What Data Looks Like for This Category
Data for this category primarily comes from financial systems, project management systems, contract ledgers, and business department requirements. Regularly produced core documents include annual budget sheets, project proposal documents, project portfolio dashboards, and investment review reports. Fields include budget amount, project cycle, priority tags, revenue forecasts, and similar items. Common units are ten thousand yuan, calendar days, and quarters. Data updates follow monthly, quarterly, or annual cycles. The volume of documents involved in a single update varies based on the enterprise's business scale.
What Constraints These Characteristics Impose on Reranking and Hybrid Retrieval
Document structures in this category vary widely. Some documents are plain-text project proposals, while others include structured tables such as budget sheets and portfolio dashboards. Hybrid retrieval must support matching logic for both structured fields and unstructured text. Data updates occur infrequently, so the recall range for single retrieval does not need frequent adjustment. However, retrieval must accurately link core business fields such as budget amount and project priority. The reranking step must adjust weights for different document types. This avoids placing low-value, low-priority documents at the top of results. It also balances the number of retrieval results with the information density needed for business decision-making. Returned content must directly support budget priority sorting and project review.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Similarity threshold | 0.65–0.75 | Adapts to the semantic matching accuracy of budget and project fields for this category, filters low-relevance recall results |
| Single recall limit | Top 10–15 results | Balances information breadth and response speed, adapts to document volumes updated on a monthly/quarterly basis |
| Search mode | hybrid | Covers both structured fields and non-semantic text, compatible with multiple document types such as budget sheets and project proposals |
| Enable reranking | Enabled | Corrects the initial recall sorting logic, highlights high-priority projects and strongly relevant budget documents |
| Reranking model threshold | 0.7–0.8 | Filters low-relevance results after reranking, ensures returned results meet the information density requirements for business decision-making |
| Reranking recall count | Top 5–8 results | Focuses on core business information, avoids excessive redundant content interfering with priority judgments for budgets and projects |
Three Common Mistakes
- Symptom: The reranking model functions normally during testing, but reranking results do not take effect during official retrieval. Troubleshooting: The reranking model was only deployed in the model channel, and the reranking switch was not enabled in the application's retrieval configuration.
- Symptom: Only the reranking model can be called, and other language models cannot be used for Q&A. Troubleshooting: The required environment variables for AI Proxy were not correctly configured: AIPROXYAPIENDPOINT (the root address of the AI Proxy service, do not append paths such as /v1 or /chat/completions) and AIPROXYAPITOKEN (the AI Proxy administrator token, consistent with the AI Proxy server configuration and not empty), or the vendor credentials for the corresponding language model were not configured in the model channel.
- Symptom: Retrieval results include many general documents unrelated to budgets and projects. Troubleshooting: The similarity threshold was set too low, failing to filter low-relevance non-business recall results.
How to Confirm Configuration Is Complete
- Access the application's retrieval test page, enter a query that includes budget amount and project priority, compare initial recall results with reranked results, confirm the reranking logic operates correctly.
- Review the server environment variables for the application deployment, confirm AIPROXYAPIENDPOINT (the root address of the AI Proxy service, for example http://aiproxy:3000, do not append paths such as /v1 or /chat/completions) and AIPROXYAPITOKEN (the AI Proxy administrator token, consistent with the AI Proxy server configuration and not empty) are properly configured. Ensure relevant parameters are not filled in workflow global variables, application API request headers, or the general model management page. This configuration applies to FastGPT v4.17.0 and later versions.
- Switch the search mode to pure semantic retrieval, compare recall results with hybrid mode, confirm structured field matching functions normally.
- View the application's running logs, confirm there are no errors in reranking model call requests and responses, and that returned results match expected quantity and sorting logic.
The values provided here 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 2026-10-19.