What This Category of Data Looks Like
Data sources for internal office and employee services of urban investment platforms include official documents from internal OA systems, project ledgers, attendance reports, employee handbooks, meeting minutes, and project site photos. Update rhythms vary significantly by data type. Official documents are updated per issuance cycle. Project ledgers are updated in real time with project progress. Employee handbooks are updated 1 to 2 times per year. Meeting minutes are generated per individual meeting. Document structures include structured fields such as document number, issuing department, date, and main text. They also include business fields such as project number, budget amount (unit: ten thousand yuan), and employee ID number. Some documents include image attachments.
What Constraints These Characteristics Impose on Vector Models and Indexes
Mixed multi-type data exists, including structured reports, unstructured text, and image attachments. This requires vector models to support multimodal embedding to adapt to different data formats. Differing update rhythms require index strategies to balance real-time incremental refresh and scheduled full refresh. This avoids resource waste or data lag. Structured business fields such as department and project number must be associated with vector indexes. This enables precise metadata filtering, improving the targeting of internal office queries. Long documents such as project feasibility study reports have large semantic unit spans. Chunking rules must be adjusted appropriately to avoid semantic fragmentation.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic unit length of internal official documents and project reports for urban investment platforms, avoiding cross-semantic chunking |
| Overlap Length | 100–150 characters | Retains cross-chunk context association, adapting to semantic coherence of long documents |
| Similarity Threshold | 0.72–0.85 | Matches the semantic similarity distribution of internal office data for urban investment platforms, filtering low-relevance results |
| Single Search Limit | Top 20 entries | Matches the result volume requirements of internal office queries, avoiding interference from redundant information |
maxToken | 8000 | Adapts to the default context limit of FastGPT v4.17.0, meeting long document splicing requirements |
defaultToken | 1024 | Adapts to embedding requirements for short text such as attendance reports |
Three Common Misconfigurations
- Phenomenon: When identical employee attendance or project query requests are submitted repeatedly, the system repeatedly triggers vector database retrieval, and multiple identical embedding requests appear in logs. Cause: No query caching mechanism is configured, no cache validity period or hit rules are set, so full vector retrieval triggers for each request.
- Phenomenon: After calling the "Create Training Order" interface, the knowledge base does not generate corresponding index entries, and the interface returns an empty order status field. Cause: The correct knowledge base collection is not bound before creating the order, or structured office data in the correct format is not passed.
- Phenomenon: After uploading an office document containing project site photos, search results only return text content with no associated image results. Cause: Multimodal vector embedding configuration is not enabled, and image files are not bound to text content in the same index unit.
How to Confirm Proper Configuration
- Initiate two identical internal office queries, check the number of occurrences in vector retrieval logs to confirm the cache configuration is active.
- Upload a test document containing structured reports and images, check the index generation status to confirm multimodal configuration and metadata binding work correctly.
- Call the "Create Training Order" interface and query the order status to confirm the knowledge base collection associated with the order matches the data format.
- Adjust the similarity threshold and initiate a query, check the number of returned results to confirm the threshold configuration is active.
The values provided here are common starting points, and should be evaluated 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.