Data Characteristics
Phase II-III clinical trial documents originate from clinical trial protocols, investigator brochures, informed consent forms, ethics approvals, subject screening logs, and case report forms. Sponsors, Contract Research Organizations (CROs), or research centers typically generate these documents. Update frequency varies with trial phases and protocol revisions; documents are usually finalized before trial initiation and undergo minor revisions as needed during the trial.
Document structures are complex, often containing numerous tables, nested lists, charts, and unstructured text. Fields and units are highly specialized, including dosage units (mg/kg, IU), time points (weeks, months, follow-up periods), and biomarkers (pg/mL, ng/dL). Abbreviations and industry-specific terminology are common. Documents typically range from tens to hundreds of pages, primarily in PDF format.
Constraints on Document Parsing and Chunking
The complex structure and specialized terminology of Phase II-III clinical trial documents demand advanced parsing capabilities. Parsers must accurately identify the boundaries and content of embedded tables and charts, then structure this information. Specialized terminology and abbreviations require parsed text to retain contextual semantics, preventing critical information loss due to improper chunking.
Long documents can lead to extended single-parse processing times, challenging system performance and stability. The sensitive nature of document content necessitates strict data security and privacy protocols during parsing and storage. Accurate identification of fields and units directly impacts the correctness of subsequent pre-screening logic; any parsing error can lead to misjudged screening criteria.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Retains sufficient context while preventing excessively long chunks that reduce recall efficiency. |
Chunk Overlap Length (Chunk Overlap Length) | 100–200 characters | Ensures semantic continuity at chunk boundaries and connects key information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing requirements for large PDF documents, preventing timeout interruptions. |
maxContext | 32000 | Covers the critical information density found in typical clinical trial protocols. |
Similarity threshold (Similarity Threshold) | Calibrate by actual measurement | Balances recall and precision, avoiding omission of key screening criteria or introduction of irrelevant information. |
Recall count (Number of Retrieved Chunks) | Top 5-8 | Covers multiple potentially relevant screening criteria while managing processing load. |
Common Pitfalls
- Long response times or parsing failures after file upload. The
PARSE_FILE_TIMEOUT_SECONDSparameter is set too low. Parsing hundreds of pages in a PDF document can exceed the default value. - Model inability to invoke the file parsing tool during a conversation. The file parsing tool's name or description in the model tool configuration does not match its registered name, preventing the model from correctly identifying and calling it.
- Table data loss or format corruption in parsing results. The document parser inadequately supports complex table structures, failing to correctly extract table boundaries and cell content.
Configuration Verification
- Upload a typical Phase II-III clinical trial protocol PDF document. Verify that the parsing status shows success and no timeout errors occur.
- Randomly select parsed text chunks. Check for complete specialized terminology, dosage units, and time point information, ensuring semantic coherence.
- For pages containing complex tables, verify that parsing results accurately present table content, including row/column relationships and data integrity.
- Use FastGPT's debugging interface to observe whether the model correctly references parsed document chunks when processing relevant queries, and if the recalled chunk content is highly relevant to the query intent.
The values provided are common starting points. Measure them against your 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.