What Data Looks Like for This Category
Data for internal office and employee services in government and enterprise IT service scenarios mainly comes from internal OA systems, employee handbooks, departmental policy documents, IT operation and maintenance guides, welfare notifications, and other office outputs.
Updates trigger on-demand when internal policies are revised, employee information changes, or office workflows are optimized. Standard update cycles run daily or weekly.
Document structures include fields such as title, body, publishing department, effective date, and applicable job scope. Some documents include structured metadata, such as associated business system numbers and approval workflow node identifiers.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
Documents have mixed structured metadata and unstructured bodies. This requires retrieval to support both semantic recall and metadata filtering. The goal is to accurately match content with clear applicable scopes.
High-frequency on-demand updates require support for incremental index updates. This avoids resource consumption from full reindexing.
Document lengths vary widely. Ranges span short welfare notifications to long policy specifications. A chunking strategy that adapts to different text lengths enables balanced recall.
Internal documents use formal writing styles. The retrieval model must filter colloquial irrelevant content. This ensures the professionalism of recall results.
How to Configure Settings
This configuration uses FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Adapts to the formal writing style of government and enterprise documents. Filters low-relevance colloquial content. Calibrated based on actual testing. |
limit | Top 10 entries | Balances recall coverage and context length limits. Adapts to the average single-item length of internal office documents. |
searchMode | hybrid | Combines semantic matching and keyword matching. Adapts to the mixed structured and unstructured structure of government and enterprise documents. |
usingReRank | Enabled | Filters redundant content from preliminary recall results. Improves the ranking priority of highly relevant documents. |
datasetSearchUsingExtensionQuery | Enabled | Optimizes vague wording in employee questions. Matches professional terms and abbreviations used in internal documents. |
collectionIds | Knowledge base collections bound to business departments | Limits the recall scope. Avoids recalling irrelevant cross-department documents. |
Three Common Mistakes
- Symptom: After calling the knowledge base index generation interface, recall results differ significantly from those before replacing the embedding model. Cause: Only the model was replaced. No reindex operation was triggered for imported documents. Original vectors were not updated synchronously.
- Symptom: When using PostgreSQL to store vectors, the number of single recall results is lower than expected. Queries may also time out. Cause: The sharding strategy of the vector index was not optimized for the mixed structure of government and enterprise documents. Retrieval scope was not limited by department metadata. This results in an overly large scan range.
- Symptom: A format error prompt pops up when importing data to the knowledge base. Index creation cannot be completed. Cause: Imported documents do not include required metadata fields such as publishing department and applicable scope. Document formats may also not meet the platform's required specifications.
How to Confirm the Configuration Is Correct
- Submit a test query containing specific department keywords. Check whether recall results only include documents from the corresponding business department.
- View the retrieval log. Confirm that similarity scores of recall results fall within the preset
similarityrange. - Trigger an incremental sync task. Verify that only updated documents are reindexed. No full reindexing process is triggered.
- After enabling the
usingReRankfunction, compare recall result rankings before and after enabling. Confirm that the position of highly relevant documents has been optimized.
The values given 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 2026-09-28.