What This Category of Data Looks Like
Data for this category comes from government information disclosure platforms, official archive systems, internal document systems, departmental business ledgers, and relevant public disclosure norms. Commonly processed documents include government information disclosure application forms, supplementary notice letters, third-party opinion solicitation letters, reply letters, service receipts, and administrative reconsideration response materials. Data updates have no fixed cycle, with new content generated based on individual case applications and changes to internal processes. Most documents contain structured fields such as document number, issuing date, applicant subject, application items, and reply opinions. Some files are unformatted transcribed texts.
What Constraints Do These Characteristics Impose on the Vector Model and Index Link
The characteristics of this category of data impose multiple constraints on the vector model and index link. Data sources are scattered and updates have no fixed cycle. This requires support for incremental indexing and on-demand synchronization to avoid resource consumption from full retransmission. Documents contain both structured fields and unstructured reply content. Chunking must balance field integrity and semantic coherence to prevent breaking the association between application items and reply opinions. Some files have no fixed format. This requires the vector model to accurately capture the semantic matching relationship between application reasons and corresponding documents. Additionally, queries for individual case applications are highly targeted. Recall scope must be limited to avoid interference from irrelevant content.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | This category of documents contains both structured fields and unstructured replies. This range preserves semantic integrity while avoiding overly long single chunks |
| Overlap Length | 100–150 characters | Connects semantic associations between adjacent chunks, preventing application items from being split across two chunks |
maxToken | 8192 | Adapts to the context window of mainstream Chinese embedding models, matching the average length of documents in this category |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Filters low-match irrelevant documents to ensure relevance between recall results and application items |
| Single Search Limit | Top 10 entries | This category requires precise matching of specific applications. Too many recall results increase subsequent screening costs |
defaultToken | 2048 | Controls the context length of a single round of Q&A to avoid exceeding model processing limits |
Three Common Mistakes
- Phenomenon: After switching vector models, existing indexed documents cannot match queries normally. Cause: A new model with embedding dimensions inconsistent with the original model was used, or vector embeddings for corresponding documents were not regenerated.
- Phenomenon: A single file remains in the indexing state for a long time with no progress updates. Cause: The file format is not supported by the system, or the file size exceeds the single-file processing limit, causing the parsing process to block.
- Phenomenon: Q&A recall results include content from irrelevant knowledge bases. Cause: No knowledge base priority rules were configured, or priority settings did not take effect as expected, leading to failure to prioritize matching the specified knowledge base during multi-knowledge base retrieval.
How to Confirm Proper Configuration
- Upload a single typical document, such as a government information disclosure reply letter, to verify that chunked content retains complete application items and reply opinions without obvious semantic breaks.
- After switching the vector model, regenerate document vectors, and use the same test query to verify consistency of matching results, confirming that embedding dimensions and model compatibility meet requirements.
- After configuring knowledge base priority rules, initiate a query to two knowledge bases containing duplicate content, and verify that recall results prioritize the specified high-priority knowledge base.
- Submit a single large file, check whether the indexing progress completes within a reasonable time, and confirm no long-term blocking occurs.
The values provided here are common starting points and should be evaluated against samples from the actual deployment scenario.
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.