Data Characteristics for This Category
Bidding and listing data for clinical trials primarily originates from government drug procurement platforms, medical institution official websites, and third-party bidding information release platforms. This data updates frequently, often hourly or daily, with new bidding announcements or award results. Document structures are predominantly unstructured text, containing extensive information such as project names, sponsors, CRO companies, research centers, experimental drugs, indications, inclusion/exclusion criteria, and budget amounts. Fields are mixed: some are clearly structured, like project number and publication date, while many are descriptive unstructured text, such as project background and technical requirements. Units vary, including monetary values (CNY, USD), time (days, months, years), and quantities (cases, batches), often embedded within descriptive text.
Constraints Imposed by These Characteristics on Vector Models and Indexing
High update frequency requires the vector index to support efficient incremental updates and real-time querying. This prevents data staleness from impacting pre-screening accuracy. The high proportion of unstructured text necessitates robust text segmentation strategies. These strategies must ensure critical information is not truncated and avoid redundant context. Mixed field structures demand vector models capable of effectively processing different information types, for example, by vectorizing structured data alongside unstructured descriptions. Diverse units and embedded text require vector models to possess semantic understanding. This enables them to identify and differentiate numerical information, preventing semantic deviations caused by unit differences. For instance, 100Million CNY (1 million CNY) and 100Case (100 cases) must have distinct representations in the vector space.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 512 characters (512 characters) | Balances text semantic completeness with vector model processing efficiency. Avoids noise from overly long segments. |
Chunk Overlap Length (Segment Overlap Length) | 64 characters (64 characters) | Ensures contextual continuity between segments. Reduces semantic fragmentation caused by splitting. |
Recall count (Recall Count) | 10 entries (10 items) | Improves the comprehensiveness of initial recall. Covers more potentially relevant bidding information. |
Similarity threshold (Similarity Threshold) | 0.75 | Filters out low-relevance results. Reduces noise interference and focuses on highly matched information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (600 seconds) | Handles parsing demands of complex or large bidding documents. Prevents parsing timeouts that lead to indexing failures. |
Embedding Dimension | Calibrate by actual measurement (Calibrated by actual measurement) | Must align with the actual vector model used, such as bge-large-zh-1.5 or a compatible model, to ensure vector space matching. |
Three Common Mistakes
- Symptom: Some bidding announcements remain in an "indexing" state and cannot be vectorized. Reason: The file content is too large or contains extensive non-textual information (e.g., images). This causes
PARSE_FILE_TIMEOUT_SECONDSto be set too short, preventing the parser from completing processing within the allotted time. - Symptom: Pre-screening results recall bidding information with low relevance to the query intent, or even irrelevant information. Reason: An inappropriate vector model was selected. For example, using a general-domain model for highly specialized biomedical text leads to semantic understanding deviations. Alternatively, the
Similarity threshold(similarity threshold) is set too low, failing to effectively filter low-quality matches. - Symptom: After changing the vector model, existing vector data cannot be used normally, or query performance significantly declines. Reason: The new and old vector models have different
embedding dimensionsor training corpora. This results in incompatible vector spaces, causing semantic matching to fail when using old vector data directly.
How to Confirm Correct Configuration
- Select representative bidding announcement samples, including various complex scenarios. Process them through the system for vectorization. Verify that the indexing status completes normally, without timeouts or error messages.
- Execute multiple pre-screening queries for key keywords or phrases. Manually evaluate if the
Recall count(recall count) of the returned results meets expectations and if the top results are highly relevant. - Compare pre-screening results across different
Similarity threshold(similarity thresholds). Observe trends in result quantity and quality to determine a reasonable threshold that balances recall and accuracy.
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.