What this category of data looks like
This category of data comes from the national petition information system, local petition systems, the 12345 citizen hotline platform, grassroots governance platforms, and historical petition archives. Daily processed documents include petition registration forms, acceptance notices, transfer and assignment letters, handling opinions, review and re-examination applications, and resolution plans. Data updates occur daily. Most documents are a mix of structured and semi-structured text, with fields including request content, handling process nodes, responsible entities, policy basis, and other fields. Some documents are non-standard text transcribed from scanned materials.
What Constraints These Characteristics Impose on the Knowledge Base Retrieval and Recall Link
Data sources are diverse and dispersed. The knowledge base retrieval pipeline must support multi-format petition documents, and support both structured field matching and free-text semantic retrieval. Daily updated data requires the retrieval pipeline to support incremental synchronization, to avoid resource consumption from full re-import. Documents have no unified fixed format, which can cause semantic fragmentation during chunking. The chunking strategy must be adjusted to adapt to different document structures. Petition matters must follow legal handling procedures strictly. Retrieved results must be associated with compliance basis for corresponding handling processes, and must not deviate from legal requirements.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Adapt to the semantic matching accuracy of petition requests, filter out historical archives with low relevance to petition matters |
limit | Top 8–12 entries | Match the single query context length for petition handling, avoid exceeding the large model's context limit |
searchMode | hybrid | Combine keyword and semantic retrieval, adapt to the mixed characteristics of petition documents that include both structured fields and free text |
usingReRank | Enabled | Perform secondary ranking on initial retrieval results to further filter petition content with weak semantic relevance |
datasetSearchUsingExtensionQuery | Enabled | Automatically generate extended query terms based on user questions, cover synonymous expressions of petition requests, and improve retrieval coverage |
Three Common Mistakes
- Missing key content in long retrieval results: The symptom is incomplete query results from the chunked knowledge base. The cause is that the chunking strategy does not retain contextual associations in petition documents, leading to semantic fragmentation.
- No search results and error display after importing PDF documents: The symptom is failure to retrieve content after uploading petition-related PDF files. The cause is incomplete PDF text extraction configuration, or an exception during the parsing process.
- Invalid configuration parameter name error during search: The symptom is returning
{"message": "invalid configuration parameter name \"hnsw.iter\""}. The cause is using a custom configuration parameter not permitted by the platform, rather than using configuration items within the specified range.
How to Verify Proper Configuration
- Upload a single petition registration form document, run a retrieval test, and confirm that retrieved results include the core request content and handling requirements in the document.
- View configuration logs, confirm that the values of configuration items such as
similarityandlimitmatch the preset values, and no unauthorized parameters are present. - Import multiple different types of petition documents, run hybrid retrieval, and confirm that when
searchModeis set to hybrid, both keywords and semantic content are matched. - Test incremental import of daily updated petition data, confirm that the retrieval pipeline can normally load new data without synchronization delay.
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.