What the data for this use case looks like
Data comes from official system documents of internal units, including departmental regulations, process specifications, management measures, and similar materials. Data updates follow no fixed cycle, and are synchronized with new system releases, revisions, or the abolishment of existing systems. A single document typically includes structured fields such as system number, issuing unit, effective date, and main body content, and is bound to a unique affiliated unit identifier. Document length varies widely, ranging from hundreds of words to tens of thousands of words. The system collections of each unit are relatively independent, and retrieval results must be isolated by affiliated unit.
What constraints do these characteristics impose on the vector model and indexing workflow?
The relatively independent system collections of each unit, combined with the bound affiliated unit identifier, require the vector index to support retrieval isolation based on unit metadata. This prevents confusion from cross-unit results. Data updates follow no fixed cycle, so incremental index synchronization logic must be configured to reduce resource consumption caused by full index reconstruction. The large variation in single document length and structured format require retaining the affiliated unit metadata field during chunking. This ensures accurate association with the corresponding unit during retrieval. Some systems have cross-paragraph related content, so chunking must balance context integrity to avoid disrupting semantic coherence.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Config Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Internal system documents are mostly standardized text. This range preserves complete clause context and avoids chunk breakage |
| Chunk Overlap Length | 100–150 characters | Chunk overlap preserves cross-block context association, ensuring complete clause semantics during retrieval |
| Similarity Threshold | 0.72–0.80 | Internal system terminology is precise. This range filters low-match irrelevant results and balances recall and accuracy |
| Maximum Search Results Per Request | Top 10 results | Internal system retrieval does not require excessive results. A small number of precise results improves retrieval efficiency |
maxToken | 4000 token | The context length for internal system Q&A is moderate. This value covers the content of multiple related system documents |
defaultToken | 200 token | The single-round input length for internal system Q&A is moderate. This value covers basic query content |
Three Common Misconfigurations
- Phenomenon: A 400 status code is returned when creating an index, with no response body. Cause: The affiliated unit metadata field is not correctly associated, so the index engine cannot recognize the unit isolation rule.
- Phenomenon: The retrieval service continuously occupies a large amount of server resources and crashes. Cause: No reasonable chunk length and maximum search results per request are set, causing vector retrieval to load too many document chunks and exceed the server memory threshold.
- Phenomenon: Knowledge base file upload occasionally gets stuck during the 1st or 2nd index group. Cause: The affiliated unit field format of some documents is abnormal, causing index group synchronization to interrupt.
How to Verify Correct Configuration
- Upload a test system document that includes affiliated unit metadata, and check whether the index generation log contains the corresponding unit identifier field.
- Send a cross-unit retrieval request to verify that the returned results only include system documents from the specified unit.
- Initiate a retrieval for a single system document, and check that the returned chunk length matches the set chunk length range.
- Upload multiple system documents from different units consecutively, and observe that the index generation process has no lag or interruption.
The values listed are common starting points and should be evaluated against the user'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.