What the Data for This Category Looks Like
Data sources for cross-sector research reports include public industry news aggregation sites, securities firm research report repositories, and vertical industry document platforms. Update frequency aligns with external data sources, with no fixed batch cycle. New documents are added to the repository on demand after going live. Each individual document includes a title, publishing institution, release time, industry tags, and main body content. Some documents contain embedded tables and chart fragments. Fields include a unique document identifier, publishing institution name, release timestamp, industry classification tags. Main body content is counted in characters, with wide variation in word count per document.
What Constraints Do These Characteristics Impose on the Vector Models and Indexing Stage?
Differences in data formats across multiple sources require preprocessing logic with multi-format compatibility to be integrated into the indexing stage, to avoid semantic alignment deviations after data is added to the repository. The lack of a fixed update cycle requires indexing to support incremental synchronization mode, reducing resource consumption from full reindexing. Wide variation in word count per document and embedded structured fragments require context-aware chunking strategies, to avoid breaking industry logical associations during splitting. The presence of multi-dimensional metadata fields requires indexing to support combined queries of vector recall and metadata filtering, to meet precise retrieval needs.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Cross-sector research reports have wide variation in main body length and contain structured fragments. This range preserves semantic integrity of individual chapters |
chunk_overlap | 150–200 characters | Avoids splitting that breaks contextual associations, ensuring natural semantic continuity between adjacent chunks |
RECALL_TOP_K | 20–30 | Research report content has high professionality. Sufficient candidate sets must be recalled before reranking to ensure retrieval accuracy |
INDEX_INCREMENTAL_MODE | enabled | Research report updates have no fixed cycle. Incremental indexing mode reduces computational resource consumption from full reindexing |
PARSE_TABLE_CONTENT | enabled | Embedded tables in cross-sector research reports are core information carriers. Table content must be converted into independent vector chunks |
SIMILARITY_THRESHOLD | 0.72–0.85 | Professional documents require a high semantic similarity threshold to filter low-relevance retrieval results |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- The symptom is that after upgrading the platform version, the same CSV-format research report material triggers an indexing error, returning the
400 Bad Requesterror code. The cause is that the new version updated the field validation logic for CSV files, and the missingindustry_tagfield in the original material is not automatically supported. - The symptom is that the knowledge base indexing task gets stuck, with the interface showing "Indexing incomplete" status and no progress updates. The cause is that the
INDEX_INCREMENTAL_BATCH_SIZEparameter is not configured, and the volume of documents processed in a single batch exceeds the system default threshold, causing the task to enter a blocked state. - The symptom is that a large number of off-topic industry research reports are included in retrieval results, and the actual number of recalled documents does not match the configured
RECALL_TOP_Kvalue. The cause is that metadata fields are not added to the index combined query rules, only relying on vector similarity recall without industry tag filtering.
How to Verify Successful Configuration
- A chunking test can be performed on a single research report, to verify that chunking results retain complete chapter logic with no overly fragmented splits.
- An incremental indexing task can be initiated, to check whether the task log includes the "incremental synchronization" identifier and no full reindexing prompts.
- After configuring metadata filtering rules, a retrieval with industry tags can be initiated, to verify that results only include research reports from the target industry.
- Vector database index statistics can be reviewed, to confirm that the number of stored vector chunks matches the actual parsed document character count.
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.