Data Characteristics for Compliance Q&A
Compliance Q&A data primarily originates from pharmaceutical companies' internal compliance approval processes, industry regulatory policy documents, and clinical guidelines for specific disease areas. This data is predominantly in unstructured text formats, such as PDF approval documents, Word policy interpretations, and structured Excel tables containing drug instruction excerpts. Update frequency typically depends on policy release cycles or drug approval progress, potentially monthly, quarterly, or annually. Documents involve extensive specialized terminology, drug names, dosage units, and indication descriptions. Field content is highly specialized, and accuracy requirements are stringent. The data often includes legal citations and disclaimers in specific formats.
Constraints on Sharing and Embedding Due to Data Characteristics
The specialized and compliance-driven nature of compliance Q&A imposes multiple constraints on sharing and embedding functionalities. First, the uncertain data update frequency requires embedded content to have real-time synchronization capabilities to avoid compliance risks from outdated information. Second, the extensive specialized terminology and legal provisions in documents necessitate precise semantic understanding during consultation conversion in private domains to prevent misinterpretations or ambiguities. This means the embedded AI Agent must strictly adhere to the original document's context during retrieval and generation. Additionally, due to sensitive medical information and patient privacy, sharing and embedding channels require strict data isolation and access control mechanisms to ensure information security and compliance with relevant regulations. For example, embedding in mini-programs requires considering filing and security audits. Embedded content must also clearly identify information sources to enhance user trust.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
context_max_tokens | 4000 tokens | Ensures complete retrieval and understanding of long text passages and context within compliance Q&A. |
temperature | 0.1 | Reduces the randomness of model-generated content, ensuring the rigor and consistency of compliance Q&A output. |
top_p | 0.3 | Further limits the diversity of vocabulary generated by the model, focusing on high-probability words to avoid deviation from compliance topics. |
max_tokens | 800 tokens | Limits the length of a single response, preventing the generation of lengthy or unnecessary non-compliant content. |
retrieval_limit | Top 5 entries | Prioritizes retrieval of the most relevant compliance terms or descriptions to the user's query, reducing interference from irrelevant information. |
iframe_sandbox_permissions | allow-scripts allow-same-origin | Allows necessary interactive scripts to run while restricting potential cross-origin security risks, ensuring embedding security. |
Common Pitfalls
- After embedding into a mini-program, user questions receive no robot replies, but the debugging area functions normally. This typically results from security policy restrictions in the mini-program environment for
iframeorwebview, preventing the FastGPT embedded page script from executing correctly and establishing communication with the FastGPT backend. - Shared URL links fail to pass global variables via URL parameters. This can occur if the FastGPT embedded page script does not correctly parse or process query parameters in the URL, preventing global variables from being successfully injected into the session context.
- Shared compliance Q&A content displays with layout issues on different devices. This stems from the embedded page's CSS styles or responsive design not adapting to all target devices (e.g., mobile phones, tablets, desktops), leading to content overflow or layout misalignment.
Verifying Configuration
- Test in the target private domain channel (e.g., mini-program, WeChat Work) with different identities (regular user, administrator) to verify smooth Q&A flow for compliance Q&A and check that each reply can be traced to a clear knowledge source.
- Simulate edge cases or questions containing sensitive words to observe whether the AI Agent's responses strictly adhere to compliance requirements, avoiding vague, speculative, or inappropriate expressions, and compare against original compliance documents.
- Check the network request logs of the embedded page to confirm successful API calls to the FastGPT backend, acceptable response times, and encrypted data transmission.
- Test the loading speed and interaction fluidity of the embedded page in different network environments (Wi-Fi, mobile data) to ensure a consistent user experience and verify proper data synchronization mechanisms.
The values given 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.