Reference only; verify against the target FastGPT release and deployment documentation.
What This Category’s Data Looks Like
Data primarily comes from internal party building management systems, policy documents issued by superior party committees, party building learning ledgers and monthly assessment records from individual gas stations. Update cadence includes on-demand updates (for superior policy documents) and scheduled updates (for monthly learning records and assessment question banks). Document structure includes three types: full-length original policy documents, structured learning points, and assessment question banks in Q&A format. Fields include document number, issuing unit, release date, and applicable station scope. Metrics include document size and number of stations.
What Constraints These Characteristics Impose on the Vector Model and Indexing Workflow
- The long text of party building policy documents requires chunking that preserves semantic integrity, avoiding breaks to the contextual connections of party and government terminology.
- The presence of structured learning points and assessment Q&A pairs requires indexing to support precise matching and semantic recall of Q&A pairs.
- The applicable station scope field attribute requires indexing to support filtered recall by business fields, narrowing the retrieval scope.
- Scheduled updates of learning records and assessment question banks require support for incremental index updates, avoiding resource consumption from full index reconstruction.
- The professional nature of party and government content requires vector models to adapt to party building domain terminology semantics, ensuring the relevance of recall results.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to semantic units of party building policy documents, avoids breaking contextual connections of party and government terminology |
| Overlap Length | 100–150 characters | Ensures semantic continuity between adjacent chunks, avoiding context breaks |
defaultToken | 4096 | Matches the average token count of party building document chunks, balances recall scope and calculation efficiency |
maxToken | 8192 | Adapts to the total token limit of chunked long documents, meeting complete semantic recall for policy documents |
| Similarity Threshold | 0.72–0.78 | Balances semantic relevance and recall count for party building content, avoiding redundancy or missed results |
| Single Search Limit | Top 8 entries | Adapts to gas station retrieval scenarios, controls returned result count and response speed |
Three Common Misconfigurations
- Symptom: When deploying a PostgreSQL database via Docker, knowledge base index creation fails. The host is an 8-core 16GB virtual machine without a GPU. Cause: Index shard configuration was not adjusted for the chunk length of party building documents, causing single shard data volume to exceed memory thresholds.
- Symptom: After entering party building-related test questions, retrieval results include excessive irrelevant content or no matching results. Cause: Similarity threshold setting deviates from the calibrated range for the business scenario, and was not adjusted based on the characteristics of refined oil sales party building content.
- Symptom: After importing assessment question banks in Feishu multi-dimensional document or Excel format, Q&A pair content cannot be parsed correctly. Cause: Document structured parsing switch was not enabled, causing non-standard format Q&A pairs to fail to be split and indexed correctly.
How to Confirm the Configuration Is Complete
- Upload a single party building policy document, check that chunked text retains complete contextual connections of party and government terminology, with no obvious semantic breaks.
- Enter party building-related test questions, verify the matching degree of retrieval results, and adjust the threshold to meet business requirements.
- Import assessment question banks in Feishu multi-dimensional document and Excel formats, check that parsed text includes all fields and content from the original documents.
- View index creation logs, confirm there are no shard timeout or out-of-memory errors, matching the current hardware resource configuration.
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.