What Data Looks Like for This Category
Multi-financial investment research data sources include public regulatory disclosure documents, third-party industry research reports, internal institutional investment research minutes, and product issuance and ongoing announcements. Update frequency varies by content type: regulatory documents are released upon event triggers, industry research reports are updated on a fixed schedule, and internal minutes are generated as needed. Documents fall into two categories: structured and unstructured. Structured documents include fields such as product code, outstanding size, net value benchmark, with units including yuan, 100 million yuan, and others. Unstructured documents are primarily paragraph-based text, with attached metadata such as marked target names and rating conclusions.
Constraints on Knowledge Base Retrieval and Recall
The multi-source heterogeneous data structure requires the retrieval and recall process to support a hybrid logic of vector semantic matching and exact keyword matching, to avoid missed recalls for structured fields such as product codes. Differences in update frequency require support for event-triggered incremental synchronization mechanisms, to meet the real-time update needs of regulatory documents, while retaining full synchronization options for historical data completion. The presence of standardized fields requires configuration of exact matching rules for codes and target names, to improve recall accuracy for content related to specific targets. The wide range of document lengths requires configurable segmentation parameters to adapt to the content density of different documents, avoiding truncation of key information in long documents.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall Count | Top 10-15 | Multi-financial investment research documents cover multiple types such as research reports, announcements, and minutes. Sufficient associated information must be recalled to avoid missing key target-related content due to too few results |
Similarity Threshold | 0.72-0.85 | Investment research scenarios have high requirements for content relevance. This interval can filter low-relevance generic matching results while retaining sufficient valid recall content |
Segment Length | 800-1200 characters | Multi-financial documents have a wide range of lengths. This segment length balances context completeness and local semantic relevance, adapting to the content density of research reports and minutes |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing long documents such as complete industry research reports takes a long time. This duration avoids parsing failures due to timeouts, adapting to the size characteristics of multi-financial documents |
Exact Match Fields | Product Code, Target Code | Investment research scenarios require accurate recall of documents related to specific targets. Configuring exact matching for standardized fields improves recall accuracy |
Incremental Sync Trigger Rule | Triggered by file update time | The update frequency of multi-financial data varies significantly. Triggering by update time can accurately synchronize the latest regulatory documents and research reports, avoiding ineffective synchronization |
The parameter values provided on this page are conventional recommendations used as a starting point 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 Configuration Mistakes
- Phenomenon: Multiple specified files cannot be selected for retrieval at once in the retrieval interface; only full-library or single-file retrieval is supported. Cause: Batch retrieval configuration for the knowledge base is not enabled, and the FastGPT default retrieval mode does not adapt to the need to selectively retrieve documents related to partial targets on demand in investment research scenarios.
- Phenomenon: Automatic synchronization fails after configuring a Feishu data source; only manual file upload is supported. Cause: The incremental synchronization rule triggered by file update time is not configured, and only full synchronization mode is used, which cannot adapt to the differentiated update rhythm of multi-financial data.
- Phenomenon: Retrieval requests return an error indicating request frequency limit exceeded. Cause: Current limiting rules for retrieval requests are not configured, and the high-frequency request feature of batch retrieval in investment research scenarios is not considered, resulting in triggering the system's default frequency limit.
How to Verify Successful Configuration
- Access the data source management page of the knowledge base, confirm that the synchronization status of external data sources such as Feishu is connected, and that the incremental synchronization trigger rule has been set to trigger by file update time.
- Enter a specific target code for retrieval, check whether the recall results include documents related to the target, and that the number of results matches the configured
Recall Count. - Upload a research report document with a length of more than 10,000 characters, check whether it is correctly segmented after parsing, with no key content truncated.
- Initiate multiple consecutive retrieval requests, confirm that no errors related to frequency limit are returned, and that the system can normally handle batch retrieval requirements.
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.