What the Data for This Category Looks Like
Data sources for comprehensive service category research report retrieval include compliant industry research documents, industry analysis reports released by securities research institutes, public research materials from industry associations, and similar sources. Updates follow an incremental sync rhythm when new documents go live. Full updates for existing documents are triggered on demand, with no fixed batch cycle.
Each single document has a structure including title, publishing entity, publish time, covered industry tags, main body analysis, and conclusion module. Fields include standardized document identifiers, publishing information, industry classification, and main body content. Some documents include indicator fields using industry-standard units, following information disclosure regulations of the corresponding industry.
Constraints for Vector Models and Indexing
Single research report main bodies have wide length spans and contain multi-chapter coherent argumentation. When performing segmented indexing, the integrity of core logical units must be retained, and cross-chapter argumentation content must not be split.
Differences in document formats across multiple sources require standardized mapping of meta-fields before indexing, to ensure that metadata such as industry tags are correctly identified as filterable index items.
The incremental update feature requires the indexing process to support incremental triggers, only syncing new or updated documents to reduce resource consumption of full indexing.
Text content dense with professional terminology requires the vector model to adapt to financial domain semantic features, avoiding semantic drift of general models for industry-specific terms that would affect recall accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CHUNK_SIZE | 800–1200 characters | Matches the paragraph length of research report main bodies, avoids splitting core argumentation units while maintaining semantic integrity of single vector blocks |
CHUNK_OVERLAP | 100–150 characters | Retains contextual association between adjacent segments, prevents cross-segment argumentation logic from being disconnected |
RECALL_TOP_N | Top 8–12 results | Balances recall accuracy and retrieval efficiency, adapts to scenarios where research report retrieval needs to cover multi-dimensional analysis |
SIMILARITY_THRESHOLD | 0.72–0.80 | Filters low-relevance non-professional text, retains research report content that highly matches query semantics |
ENABLE_INCREMENTAL_INDEX | Enabled | Adapts to the incremental update feature of research reports, reduces computational resource consumption of full indexing |
EMBEDDING_BATCH_SIZE | 32–64 | Balances vector generation speed and memory usage, avoids deployment environment overflow caused by overly large single batch processing |
The parameter values provided on this page are conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Knowledge base indexing progress stalls with no updates over long periods in Docker deployment environments. Cause: Incorrect configuration of vector model API access permissions, causing vector generation tasks to fail to execute normally and blocking the indexing process.
- Phenomenon: Chat function works normally but vector indexing shows no progress. Cause: A chat model is incorrectly used as the vector embedding model, and no dedicated embedding model interface is configured, making it impossible to generate document vectors.
- Phenomenon: Single long research report is split into too many invalid segments, resulting in contextual breaks during retrieval. Cause:
CHUNK_SIZEis set too small, and reasonable segment overlap is not retained, causing core argumentation logic to be split across multiple independent segments.
How to Verify Correct Configuration
- Upload a single standard research report, check the chunk preview interface, confirm that segment length matches the
CHUNK_SIZEconfiguration, and no core argumentation paragraphs are forcibly split. - Initiate a simulated query containing industry tags, check the retrieval result list, confirm that the number of recalled results matches the configured settings.
- Check indexing execution logs, confirm that only new or updated documents trigger vector generation and indexing processes, with no records of full repeated indexing.
- Test queries for non-target industries, confirm that retrieval results do not include irrelevant research report content, and the filtering logic takes effect.
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.