What the Source Data Looks Like
Research report data comes from three main sources: monthly operational reports for internal business segments, special investigation documents from subsidiaries, and publicly available industry segment research reports. Data update schedules follow two cycles: monthly for internal subsidiary data, weekly for publicly available industry reports. Individual document character counts vary widely. Values should be confirmed using sample statistics or actual testing for the target deployment. Common fields include segment revenue proportion, subsidiary business gross margin, and industry benchmark parameters. Units include 100 million yuan, percentage, and year-over-year growth rate. Some documents include subsidiary production capacity planning tables and quarterly performance comparison data.
Constraints Imposed on Vector Models and Indexing
Long individual research reports with multi-segment business data split business context when using conventional segmentation strategies. Long-document-adapted segmentation logic is required. Multi-field mixed document structures require setting differentiated embedding weights for core fields such as revenue and gross margin during indexing. This avoids non-core fields interfering with recall accuracy. Mixed internal and public data sources require distinguishing embedding vector generation priorities. Incremental indexing configurations must adapt to monthly and weekly update cycles to avoid resource consumption from full indexing. The numerous specialized fields of subsidiary businesses require indexing recall dimensions to cover both segment and subsidiary levels. This prevents overly generalized recall results.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Individual research reports contain multiple business segments. This segmentation length preserves complete context for a single business module, avoiding business logic breaks from excessive splitting |
embedding_model | qwen3-embedding-8b or locally deployed m3e-large | Research reports contain multi-dimensional business fields. This type of model’s long-text embedding capability covers semantic associations across multiple fields, adapting to complex business data |
recall_topk | Top 10–15 results | Research report retrieval needs to cover relevant content across multiple subsidiaries or segments. Too many recall results increase subsequent re-ranking pressure, while too few may miss core business information |
index_incremental | Enabled | Internal data is updated monthly, industry research reports weekly. Incremental indexing reduces resource usage from full indexing, adapting to the data update schedule |
similarity_threshold | 0.72–0.85 | Research report content has high semantic similarity. This threshold filters low-correlation recall results, retaining document fragments related to core business |
parse_timeout | 600 seconds | Individual research reports are lengthy. Sufficient time is required for parsing and embedding processes, to avoid indexing interruptions from mid-process timeouts |
The parameter values provided on this page are common recommendations for starting point configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. Test on applicable samples before finalizing settings.
Three Common Configuration Errors
- The interface shows the file status as "Indexing" continuously with no progress updates. In FastGPT v4.9.11, this is most often caused by incorrect configuration of the local deployment path for the embedding model, or a model load timeout without a retry mechanism, leading to suspended indexing tasks.
- After adding an index model configuration with the same name, the original configuration is automatically overwritten. This occurs because the model configuration interface does not distinguish between model deployment addresses and version identifiers, and only uses the model name as the unique identification key, resulting in overwritten configuration items.
- Retrieval results only contain scattered business data fragments, without associating complete subsidiary business logic. This is caused by an excessively small
chunk_sizevalue, where split document fragments lose context association of business segments, making complete business logic unrecognizable.
How to Verify Successful Configuration
- Enter the FastGPT model management interface, check if the configured embedding models include the target model name and deployment path, and confirm there are no duplicate configuration items.
- Upload a small test research report, check if the indexing progress completes within a reasonable time frame, with no continuous suspended status.
- Submit a research report retrieval request, check if the returned result fields cover core business dimensions such as subsidiaries and segments, meeting retrieval requirements.
- Check the indexing logs, confirm that incremental indexing only synchronizes newly added or updated documents, and no full indexing tasks are triggered.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.