What this category of data looks like
Data comes from three main sources: grassroots branch study records from the state-owned and public enterprise equipment manufacturing sector, party building policy documents for the construction machinery industry, and supporting party building content embedded in equipment operation and maintenance manuals. Updates follow a set cadence: monthly for grassroots branch study records, quarterly for industry party building policies. Document structure includes standard fields: branch ID, equipment model, study topic, study duration, and discussion content. Units used include sets, hours, times, and other relevant units. Individual document lengths vary greatly, from short study check-in records (a few dozen characters) to detailed special discussion minutes (thousands of words).
Constraints imposed on vector models and indexing
Documents include structured metadata such as branch ID and equipment model. Vector indexes must support filtering recall results by metadata to improve targeted retrieval accuracy. Individual document lengths vary widely. Chunking strategies must balance semantic completeness for short texts and context retention for long texts, to prevent truncation of construction machinery technical terms. Content mixes construction machinery technical terms and party building policy text. Embedding models must support cross-domain semantic matching. Updates follow a low-frequency batch cadence. Batch re-embedding scheduling must adapt to off-peak hours to avoid occupying business resources.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic completeness for long discussion records and policy documents in construction machinery party building materials |
| Overlap Length | 100–150 characters | Retains contextual connections between chunks, prevents truncation of construction machinery technical terms |
| Similarity Threshold | 0.72–0.85 | Filters low-relevance recall results, adapts to semantic matching accuracy for technical terms |
| Single Search Limit | Top 10 results | Covers recall requirements across multiple dimensions such as branch and equipment type |
| defaultToken | 4096 | Adapts to long text vector generation for party building policy documents |
| maxToken | 8192 | Supports context length after splicing multiple documents |
Three common configuration errors
- Testing returns a 404 error after configuring the Doubao-embedding model. The cause is failure to properly configure the API key and access domain name, resulting in requests that cannot match valid interfaces.
- Multilingual recall rate decreases after batch re-embedding of the knowledge base. The cause is failure to adjust adaptation parameters for the embedding model for construction machinery technical terms, leading to insufficient cross-language semantic matching accuracy.
- Indexes generated for manually inserted party building knowledge base entries disappear several hours later. The cause is failure to enable index persistence configuration, and the system automatically cleaned temporary index entries not associated with valid metadata.
How to confirm the configuration is correct
- Upload a test document containing both construction machinery technical terms and party building topics. Check the vector generation logs to confirm there are no abnormal truncation prompts.
- Submit a test question related to party building topics. Check if the recall results' metadata includes the correct branch ID and equipment model fields.
- Run a batch re-embedding task. Check if the task progress and final generated index count match the total number of uploaded documents.
- Test the configured embedding model interface. Confirm that the returned vector dimensions meet FastGPT requirements.
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.