Data Characteristics of This Category
Data for this category of investment research knowledge bases includes multiple sources such as broker research reports, public industry operational data, internal enterprise research survey logs, and third-party industry news. Update frequencies vary widely: some reports update on their publication date, internal logs sync daily, and third-party news is pushed in real time. Document structures range from dozens of pages of long-text research reports, structured tables with numeric fields, to short industry updates. Fields include identifying fields such as publishing institution, publish date, and industry classification, plus numeric fields with units like revenue and production capacity.
Constraints for Model Access and Configuration
Multi-source heterogeneous data structures require the model access layer to support multiple parsing engines. These engines separately handle long text, structured tables, and short news. Data sources with different update frequencies need customized sync trigger rules. This avoids sync frequencies that are too high or too low. Parsing long documents requires adjusting timeout and chunking parameters to prevent parsing interruptions. Field extraction for structured data needs configured matching rules for corresponding fields. This ensures accurate unit recognition for numeric fields.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | General investment research documents for this category are mostly long research reports or batch-uploaded document packages, with generally long parsing times |
maxContext | 8000-12000 characters | Aligns with the context window limits of most commercial large models to avoid truncation of long documents |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports single large research reports or batch-uploaded document collections to meet multi-source data import requirements |
Chunk Length | 1500-2000 characters | Balances context coherence and recall accuracy, adapting to the splitting needs of long-text research reports |
Recall Count | Top 6-8 entries | Balances coverage of multi-source data and result redundancy, aligning with information filtering logic for investment research scenarios |
Similarity Threshold | 0.72-0.80 | Adapts to data sources mixing structured fields and unstructured text, avoiding false recalls or missed recalls |
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, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Model icon not displaying in workflows: Corresponding model cards appear blank when the interface loads the model list. This issue is common in version V4.14.3. The cause is failure to configure the API access whitelist for the model channel in environment variables.
- File upload prompts network error: A network error pop-up appears when uploading TXT or research report files, with a 504 status code returned. The cause is that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration value is lower than the actual parsing time. - Index model channel configuration failed: When creating a new index, selecting a model channel prompts "Channel unavailable". The cause is that the locally deployed model service port does not have cross-domain access permissions, or the API key is configured incorrectly.
How to Verify Successful Configuration
- Run a local curl request to test the configured model API. Check the returned response format and content, and adjust the timeout configuration based on response time.
- Upload a single typical document, such as a long research report or structured table. Check if the parsed fields are complete, and adjust the chunk length parameter based on the parsing results.
- Trigger an incremental sync task. Check the knowledge base update log to confirm that new data is correctly indexed, and adjust the sync cycle based on the business update frequency.
- Upload a test document in a workflow. Run a summary or question-and-answer node, check the recall quantity and relevance of the output results, and adjust the recall count and similarity threshold based on business needs.
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.