What this category of data looks like
State-owned industrial fund policy and regulatory basis data primarily comes from official public documents issued by state-owned assets supervision and administration commissions, finance departments, and local state-owned assets supervision institutions. It also includes internal system documents such as fund contribution agreements and management charters. Document formats are mostly PDF or official web pages, with wide variations in total length. Fields include document number, issuing authority, effective date, clause hierarchy, original text body, and more. Clauses are layered and marked by article, paragraph, and sub-paragraph. Data updates have no fixed cycle, and are triggered by the release of new national and local policies or adjustments to internal fund systems.
What constraints do these characteristics impose on the vector models and indexing link
The wide length range of individual documents requires chunking strategies adapted to both short and long texts, to avoid single chunks exceeding model limits. The rich metadata fields require clear specification that vector modeling only targets the main text content, to avoid metadata interfering with retrieval accuracy. Data updates have no fixed cycle, so incremental indexing trigger logic must be configured to only sync updated files, reducing resource consumption from full indexing. Policy documents have hierarchical and repealed relationships, so metadata tags must be associated during indexing to facilitate filtering invalid content during subsequent retrieval.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the length of individual clauses in state-owned industrial fund policy documents, avoid single chunk tokens exceeding maxToken limits |
| Overlap Length | 100–200 characters | Prevent policy clauses from being truncated by chunking, which would cause context breaks and ensure semantic coherence |
| Similarity Threshold | 0.72–0.78 | Balance recall accuracy and coverage for policy Q&A, meet the rigor requirements of official documents |
| Single Search Limit | Top 10 results | Control the volume of returned results, avoid exceeding defaultToken limits due to overly long context |
| Reranking Model | Enabled | Optimize semantic ranking for policy documents and improve precise matching |
| Maximum Context Length | 6000–8000 tokens | Adapt to the multi-clause combination requirements of state-owned industrial fund policy Q&A, align with the general parameter range of FastGPT v4.17.0 |
Three common mistakes
- Phenomenon: After enabling the reranking model, no changes are observed in online recall test results. Cause: The associated switch for the reranking model was not enabled synchronously in the knowledge base indexing configuration, or the reranking model's call parameters do not match the dimensions of the main vector model.
- Phenomenon: An error during indexing prompts parameter overlimit. Cause: The chunk length exceeds the range allowed by
maxToken, leading to failure in generating single-chunk vectors. - Phenomenon: Retrieval results include repealed policy documents. Cause: No metadata filtering rules were configured, and the effective and repealed status tags of policy files were not associated.
How to confirm the configuration is complete
- Access the knowledge base indexing configuration page in FastGPT v4.17.0, verify that the set values for chunk length and overlap length match the preset plan.
- Initiate a single test retrieval, check whether the number of returned results conforms to the single search limit configuration.
- After enabling the reranking model, compare the sorting differences of retrieval results before and after enabling to confirm the reranking logic is active.
- Upload a test policy document to verify that incremental indexing can normally sync updated content.
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.