Reference only; verify against the target FastGPT release and deployment documentation.
What Data Looks Like for This Category
Data sources for this category include government portals, document management systems, government information disclosure directories, policy document repositories, and press conference materials. The update rhythm is daily synchronization. Daily documents cover policy documents, policy interpretation drafts, public directories, information release approval forms, press releases, and website column update records. They include fields such as public attribute tags, confidentiality levels, publication deadlines, and affiliated columns. The length of individual documents varies widely, ranging from hundreds-word press releases to thousands-word policy documents.
What Constraints Do These Characteristics Impose on the Vector Model and Indexing Link
The daily update feature requires indexing to support incremental synchronization mechanisms, avoiding resource consumption caused by full reindexing. Wide length ranges and structural differences across multiple document types require chunking strategies adapted to different content lengths, preventing long text truncation or overly fine granularity for short text. Public attribute and confidentiality level fields in documents require corresponding filtering logic embedded in the indexing link, preventing sensitive or non-disclosable content from entering the retrieval scope. Cross-source data format differences require unified preprocessing workflows to ensure consistency in vector generation. Field attributes of different documents also require index metadata to support various annotation information, ensuring precise filtering during retrieval.
How to Configure Settings
| Configuration Item | Suggested Value | Basis for This Value |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the common length of policy documents and interpretation drafts in this category, avoid individual chunks exceeding vector model limits, and ensure semantic integrity |
| Overlap Length | 200–300 characters | Connect the semantics of adjacent chunks, prevent key information from being truncated at chunk boundaries |
defaultToken | 1024 token | Adapt to the basic input length of mainstream embedding models, balance semantic coverage and computational overhead |
maxToken | 8192 token | Adapt to the maximum input limit of Qwen/Qwen3-Embedding-8B, support complete semantic processing of long documents |
defaultToken | 512 token | Adapt to the input requirements of short-text documents such as press releases, optimize vector generation efficiency for small-volume content |
| Single Retrieval Upper Limit | Top 5 results | Match the precise retrieval needs of party building study Q&A, avoid excessive low-relevance results interfering with judgment |
| Metadata Filtering | Filter by public attribute and confidentiality level | Match the metadata requirements of documents in this category, prevent sensitive content from entering the retrieval library |
Three Common Mistakes
- Phenomenon: When an embedded model with the same name is configured repeatedly, the new configuration automatically overwrites the original one, and settings for multiple models of the same type cannot be retained. Cause: The system uses the model name as the unique identifier for configuration, and no custom alias is provided to distinguish models with the same name.
- Phenomenon: After importing a thousands-word policy document, retrieval results show semantic breaks or missing key information. Cause: The chunk length setting is not adapted to long documents, and the overlapping chunking mechanism is not enabled, causing core content to be truncated at chunk boundaries.
- Phenomenon: After importing Excel-format public directory data, chunk granularity is too large, resulting in insufficient retrieval matching accuracy. Cause: Chunking rules are not adjusted for table documents, and the default chunking logic splits directly by row or entire table, without optimizing granularity combined with headers and business fields.
How to Confirm the Configuration Is Correct
- View the embedded model configuration list, confirm that all models that need to be enabled have been added, and there are no cases of repeated overwrites.
- Upload a typical policy document of this category, manually trigger chunk preview, check whether the chunk length and overlap length match the preset configuration.
- Configure filtering rules for public attributes and confidentiality levels, upload a test document with sensitive annotations, and verify whether it is correctly excluded during retrieval.
- Initiate policy-related questions related to party building studies, check the number and relevance of retrieval results, and confirm that the single retrieval upper limit meets expectations.
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.