Data Characteristics
Market access data in the biopharmaceutical sector originates from regulatory bodies, healthcare insurance institutions, and industry associations across various countries and regions. This includes regulatory documents, guidelines, approval results, and reimbursement catalogs. Data typically exists as unstructured text in formats like PDF for regulations, Word for application guidelines, and Excel for pricing lists. Update frequencies vary; regulations may change several times a year, while reimbursement catalogs might update quarterly or annually. Documents are structurally complex, containing legal terminology, technical parameters, and clinical data references. Common fields include drug generic name, brand name, dosage form, specifications, indications, reimbursement codes, payment standards, approval numbers, and effective dates. Units involve dosage (mg, IU), packaging (boxes, vials), and currency (CNY, USD).
Constraints on Forms and Interactions
The complexity and update frequency of market access data impose specific requirements on form and interaction design. The unstructured nature of regulations and guidelines limits the effectiveness of direct keyword matching. This necessitates intelligent semantic understanding and information extraction capabilities. Form designs must support complex natural language queries, enabling the model to locate relevant clauses within extensive legal texts. Unpredictable update frequencies demand version management and incremental update capabilities to ensure users access the latest data. This directly impacts knowledge base synchronization mechanisms and index reconstruction strategies. The diversity of fields and units requires an interface that clearly displays field meanings and relationships, offering unit conversion or standardization where necessary to prevent misinterpretations due to unit confusion. Additionally, critical information like approval numbers often appears in specific formats, requiring form support for regex-based input validation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Regulatory text paragraphs are long. Preserving context integrity aids semantic understanding and reduces information loss from splitting. |
overlapSize | 100 characters | Ensures sufficient overlap between adjacent text blocks, preventing critical information from being truncated at split boundaries. |
maxContext | 8192 tokens | The complexity of market access questions requires the model to process long contexts, accommodating key information from multiple regulations or guidelines. |
recallTopK | Top 5 | Recalls more potentially relevant documents, increasing the probability of hitting key information, especially when regulatory clauses are ambiguous. |
similarityThreshold | 0.75–0.85 | Filters out document segments with low relevance to the query topic while maintaining recall rate, improving result precision. |
reRankTopN | Top 3 | Presents the most relevant document segments to the user after re-ranking, improving efficiency in information retrieval. |
Common Pitfalls
- A user's initial query fails to yield expected results, but a slightly modified or re-entered query succeeds. This often results from a
similarityThresholdset too high, leading to overly strict recall, or achunkSizethat is too small, causing text splitting to break key semantics. - The AI model selection interface in a workflow shows no available models, but the workflow runs correctly after deployment. This may indicate an issue with model interface or permission configurations in the deployment environment, preventing the frontend from correctly loading the list of available models, while the backend service can successfully call models via internal configurations.
- Attempts to install
ffmpegorpipdependencies within the FastGPT container fail, preventing speech input functionality from working. This typically occurs due to a lack ofrootprivileges or missing package management tools within the container, preventing system-level installations and leading to the absence of external components required for speech-to-text.
Validation Steps
- Test various combinations of
recallTopKandsimilarityThresholdagainst typical market access queries. Observe the quantity and relevance of recalled documents to ensure comprehensive and precise results. - Import a recently updated regulatory document into the knowledge base and immediately run queries. Verify that the system accurately returns the latest version of clauses, confirming the effectiveness of the knowledge base update mechanism.
- Construct queries containing specific approval numbers or payment codes. Check if the system can accurately extract and display this structured information from unstructured text, confirming the accuracy of information extraction capabilities.
- Use queries containing complex legal terminology. Evaluate the model's understanding of these terms and its ability to locate and integrate information within long text contexts, ensuring the depth of semantic understanding.
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.