What Data Looks Like for This Category
This category of data comes from regulatory and rule repositories, higher-level law databases, departmental policy document repositories, and administrative regulatory document filing systems. Commonly processed documents include draft regulatory documents, drafting explanations, legality review opinions, summary of solicited comments, and filing reports. Data updates do not follow a fixed cycle, and are synchronized with the progress of policy formulation and filing processes. Each individual document contains fields such as text content, issuing authority, effective date, and solicited opinion feedback. There is no unified standardized unit, and processing must be split according to the original structure of the document.
What Constraints These Characteristics Impose on the Vector Models and Indexes Link
Data for this category is scattered across multiple official databases and business systems, and has no unified format standard. This requires the indexing link to support automatic adaptation and batch import of multi-source heterogeneous data. Data updates do not follow a fixed cycle, and are dynamically triggered during the policy formulation process. This requires configuring a flexibly adjustable incremental update logic to avoid resource consumption caused by full re-indexing. Individual documents contain long text content and multiple types of metadata fields. Reasonable semantic chunking rules must be set to ensure that core semantic units are not split. The field structures of different documents vary significantly, so support for custom metadata extraction configuration is required for precise matching during subsequent retrieval. At the same time, retrieval results must cover core references such as higher-level laws and departmental policies to meet the comparison needs of legality review.
How to Configure the Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic integrity of regulatory document articles, avoiding splitting of long articles |
| Overlap Length | 100–200 characters | Retains semantic connection between chunks, preventing key information from being lost at chunk boundaries |
| Similarity Threshold | 0.75–0.85 | Balances retrieval recall rate and precision, adapting to the comparison needs of higher-level laws and policy articles |
| Single Search Limit | Top 10 results | Covers the common range of regulatory references, avoiding excessive redundant results that interfere with review |
maxToken | 8000 | Adapts to the context loading requirements of long documents, meeting the information integrity needs of multi-article comparison |
defaultToken | 4096 | Configures the token limit for basic embedding tasks, ensuring processing capability for conventional review documents |
Three Common Mistakes
- Phenomenon: Embedding model call fails, and the interface returns a 503 status code or the prompt "No available channels for the current group". Cause: No separate embedding model configured specifically for indexing, and the API key and channel of the conversation model are reused. An error is triggered when the corresponding channel restricts embedding model calls.
- Phenomenon: The indexing function still cannot start normally after the CHATAPIKEY environment variable is configured. Cause: The Docker container was not recreated to make the new environment variables take effect, and the initial container configuration is still used for operation.
- Phenomenon: Retrieval results cannot match the expected higher-level law articles, and the number of retrieved results does not meet expectations. Cause: The chunk length is set incorrectly, leading to splitting of core semantics, or the similarity threshold is set too high, missing some eligible matching results.
How to Confirm the Configuration Is Complete
- Upload a typical draft regulatory document, confirm the indexing task completes automatically and no format error prompts appear.
- Initiate a retrieval request, verify the number of returned results matches the configured single search limit.
- Adjust the similarity threshold, observe changes in the matching precision of retrieval results, confirm the threshold meets current review matching requirements.
- Trigger an incremental update task, confirm only newly added documents are included in the index and no full re-indexing is triggered.
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.