What Data for This Category Looks Like
This category's data primarily comes from the daily deliverables of internet product teams, business analysts, and product managers. It includes requirement research notes, PRD documents, user story lists, and requirement change forms. Data sources cover customer interview records, internal ticket systems, public competitor pages, and user behavior tracking dashboards. The update frequency is once per week or per product version. Most documents use business objectives, acceptance criteria, and functional details as core fields, with no unified standardized format. Some documents include vague boundary condition descriptions and change traceability records.
Constraints Imposed on Vector Models and Indexing
The characteristics of this category's data impose multiple constraints on the vector model and indexing workflow. First, data sources are scattered and formats are inconsistent. Semantic encoding must adapt to non-standard text to avoid vector deviation caused by format differences. Second, the update frequency is once per week or per product version. Indexing must support incremental updates to reduce the computational overhead of full reconstruction. Third, documents include vague boundary conditions and acceptance criteria descriptions. Vector recall must prioritize matching core business fields and filter irrelevant change records. Finally, individual document lengths vary significantly. Chunking rules must be adjusted flexibly to prevent semantic fragmentation in long documents and information loss in short documents.
Configuration Guidelines
This configuration is based on the interface and parameter logic of FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of PRD documents and requirement notes, avoiding semantic fragmentation |
| Overlap Length | 100–200 characters | Retains contextual connections between chunks, preventing loss of boundary information |
| Similarity Threshold | 0.72–0.85 | Filters low-match non-core requirement content, reducing invalid recall |
maxToken | 4096 | Adapts to the input limit of mainstream open-source vector models, avoiding encoding truncation |
| Single Search Limit | Top 8 entries | Balances recall coverage and query latency, adapting to the narrow-range retrieval needs of requirement analysis |
| Maximum Context | 6000 characters | Matches the typical context length of product documents, ensuring recalled content can fully link to business objectives |
Three Common Configuration Errors
- Symptom: After uploading a PRD or requirement change form, no new entries appear in the vector index list. Background logs display "index task not triggered". Cause: The incremental index switch is not enabled, or the chunk length setting exceeds the vector model's
maxTokenlimit, preventing successful text encoding. - Symptom: An error "no available channels" is returned when calling the m3e model, and vectors cannot be generated. Cause: Access parameters for the m3e model were not configured per official guidelines, or channel quotas have been exhausted.
- Symptom: Repeated identical requirement-related queries each trigger a vector database search, with no cache hits. Cause: Query cache configuration is not enabled, or the cache validity period is set too short to cover the typical cycle of requirement queries.
How to Verify Successful Configuration
- Upload a standard PRD document, check the completion status on the index progress page, and confirm the number of chunks matches expected values.
- Submit a query targeting requirement boundary conditions, verify that the similarity scores of returned results fall within the preset threshold range.
- View the vector database monitoring panel, confirm that incremental index tasks trigger automatically at the weekly or per-product-version update frequency.
- Submit the same requirement query repeatedly, confirm that cache hit logs are generated normally.
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.