Data Characteristics
Public tendering data for clinical trials originates from government drug procurement platforms, medical institution websites, and third-party bidding information portals. This data updates frequently; new project tenders may appear weekly, while bid award announcements have a delay. Documents are primarily structured or semi-structured, often including PDF tender documents, announcement texts, and attached tables. Key fields include project name, sponsor, indication, trial phase, research center, budget amount, publication date, deadline, and winning bidder. Some documents use non-standard abbreviations or specific industry terms, such as "Phase I clinical" or "Phase IIa" for trial phases, and budget units like "million CNY" or "USD".
Constraints on Citation and Traceability
The high update frequency of public tendering data requires the knowledge base to quickly synchronize and index information, ensuring timely citation sources. The diverse structure of tender documents (mixed PDFs, tables, plain text) challenges document parsing. Accurate extraction of key information and preservation of its context is essential. Non-standardized terms and abbreviations, such as IND (Investigational New Drug) or CRO (Contract Research Organization), require the RAG model to possess domain-specific knowledge for accurate recall of relevant content. Additionally, numerical fields like budget amounts must retain their units in citations to prevent information distortion. These characteristics collectively dictate that citation and traceability must focus on both text similarity and ensuring information granularity and semantic accuracy.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 300–500 characters | Balances contextual completeness and retrieval efficiency; avoids diluting information with overly long paragraphs. |
Recall count (Recall Count) | 8 entries | Ensures coverage of multiple potentially relevant sources; balances retrieval cost. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Filters low-relevance content, reduces noise, and improves citation quality. |
Rerank result count (Rerank Return Count) | 5 entries | Selects the most relevant results, enhancing the precision of final cited content. |
maxContext | 4096 | Accommodates the potentially long nature of tender documents, ensuring sufficient context window. |
PARSE_FILE_TIMEOUT_SECONDS | 180 seconds | Addresses the time-consuming parsing of large PDF tender documents, preventing timeout failures. |
Common Pitfalls
- Dialogue includes irrelevant tender information citations. This can occur if the
Similarity threshold(Similarity Threshold) is set too low, leading to the recall of many broadly related items. - Cited source document page is empty or incomplete. This usually results from PDF file parsing failures or loss of critical fields during parsing.
- Model responses cite outdated tender information. This is indicated by citation sources with publication dates significantly earlier than the current time, due to the knowledge base not synchronizing the latest data promptly or indexing updates being delayed.
Verification Steps
- Test the model with 5 recently published tender documents of different types. Verify if it accurately cites the project name, sponsor, and deadline. Cross-check if the cited source's page number or paragraph matches the original text.
- Input queries containing industry-specific abbreviations (e.g., "CRO", "GCP"). Check if the model recalls and cites document snippets that include these abbreviations and are semantically relevant.
- Simulate a question about an expired tender project. Observe if the model identifies its timeliness or explicitly states its publication date in the citation, avoiding misleading information.
Note: The values provided are common starting points. Measure performance against your own samples and adjust as needed.
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.