What the data for this category looks like
Data sources for general comprehensive investment research include public industry research reports, regulatory policy documents, internal research meeting minutes, and cross-domain database collections. Update frequencies vary: public reports are updated on a fixed quarterly or monthly schedule. Internal research meeting minutes are synced in real time alongside research activities. Cross-domain databases receive daily incremental updates. Document structures include semi-structured paragraph text, structured industry indicator tables, and transcribed voice meeting minute snippets. Fields include publishing entity, publish time, industry tag, and core data indicators. Corresponding units are attached to indicator fields.
What constraints these characteristics impose on vector models and indexing
Multi-source heterogeneous data structures require vector models to support cross-modal encoding of text and structured data. This avoids feature loss caused by inadequate adaptation of a single model. The wide range of data lengths requires the indexing link to support dynamic segmentation strategies. This adapts to vector generation for both short text and long documents. Real-time updated data sources require indexing to support incremental synchronization. This avoids performance losses from full index rebuilding. Multi-field metadata features require indexing configurations to support filtering and recall based on business tags. This narrows retrieval scope and improves retrieval accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to semi-structured paragraphs and long table snippets from general comprehensive investment research documents, avoids losing context from overly fragmented segmentation or exceeding model input limits from overly long segments |
recall_top_k | Top 10–15 results | Covers the recall scope for multi-source data, balances retrieval efficiency and result relevance |
similarity_threshold | 0.65–0.75 | Filters low-similarity irrelevant documents, adapts to feature differences across cross-domain data |
index_incremental_sync | Enabled | Adapts to real-time updated meeting minutes and daily incremental databases, reduces resource consumption from full index rebuilding |
embedding_batch_size | 32–64 entries | Balances video memory usage and batch processing efficiency for locally deployed models, adapts to batch encoding of multi-source data |
filter_by_metadata | Enabled, filter by industry_tag | Narrows retrieval scope based on the document's industry tag field, improves retrieval accuracy |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- After connecting a locally deployed vector model, the knowledge base interface continuously shows an "Indexing" status with no progress updates. The cause is incorrect configuration of the model's API access address and authentication key, resulting in the indexing task being unable to pull vector generation results.
- When using a custom-deployed embedding-2 model, a
400 Bad Requesterror is returned. The cause is failing to pass text snippets in the input format required by the model, or failing to correctly bind interface parameters for the OpenAPI channel. - After configuring custom indexing rules, retrieval results do not cover the expected business data scope. The cause is failing to correctly bind the document's metadata fields to the index filtering conditions, resulting in the index not generating corresponding vectors according to the configured dimensions.
How to confirm the configuration is complete
- Navigate to the vector model management page, verify that the configured model name and deployment address match the actual deployed service.
- Upload a single test document, check the indexing task logs to confirm there are no error messages in the vector generation step.
- Initiate a retrieval request, check whether the metadata fields of the returned results match the configured filtering rules.
- Adjust the similarity threshold and recall count parameters, observe whether the quantity and relevance of retrieval results change as expected.
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.