Data Characteristics for This Category
Lead synchronization data in the biopharmaceutical sector originates from pharmaceutical company marketing activities, medical conferences, online questionnaires, and CRM systems. Data updates typically occur daily or weekly in batches to ensure timely consultation. Lead information primarily consists of structured data. This includes basic patient or physician information, consultation intent, disease status, and medication history. Examples of fields are patient_id, consultation_intent, and disease_history. Units may involve diagnostic codes (e.g., ICD-10) for disease history and medication dosages (e.g., mg, ml). Consultation times are precise, down to yyyy-MM-dd HH:mm:ss.
Constraints Imposed by These Characteristics on Sharing and Embedding
The structured nature of lead data requires accurate field mapping and completeness during sharing and embedding to prevent information loss. The update frequency demands that shared content reflects the latest lead status promptly, especially for high-value leads. Sensitive personal information (e.g., disease history, medication status) within the data necessitates high standards for data security and compliance. Sharing links must have strict access controls. Since lead data often integrates with internal business systems, the embedding function must support flexible parameter passing. This allows identification and association with specific leads or users in external applications, for example, by using the user_id parameter to identify the consultant.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
share_mode | private_access | Ensures lead data is accessible only to authorized users, complying with data privacy regulations. |
max_context_tokens | 4096 | Covers typical context lengths in lead consultation scenarios, reducing information truncation. |
data_refresh_interval | 3600 seconds | Matches the daily update frequency of lead data, ensuring information timeliness. |
allowed_domains | *.yourcompany.com | Restricts the origin of embedded pages, preventing unauthorized website embedding and enhancing security. |
user_id_param_name | mcp_user_id | Standardizes user identification across external systems, facilitating lead attribution and conversion tracking. |
session_timeout | 1800 seconds | Balances user experience and data security, preventing information leakage from long inactive sessions. |
Common Pitfalls
- The "cite" or "view original" functions fail when accessing shared links. This typically occurs due to overly strict permission controls for unauthenticated access in the backend configuration, or the frontend page failing to correctly parse permission fields returned by the backend.
- The embedded page cannot obtain the user's
user_id. This often happens because the external application does not correctly pass the user identification parameter to FastGPT when calling the embedded link, or FastGPT is not configured to recognize this parameter. - Lead data in shared content does not update promptly. This is usually due to delays in the data synchronization mechanism, or the knowledge base's caching strategy not aligning with the lead data's update frequency.
Verification Steps
- Access the shared link using an unauthenticated browser session. Check if the content loads correctly and verify if the "cite" and "view original" functions are available.
- When integrating with MCP Server or other external systems, use system logs or debugging tools. Check if the embedded page's URL carries the correct
user_idparameter and observe if the FastGPT backend correctly recognizes it. - After simulating a lead data update, immediately access the relevant content via the shared link. Verify if the displayed lead information matches the latest data and check if the refresh interval meets expectations.
- Attempt to embed the FastGPT application from a domain not listed in
allowed_domains. Confirm that the embedding request is correctly rejected.
The values provided are common starting points and should be measured against the reader's 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.