Data Characteristics in This Category
Market access clinical trial prescreening data originates from global clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), regulatory guidance documents, drug labels, medical journal articles, and internal pharmaceutical market analysis reports. Data update frequencies vary; clinical trial registration information may update weekly, while regulatory guidelines or drug labels have longer update cycles. Document structures are diverse, including unstructured plain text reports, semi-structured clinical trial protocol PDFs, and structured XML or JSON data with specific fields. Key fields include disease area, drug target, inclusion/exclusion criteria, trial phase, primary endpoint, secondary endpoint, geographic region, and market access policy terms for different countries or regions. Units involve common medical and statistical measures such as dosage (mg, g), time (weeks, months, years), and percentages (%).
Constraints Imposed by These Characteristics on "Knowledge Base Retrieval and Recall"
The wide range and heterogeneity of data sources require the knowledge base to have robust multi-format file parsing capabilities, especially for complex documents like PDFs and XML. Varying update frequencies mean the knowledge base must support incremental updates and regular full synchronization to ensure information timeliness. Diverse document structures pose challenges for chunking strategies, which must balance semantic completeness with avoiding excessive length that reduces recall efficiency. The richness and specialization of key fields mean traditional keyword matching may not capture deep semantic relationships, necessitating more sophisticated vector embedding models. The strong correlation of geographic regions and policy terms requires effective multi-dimensional filtering and precise matching during retrieval, such as filtering specific policies by country or region. Accurate identification of medical units and specialized terminology demands high performance from tokenizers and entity recognition models to prevent recall bias due to misidentification.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Clinical trial documents often contain lengthy descriptive text. This length balances semantic completeness with recall efficiency. |
Chunk Overlap Length | 100 characters | Ensures contextual continuity and prevents critical information loss due to chunk truncation. |
Recall count | 8–12 entries | Market access decisions are complex, requiring multi-faceted information. Increasing recall count improves coverage. |
Similarity threshold | 0.75–0.85 | Clinical information demands high precision. A threshold that is too low may introduce irrelevant information, while one that is too high may miss critical details. |
Rerank result count | 5 entries | Re-ranks retrieved results to select the most relevant entries, improving final presentation quality. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large clinical trial protocols or regulatory documents can take a long time. This prevents parsing failures due to timeouts. |
Three Common Mistakes
- Knowledge base upload of HTML interface documents fails to parse, resulting in empty content. This often occurs when HTML files have complex structures, including extensive scripts or styles, preventing the default parser from effectively extracting plain text.
- Search results contain numerous irrelevant or outdated policy details. This happens when the knowledge base lacks effective data deduplication or has inadequate incremental update mechanisms, leading to a mix of old and new data.
- When using the API with the FastGPT knowledge base search node, the system cannot optimize queries based on historical conversations or resolve anaphora. This indicates that the complete conversation history was not provided as context during the API call, preventing the model from understanding referential relationships in user intent.
How to Confirm Correct Configuration
- Upload various formats (PDF, XML, HTML) of clinical trial documents. Check if the knowledge base successfully parses and extracts core text content, and if fields like
疾病领域and入排标准are correctly identified. - Perform searches for specific market access policies or drug targets. Verify that the retrieved results include the latest guidance documents from authoritative sources and observe if the number of recalled items matches expectations.
- Construct queries involving anaphora or context dependency. Invoke the knowledge base retrieval function via API or workflow. Validate if the system can accurately understand query intent by leveraging historical conversations and provide relevant results. For example, check the
queryfield in logs to see if it contains the expanded, complete query.
The values provided are common starting points and should be measured against specific 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.