What the Data for This Category Looks Like
IT service industry research report data comes from securities firm research institutes, public industry association reports, listed companies’ regular financial reports, and outputs from third-party research institutions. Update rhythm fluctuates with financial report release cycles. Update density increases during concentrated quarterly and annual report disclosure periods. Most documents are in PDF format. Their structure includes titles, issuing organization identifiers, release timestamps, covered track classifications, core business indicator tables, ratings and forecast content. Fields include unique research report IDs, covered company names, revenue forecast values (unit: ten thousand yuan), year-over-year growth rate percentage markers, and other related fields.
Constraints Imposed by These Characteristics on Vector Models and Indexing
Research reports have high demand for long paragraph analysis, and contain large volumes of structured table data. This requires vector models to support long text encoding and multimodal table vector generation. The volatile update pattern requires indexes to support incremental updates and regular full refreshes, to avoid index lag behind research report release schedules. Structured data with multiple fields requires separating metadata and content vectors, to prevent irrelevant fields from interfering with vector recall accuracy. Long text chunking must retain contextual connections, to avoid breaking business logic coherence after splitting, and adapt to indicator tables of varying lengths.
Configuration Settings
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
embedding_model | Doubao-embedding-large | Adapts to vector encoding requirements for long text and structured tables, matches the characteristics of research report content |
max_chunk_length | 800–1200 characters | Adapts to the average length of research report paragraphs and table cells, avoids splitting that damages business context |
chunk_overlap_rate | 10–15% | Retains contextual connections between adjacent chunks, prevents logical breaks after long paragraph splitting |
vector_index_dim | 1024 | Matches the output dimension of the Doubao-embedding-large model, ensures index compatibility |
recall_count | Top 8–12 entries | Research report content has high density, requires recalling a sufficient number of relevant fragments to cover complete analysis logic |
similarity_score_threshold | 0.72–0.80 | Filters low-relevance research report fragments, avoids recalling analysis content from irrelevant tracks |
The parameter values provided on this page are all conventional recommendations, used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Clicking
Enable Doubao-embedding-largeindex model, filling in the custom request address and APIKey, then testing directly results in an error. The cause is failure to verify that the port of the custom address is open, or that the APIKey has not been granted vector model call permissions. - After refreshing the knowledge base page, the prompt "No available index model detected" is displayed. The cause is that the vector model configuration has not been synchronized to all service nodes, or the model cache has not completed initial loading.
- When enabling model-based paragraph recognition during knowledge base chunking, table data does not generate corresponding vector chunks. The cause is that the multi-vector support switch is not enabled, or table fields are not recognized as structured content included in the vector generation process.
How to Confirm Configuration Is Successful
- Enter the settings page of the target knowledge base, confirm that the
embedding_modeldropdown has selected the target model, and the custom request address and APIKey configuration items are filled correctly. - Upload a single compliant research report document, wait for parsing to complete, then enter the chunk details page to confirm that table data has generated independent vector chunk entries.
- Initiate a test conversation, enter a business question related to the research report content, check that the recall results include matching research report fragments, and there is no obviously irrelevant track analysis content.
- Refresh the knowledge base page, confirm that the prompt "No available index model detected" no longer appears, and the global model status shows normal operation.
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.