What Data for This Category Looks Like
IT service category research report data mainly comes from public reports of financial industry consulting institutions, technical white papers of financial vendors, internal technical project approval documents of financial enterprises, and public industry summit transcripts. Update schedules follow research report release cycles: regular quarterly updates, with temporary research reports added during sudden technical iterations. Each document includes fields such as title, publishing institution, release date, core technical parameters, and market size entries. Some documents contain embedded structured tables, with a wide range of character counts. Core parameter fields often have clear units, such as processing performance units and storage capacity units.
Constraints Imposed on Knowledge Base Retrieval and Reranking
Research reports come from scattered sources with diverse formats, including public web pages and internal documents. This creates a need for standardized processing of multi-source data to resolve inconsistent parsing requirements. Update frequencies are uneven, with both regular quarterly updates and sudden temporary updates. Incremental index updates must be used instead of full reindexing to avoid retrieval delays. Single-document character counts vary widely. Long documents must be split before indexing, and split granularity must align with research report chapter structures to avoid breaking the binding between technical parameters and their context. Core parameters have clear units. Retrieval must match semantics associated with units to prevent recall results where parameters and units are mismatched.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Aligns with the chapter length of financial IT service research reports, avoiding splitting that breaks the binding between core technical parameters and their context |
top_k | Top 8–12 results | Covers multi-dimensional information in research reports, preventing missing key technical details and market data in single recall results |
similarity_threshold | 0.72–0.85 | Balances semantic matching accuracy and recall coverage for professional terminology scenarios |
parse_timeout | 300 seconds | Accommodates parsing time for long research report documents, preventing parsing failures for large-volume documents |
batch_push_limit | 200 groups/batch | Matches the single-call limit of the knowledge base batch push interface, complying with public interface rules |
rerank_top_k | Top 3–5 results | Focuses on core research report content, reduces the information density of final returned results, and is suitable for Agent question-and-answer scenarios |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: The number of retrieved results is far lower than the configured
top_kvalue, or empty results are returned. Cause: Thesimilarity_thresholdvalue is not configured correctly, or index updates have not completed, resulting in the vector database not loading all data. - Phenomenon: The
pushDatainterface returns a413 Request Entity Too Largeerror. Cause: The number of data groups pushed in a single call exceeds the 200-group limit ofbatch_push_limit, and the push task was not split into batches. - Phenomenon: Internal Confluence pages cannot be parsed by the knowledge base, while external links can be parsed normally. Cause: The internal access whitelist or proxy node is not configured, and the parsing service cannot access the internal Confluence domain name, resulting in page crawling failure.
How to Confirm Proper Configuration
- Upload a single long research report document, view the segmented results after parsing, and confirm that the segmentation does not break the context association of core technical parameters.
- Call the
pushDatainterface to push 200 groups of data, confirm that the interface returns a successful status code, and there are no parameter limit error messages. - Initiate a retrieval request containing professional technical terms, check whether the recalled results include matching research report content, and adjust
similarity_thresholdto match business requirements. - After configuring the internal Confluence data source, perform a test parse, and confirm that the page content can be crawled and parsed normally.
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.