What data for this category looks like
The data for this category mainly comes from government information disclosure platforms, official agency archive systems, document management systems, and departmental business ledgers. Data updates follow no fixed cycle, and are generated alongside new government information disclosure upon application processing workflows. Daily 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. Each individual document contains fields such as applicant identification, application matters, processing workflow nodes, public disclosure determination results, and relief pathway descriptions. Some historical archives have unstructured text transcribed from scanned documents, with inconsistent formatting.
Constraints imposed on vector models and indexing
Multi-source and heterogeneous data sources require indexes to support embedding processing for mixed-format text. Unstructured text transcribed from scanned documents requires additional text cleaning steps for adaptation. No fixed update cycle requires indexes to support incremental updates, reducing operational costs of full reconstruction. Individual documents have strong personalized characteristics, so chunk length must be adjusted to maintain semantic integrity for both short application matters and long process texts, avoiding loss of key information such as the basis for public attribute determination. Case-specific business scenarios require retrieval results to cover the specific matter scope specified by the applicant, avoiding invalid matches from generalized retrieval.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed structure of this category's documents, which includes both short application matters and long reply texts, ensuring the integrity of semantic chunks |
| Overlap Length | 100–150 characters | Prevents key information from being truncated after chunking, and connects the contextual logic of adjacent semantic chunks |
| Similarity Threshold | 0.72–0.80, calibrated via actual testing | Balances retrieval precision and recall rate, adapting to the narrow-range matching needs of case-specific matters |
| Single Search Limit | Top 8 results | Controls the amount of returned information, avoiding redundancy caused by exceeding the context window |
maxToken | 8000 | Adapts to the context length requirements of single-round Q&A for this category, covering long texts such as reply letters and reconsideration materials |
defaultToken | 2000 | Sets the maximum token count for a single embedded text, adapting to the input limits of most embedding models |
Three common misconfigurations
- Symptom: The interface displays "No available channels", and the vector model configuration cannot be called after saving. Cause: The embedding model was not added to the available channel list for the current group, or the API address and key for model calls were not filled correctly.
- Symptom: The number of retrieval results does not match the set single search limit, with too many or too few results returned. Cause: The index configuration and retrieval parameters for the Q&A workflow were not correctly associated, or the similarity threshold was set outside a reasonable range, leading to filtering exceptions.
- Symptom: Embedding task execution fails, with connection timeout or authentication errors displayed in the console. Cause: Reasonable timeout parameters were not configured, the locally deployed open-source embedding model did not open the corresponding port, or the API key for model calls was not filled correctly.
How to confirm successful configuration
- Enter the model management page of FastGPT v4.17.0, confirm that the embedding model has been added to the current group, and that the API key and call address are configured correctly.
- Upload 1 to 2 typical documents of this category, such as a government information disclosure reply letter, perform a chunk embedding test, and check whether the chunk length and overlap logic meet expectations.
- Initiate a simulated Party Building learning-related government information disclosure upon application Q&A, verify whether the number of returned retrieval results and similarity scores match the configured parameters.
- Check the index update log, confirm that newly added government information disclosure upon application documents can be normally indexed and retrieved, with no error messages.
The values provided here are common starting points, and should be evaluated 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.