What data for this category looks like
Data for internal office and employee services in government and enterprise informatization services primarily comes from internal OA systems, employee service tickets, meeting minutes, policy manuals, and training materials. Data update frequencies cover three categories: real-time (such as service tickets submitted by users), daily (such as attendance and approval data), and periodic (such as monthly updated policy documents). Most individual documents are a mix of structured and semi-structured content, with fields including title, body, publishing department, effective date, and applicable hierarchy. Field units are mostly character count, entry count, and timestamp format.
What constraints do these characteristics impose on the vector model and indexing workflow
High-frequency new real-time service tickets require indexes to support incremental updates and low-latency synchronization, to avoid resource consumption from full index reconstruction. Semi-structured fields such as applicable hierarchy and publishing department must be separately included as metadata in indexing rules for subsequent permission filtering and precise recall. Document length varies widely, from tens of characters for service tickets to tens of thousands of characters for policy manuals. This requires chunking strategies with adaptive adjustment capabilities, to avoid semantic breaks from overly short chunks or exceeding model context limits from overly long chunks. Format differences across multiple data sources require unified preprocessing workflows to ensure consistency in vector generation.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to semantic integrity of internal government and enterprise documents, avoids losing context with short chunks, and controls per-chunk token count to stay within model limits |
| Overlap Length | 100–150 characters | Compensates for semantic breaks at chunk boundaries, ensures coherence across chunked content |
| Similarity Threshold | 0.72–0.85 | Adapts to semantic similarity distribution of internal office data, filters low-relevance recall results |
| Single Search Limit | Top 8 entries | Balances recall accuracy and context window usage |
maxToken | 4096 token | Matches input limits of mainstream vector models, avoids truncation that loses critical information |
defaultToken | 1536 dimensions | Adapts to output dimensions of general-purpose vector models, ensures consistency across index storage and recall |
Three common mistakes
- Phenomenon: Retrieval results are empty or irrelevant to the query after custom index configuration. Cause: Metadata for internal office data (such as applicable hierarchy) was not included in index filtering rules, leading to recall scope exceeding user permissions or business scope.
- Phenomenon: Unable to generate valid vectors after configuring a commercial vector model. Cause: The model-specific API key and region parameters were not correctly filled in the channel configuration, leading to model call failure.
- Phenomenon: Unable to recall existing data after connecting to a third-party vector database. Cause: The field format of internal data was not unified to the input structure required by the vector model, leading to vector generation or matching failure.
How to confirm the configuration is correct
- Upload a single typical internal document (such as a complete policy manual), check that chunked fragments fully cover core semantics with no obvious semantic breaks.
- Submit a query targeting internal employee services, verify that retrieved result metadata includes correct applicable hierarchy and publishing department, and aligns with business scope requirements.
- View vector generation logs, confirm that vector dimensions of all input documents match the
defaultTokenparameter, with no dimension mismatch errors. - Simulate submitting a new service ticket, check that the index completes synchronization within the preset time, with no delays or data loss.
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 2026-09-28.