What data for this category looks like
Data for this category comes primarily from compliant financial research report sources and public industry analysis documents, with daily incremental sync updates. Most individual documents are in PDF format, some with embedded structured tables. Documents include fields such as report title, publishing institution, publish time, core conclusions, industry indicators, target ratings, and more. Target ratings use standardized identifiers. Industry indicators are measured in hundreds of millions of yuan. Document word counts range from thousands to tens of thousands of characters. Each document has a unique internal identification number.
What constraints these characteristics impose on knowledge base retrieval and recall
Daily incremental updates require the retrieval pipeline to support incremental sync and incremental recall, avoiding resource consumption from full re-scans. Mixed multi-format document structures require the parsing module to support nested table extraction and text segment alignment, ensuring complete extraction of key indicators and ratings. Standardized fields and units require recall results to complete field normalization, preventing differences in indicator descriptions across sources. Wide variation in long document word counts requires segmented retrieval to adapt to a reasonable context window, stopping core information from being truncated. Unique document identifiers require recall results to attach original document tracing information, ensuring result traceability.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Research report documents are generally long, so sufficient parsing time must be reserved to extract long text and nested tables |
Segment Length | 800–1200 characters | Research reports contain long text and embedded tables. This length balances context completeness and retrieval accuracy |
Recall Count | Top 8–12 results | Comprehensive service research reports cover multi-dimensional business perspectives. An appropriate number of recall results can cover information needs across different sub-sectors |
Similarity Threshold | 0.72–0.85 | Research report content is highly professional. This range filters low-relevance results while retaining matching content from specific sub-sectors |
Incremental Sync Interval | 1 hour | Adapts to daily incremental update requirements, balancing real-time performance and server load |
Reranked Return Count | Top 5 results | Final displays require concise, valid information. Reranking retains the most relevant 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. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- The symptom is that retrieval result ranking does not follow text similarity priority, with some low-relevance documents appearing at the top. The cause is that the reranking step is not enabled, or the professional domain fine-tuning parameters of the reranking model are not adapted to research report scenarios.
- The symptom is that after uploading an XLSX-format research report table, only the file name is retained in the knowledge base, with no table text content. The cause is that the embedded table parsing configuration item is not enabled, or the uploaded file exceeds the
UPLOAD_FILE_MAX_SIZElimit. - The symptom is that an error is triggered when configuring the embedding model without selecting
text-embedding-ada-002, and the error message containsundefined model must match "^(text. The cause is that the embedding model name does not match the official regular verification rule, or the locally deployed model file is not loaded correctly.
How to Confirm Configurations Are Set Correctly
- Upload a standard XLSX research report document containing nested tables. Check if parsed text fragments include table indicators and rating fields, to confirm the table parsing configuration is effective.
- Initiate a retrieval using professional research report terminology. Check result ranking and field integrity, to confirm recall count and similarity threshold configurations meet business requirements.
- Trigger an embedding model test call. Confirm no model verification error is returned, to verify the embedding model configuration is correct.
- View incremental sync task logs. Confirm daily research report incremental files have been correctly synced to the knowledge base, with no timeout or parsing failure records.
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.