What the data for this category looks like
The core users of this category include product operations, customer success teams, and product managers. Data mainly comes from ticket systems, customer interview records, CRM systems, and product roadmap documents. Daily processed documents include requirement pool lists, requirement evaluation forms, customer feedback records, and version planning descriptions. Data is updated weekly or per product version. Documents typically contain fields such as requirement ID, customer identifier, requirement description, priority, evaluation cost, and associated version. The format is mostly structured or semi-structured text, with no unified mandatory specification.
Constraints on Vector Models and Indexing
The data sources for this category are scattered and update cycles are inconsistent, requiring vector indexes to support incremental updates to adapt to weekly or version-level synchronization rhythms. Document types are diverse and fields are not unified, so the chunking logic must support both semi-structured and unstructured text. Core business processes rely on duplicate requirement merging, which imposes clear requirements on the similarity recall accuracy of vector models. Fields include customer identifiers and requirement priorities, so the index must retain field association capabilities to support subsequent targeted filtering.
How to Configure Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical length of single requirement descriptions in requirement pool documents, preventing key information from being split within a chunk |
| Overlap Length | 100–200 characters | Retains cross-chunk requirement context and associated field information, preventing evaluation logic from being truncated |
| Similarity Threshold | 0.75–0.85 | Used for duplicate requirement merging, balancing business risks of false positives and false negatives |
| Single Search Limit | Top 20 entries | Covers the typical concentration of similar requirements, avoiding missing potential duplicate items |
maxToken | 4096 token | Adapts to the input length limit of mainstream embedding models, ensuring embedding quality for complete requirement text |
defaultToken | Calibrated based on actual testing | Processes scattered customer feedback text without clear fields, supplementing basic context information |
Three Common Configuration Mistakes
- An error indicating no available channel occurs after configuring the embedding model channel. The cause is failure to correctly bind the service address or API key of the corresponding model in the system, or a mismatch between the selected channel type and the actually deployed model.
- Knowledge base search takes too long. The cause is that the chunk length is set too large, increasing the embedding calculation load per chunk, or the single search limit is set too high, exceeding hardware processing capabilities. For example, RTX2070 graphics cards require controlling the number of chunks processed per batch.
- Q&A split processing results do not meet expectations. The cause is confusing the division of labor between vector models and large language models, mistakenly using vector models for semantic sentence splitting, or failing to configure an appropriate chunk overlap length, leading to context breakage.
How to Verify Correct Configuration
- Import two requirement samples with highly similar content, verify that the system can match the corresponding entries according to the set similarity threshold, and check the rationality of the matching result ranking.
- View the system embedding task running logs, confirm that the
maxTokensetting does not trigger a model input length exceeded error, and that embedding time meets the expected range adapted to the hardware. - Manually trigger an incremental synchronization task, verify that the system can synchronize the latest requirement pool data according to the weekly or version-level update rhythm, with no missing or duplicate synchronized entries.
- Test a search request containing the customer identifier field, confirm that the system can filter relevant entries in the requirement pool by field, retaining the field-associated retrieval capability.
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 2026-10-19.