What data for this category looks like
Party building learning data in the refinery and chemical production sector comes from annual party building study plans issued by the enterprise party committee office, records of three meetings and one party lesson, themed party day activity ledgers, industry party building compliance documents, and archived personal learning notes from party members. Documents are updated monthly with current month’s learning materials, and quarterly with party member learning ledgers and activity records. Most documents are formal PDF files, containing title, issuing unit, release date, main body content, and attachment links. Fields include learning topic, required study hours, assessment deadlines, scope of participating party members. Units of measure include study hours, number of participants, and number of activities.
Constraints on vector models and indexes
The main body of a single party building document can reach thousands of characters, and includes attachment links. This requires vector chunking to adapt to long text splitting, and avoid truncating key party building terminology and industry-specific expressions. Data update frequency is monthly or quarterly. Index update strategies must match this schedule, to avoid index lagging behind the latest learning materials. Fields contain party building-specific terminology and refinery and chemical industry-specific expressions, such as three meetings and one party lesson, and party building linkage during equipment maintenance. This requires vector models to have the ability to recognize domain-specific terminology, to avoid semantic deviation. Ledger-type data are structured tables. This requires support for generating multiple vectors to cover table fields and content, to ensure complete ledger information can be retrieved during queries.
Configuration settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the main body length of single refinery and chemical party building documents, avoids single chunk content being too long and exceeding model processing limits |
| Overlap Length | 100–150 characters | Ensures semantic coherence between adjacent chunks after long document splitting, covers cross-chunk expressions of party building themes |
| Similarity Threshold | 0.75–0.85 | Filters low-match non-party building content, focuses on learning materials relevant to the query theme |
| Single Search Limit | Top 10 entries | Balances recall scope and response speed, adapts to single-theme query requirements for party building Q&A |
maxToken | 8000 token | Adapts to long document vector generation and context splicing requirements, aligns with the default configuration range of FastGPT v4.17.0 |
defaultToken | 1000 token | Configures the base token length for a single vector, matches the average chunk size of party building documents |
Three common mistakes
- Enabling the Doubao-embedding-large index model, filling in a custom request address and apikey, then testing directly results in an error. The cause is that the custom request address does not adapt to the field verification rules for refinery and chemical party building data, or the apikey permission has not been granted for the vector generation interface.
- Refreshing the page displays the error "No available index model detected". The cause is that the configured vector model has not been associated with the current party building knowledge base, or the configuration items were exited without saving.
- Not enabling multi-vector support for table data, resulting in empty query recall results for party building ledger tables. The cause is that the table multi-vector option was not checked in the knowledge base settings, so independent vector segments cannot be generated for table fields.
How to confirm configuration is complete
- Navigate to the knowledge base settings page, confirm that the vector model configuration item shows as enabled and has no error prompts.
- Upload a formal refinery and chemical party building learning document, wait for indexing to complete, and check that the vector generation log has no abnormal prompts.
- Initiate a query related to party building learning, verify that the matching degree between the retrieved documents and the query theme meets expectations.
- View the vector index statistics panel of the knowledge base, confirm that the corresponding number of vector segments have been generated.
The values provided are common starting points, and should be evaluated against the samples used in the target deployment.
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.