Page Overview
This page covers the following:
- Business direction: Investment research knowledge base construction and governance
- Capability area: Knowledge base retrieval and recall
What the data for this use case looks like
Investment research data comes from internal group subsidiary financial reports, cross-industry research reports, regulatory policy documents, group strategic planning documents, and external industry databases. Update cadences vary. Subsidiary financial reports are updated quarterly and annually. Industry research reports are updated in real time alongside industry developments. Policy documents are released on an irregular basis. Document structure includes fields such as industry classification, subsidiary revenue share, ownership percentage, and risk warnings. Some long documents include cross-segment business association analysis. Units include professional investment research units such as hundreds of millions of yuan and percentage points.
Constraints Imposed by Data Characteristics on Knowledge Base Retrieval and Recall
Multi-source and heterogeneous data sources require distinguishing weights between different sources during retrieval. This prevents non-authoritative external data from interfering with investment research judgments. A high proportion of long documents with cross-segment association content requires retaining complete semantic units during chunking. This avoids truncating cross-subsidiary business logic. Fields include professional investment research indicators. Retrieval and matching must prioritize semantic association, combined with keyword matching logic. This ensures recalled content aligns closely with investment research needs. Scattered update cadences require support for incremental updates triggered by business nodes. This ensures the timeliness of recalled real-time data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
segment_length | 800–1200 characters | Investment research documents often include complete analysis of cross-subsidiary business associations. This length retains semantic units and avoids truncating key associated content |
recall_count | Top 10–15 results | Investment research data has high relevance. Too many recalled entries introduce irrelevant information. Too few may miss cross-segment associated investment research content |
similarity_threshold | 0.72–0.80 | Investment research documents are dense with professional terminology. This threshold filters low-match noise content while retaining weakly matched results from cross-segment associations |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large group annual reports and cross-segment research reports have large file sizes. This duration ensures complete parsing without timeout interruptions |
incremental_update_trigger_rule | Trigger based on file modification time + subsidiary financial report release nodes | Data update nodes are scattered. Triggering by business nodes ensures the timeliness of investment research data |
reorder_return_count | Top 5–8 results | Investment decisions require precise core information. Reordering filters redundant associated content and improves result readability |
The parameter values provided on this page are common starting points for configuration. 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 Misconfigurations
- Phenomenon: Calling the knowledge base API returns a file access link. Clicking the link returns an error message containing "Only support .txt". Cause: Uploaded investment research documents have not completed compliant format conversion, or encrypted PDF or Word files have been uploaded without passing the platform-supported parsing process.
- Phenomenon: A large number of irrelevant administrative documents appear in retrieval results. Cause: No field-level retrieval filter rules are configured. Investment research documents are not classified and distinguished from non-target documents such as administrative notifications. This leads to recall of non-investment research content.
- Phenomenon: Semantic breaks appear in retrieval results. It is impossible to associate subsidiary business with overall group strategy. Cause: Custom chunk length is set too short. This truncates complete semantic paragraphs containing cross-entity association information.
How to Verify Proper Configuration
- Upload a group annual strategic report. View the parsed chunk preview. Confirm that chunks retain complete paragraphs with cross-subsidiary business associations.
- Initiate an investment research query involving cross-segment associations. Check if recalled results include relevant content from corresponding subsidiary financial reports and industry research reports. Confirm the count falls within the preset range.
- Call the knowledge base API to obtain a file access link. Click the link to confirm normal opening of the corresponding document with no format errors.
- View the similarity scores of retrieval results. Confirm scores fall within the preset threshold interval. No abnormally high or low results should appear.
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.