What data for this category looks like
The data for this category mainly comes from internal historical speeches, annual and quarterly report materials, official document templates issued by superior authorities, and industry policy documents. Update frequency follows internal meetings and report cycles, with no unified fixed schedule. Document structures are relatively fixed, including title, main recipient unit, main body, signature and date of drafting. Some documents have attachment fields. The word count of individual documents varies significantly. Fields include clear identifiers such as document issuing entity, meeting theme, report items, and completion progress.
What constraints these characteristics impose on embedding models and indexing
Fixed document structures with clear field identifiers allow targeted extraction of core fields to generate embeddings, avoiding interference from irrelevant content on embedding quality. Significant variation in individual document word counts requires flexible chunking strategies. This prevents overly fragmented chunks that cause semantic fragmentation, or overly long chunks that exceed model context limits. The lack of a unified update cycle requires support for incremental indexing configurations. Only new or modified documents generate embeddings, reducing computing resource consumption. Most internal data is sensitive. It is necessary to ensure that embedding storage and indexing processes comply with data security specifications to prevent sensitive information leaks.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the wide range of word counts for documents in this category, balances semantic integrity and chunk granularity, and avoids semantic fragmentation or exceeding model limits |
| Overlap Length | 100–200 characters | Ensures semantic coherence between adjacent chunks, and prevents core expressions from being truncated by chunking |
| Similarity Threshold | 0.72–0.85 | Aligns with the semantic characteristics of official documents in this category, balances recall accuracy and coverage, and avoids mixing irrelevant content |
| Single Search Limit | Top 8 entries | Focuses on core reference content, prevents excessive entries from causing context overload, and meets the accuracy requirements for speech drafting |
| maxToken | 16000 token | Adapts to long document processing needs, and complies with the input context limits of mainstream embedding models |
| defaultToken | Fill in according to the actually bound key | Used for model calls without a dedicated key specified, and ensures access control for sensitive data |
Three common mistakes
- An error pop-up stating "This token does not have permission to use the model" appears. The cause is incorrect configuration of
defaultTokenor a dedicated model key, or the key is not bound to the usage permission of the corresponding model. - The system prompts that no available vector channel is available. The cause is that the access configuration for the embedding model was not added correctly in FastGPT, or the channel verification logic does not adapt to the access method of non-official models.
- The generated embedding results have severe semantic fragmentation. The cause is that the chunk length setting does not match the semantic unit characteristics of documents in this category, leading to truncation of key information.
How to confirm configurations are set correctly
- Upload a single typical speech document, check the embedding generation log, and confirm that the chunk length matches the configured value.
- Initiate a test query, check the number of recalled result entries, and confirm that the single search limit configuration is in effect.
- Add a new test document and trigger indexing, check the update records of vector storage, and confirm that the incremental indexing logic runs normally.
- Check the model call log, and confirm that the
maxTokenconfiguration complies with the input context limits of the currently used embedding model.
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.