. For example, correct parsing of dosage units and statistical indicators directly impacts the accuracy of RAG (Retrieval Augmented Generation). Inconsistent update frequencies necessitate flexible synchronization mechanisms in external systems, capable of configuring differentiated fetching strategies for various document types. For instance, for infrequently updated clinical study reports, periodic full synchronization can be used; for frequently updated medical news or guideline interpretations, incremental updates or event-driven synchronization are required. Additionally, these documents often exist in PDF, DOCX, or PPTX formats. Interfaces must support uploading and processing these file types and be able to recognize chart and table content within them.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Academic promotion documents, especially PPTs or PDFs with charts, can be large. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Complex document parsing takes time; this prevents parsing failures due to timeouts. |
Segment Length | 800–1200 characters | Maintains semantic integrity while balancing RAG recall efficiency. |
Recall Count | Top 5 | Ensures breadth of retrieval results, covering potentially relevant information. |
Similarity Threshold | 0.75 | Balances recall accuracy and coverage, avoiding excessive irrelevant results. |
CHUNK_OVERLAP_SIZE | 100 characters | Ensures context continuity between segments, handling semantic dependencies across paragraphs. |
Common Pitfalls
- Symptom: When external systems synchronize documents, some PDF or PPT files have missing content after parsing, especially text within tables or images. Reason: The file parser is not optimized for these complex structures and fails to correctly extract embedded text information from charts.
- Symptom: The HTTP interface returns a
403 Forbiddenerror, preventing access to externally stored documents. Reason: Access credentials or permission configurations for the external storage system (e.g., S3-compatible storage) are incorrect, preventing FastGPT from retrieving files. - Symptom: After a user asks a question, the AI's answer contains information inconsistent with the latest medical guidelines. Reason: The external system's document synchronization mechanism is improperly configured, failing to timely pull and update the latest medical guideline documents.
Verification Steps
- Upload a PDF document containing complex tables and charts. Check if the segmented content of that document in the knowledge base is complete, especially verifying that text information within tables and charts is correctly recognized.
- Configure an HTTP interface to connect to external storage. Attempt to synchronize a large PPT file, observe the synchronization status and logs, and confirm that the file upload and parsing process are error-free.
- For documents with different update frequencies in the knowledge base (e.g., drug package inserts and the latest clinical study abstracts), manually trigger or wait for the automatic synchronization cycle. Check if the document version number and content match the external source.
- Test with complex questions containing specialized medical terminology, such as asking about a drug's dosage unit or the
p-valueof a specific clinical trial. Evaluate the accuracy of the AI's answer and the correctness of its cited sources to verify RAG effectiveness.
Note: The values given 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.