What the Data for This Category Looks Like
The service inquiry and response data for government investment platforms primarily comes from officially published service guides, policy documents, daily inquiry response cases, and project announcement information. The data update rhythm is irregular: policy documents are updated alongside policy adjustments, service guides are reviewed and adjusted every six months, and inquiry response cases are added in real time as user inquiries arise. Document structures range from single-page service instructions, several-hundred-word official response content, to full policy texts spanning thousands of words. Fields include service item name, processing time limit, application materials, inquiry phone number, official response content, and similar items. Units involved include workdays, area codes, specific processing amounts, and other relevant units.
Constraints Imposed on Vector Models and Indexing
Government investment platform data mixes short-text service instructions and long-text policy documents. A vector chunking strategy must balance semantic completeness and encoding efficiency to handle this mix. Daily inquiry response cases are added in real time as users submit new inquiries, so systems must support incremental indexing trigger logic. Fields include structured processing time limits and unstructured official response content, so teams must set differentiated vector encoding weights for different fields. Some documents contain government affairs-specific terminology and local exclusive expressions, so the vector model must adapt to semantic features in the government affairs domain to avoid matching deviations. Additionally, the data update frequency is irregular, so teams must flexibly adjust the indexing trigger cycle to avoid wasting computing resources.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed structure of government investment platform service documents, which include both short instructions and long policies, avoiding overly fragmented chunks that lose context or overly long chunks that lose semantic meaning |
| Overlap Length | 100–150 characters | Compensates for semantic gaps at chunk boundaries, ensuring continuity of cross-chunk service processes |
defaultToken | 2048 | Adapts to the input upper limit of most general embedding models, and is compatible with the standard chunk length of government investment platform documents |
maxToken | 4096 | Meets the vector encoding needs of chunks from long policy documents, avoiding truncation of critical policy content |
| Single Index Upper Limit | First 200 entries | Adapts to the batch import rhythm of daily government investment platform inquiry response cases, avoiding overloading indexing tasks |
| Similarity Threshold | 0.75–0.85 | Matches the semantic matching accuracy required for government investment platform service inquiries, filtering low-relevance response content |
Three Common Misconfigurations
- Index tasks remain in a running state with no progress, and the vector indexing progress bar in the interface is stuck. The cause is that incremental indexing trigger rules are not configured, and a full index batch imports a large number of old service documents and policy files, resulting in excessive indexing resource usage.
- An error is returned when calling the embedding model, with logs showing token limit exceeded. The cause is that the
maxTokenparameter is not adjusted, and chunks from long policy documents exceed the input upper limit of the embedding model, resulting in vector encoding failure. - The search results return a large number of irrelevant service instructions, with insufficient matching accuracy. The cause is that the similarity threshold is set too low, failing to filter non-target documents with low semantic relevance.
How to Verify Correct Configuration
- View the vector indexing task logs in FastGPT v4.17.0 to confirm that the length of chunked documents does not exceed the configured
maxTokenupper limit. - Manually import 10 test service documents to verify that the indexing task completes within the set time period, with no stuck status.
- Input a test inquiry question to check that the semantic matching degree between the returned search results and the question aligns with the set similarity threshold.
- View the document details page after indexing to confirm that all service item and official response content fields have been correctly extracted and encoded.
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.