What the data for this category looks like
Data for this category originates primarily from CRM systems, product usage data, ticket statistics, and contract ledgers. Most updates occur on a weekly or monthly basis. Daily processed documents include customer health reports, renewal forecast sheets, high-risk customer lists, and communication notes. Data fields cover customer identifiers, contract amounts, expiration dates, active days, ticket response times, and additional metrics. Corresponding units include yuan, days, attempts, and similar units.
Constraints on Vector Models and Indexing
Data sources include structured tables and semi-structured text. Indexing must support both field concatenation and natural language semantic retrieval requirements. Most updates occur weekly or monthly. Real-time index refreshes are not needed. Incremental update modes reduce resource consumption. Document lengths vary widely. Renewal forecast sheets use short field combinations. Communication notes use long text. Chunking and indexing configurations must balance semantic integrity across all document types. Multi-field unit associations require retaining contextual links during chunking. This prevents semantic breaks that reduce retrieval accuracy.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for both short and long texts such as renewal forecast sheets and communication notes |
| Overlap Length | 100–200 characters | Prevents chunking from cutting critical contextual links such as contract expiration dates and customer risk levels |
| Similarity Threshold | 0.72–0.80, calibrated via actual testing | Balances recall and precision for high-risk customer identification |
| Single Search Limit | Top 10 results | Supports fast retrieval for bulk high-risk customer lists |
defaultToken | 4096 | Accommodates multi-field concatenated context from customer health reports |
maxToken | 8192 | Covers all fields from renewal forecast sheets and associated communication note content |
Three Common Configuration Mistakes
- Initiate retrieval directly when the interface displays "Index Not Ready". This returns empty results or status code 400. The root cause is incomplete index construction, or chunk length exceeding the maximum input limit supported by the model.
- Use specific columns from an entire table directly as a knowledge base index, without splitting chunks by document type. This ignores semantic associations across different fields, leading to retrieval results that fail to match full customer risk context.
- Incorrectly configure associated vector model parameters when deploying the deepseek model. This causes vector model call errors. The root cause is either failure to bind a vector model compatible with deepseek, or incorrect parameter configuration format.
How to Verify Proper Configuration
- Access the index management page in FastGPT v4.17.0. Check the status indicator for the target index. Confirm it shows ready.
- Upload a single test fragment of a high-risk customer list. Run a retrieval test. Confirm the number of returned results matches the single search limit setting.
- Review the vector model binding configuration. Confirm the selected model type is compatible, such as Doubao-embedding.
- Call the test interface with a renewal-related query. Check that the token count of the returned context does not exceed the
maxTokenlimit.
The values provided are common starting points. They should be evaluated against sample datasets relevant to the specific deployment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.