What the Data for This Category Looks Like
Investment research data for large state-owned commercial banks primarily comes from internal compliance research reports, public documents from central banks and regulatory authorities, monthly statistical data from industry associations, and macroeconomic database interfaces. Update cycles vary significantly: macro indicator data is updated daily or weekly, regulatory policy documents are released in real time, and internal investment research reports are updated quarterly or monthly. Most documents are a mix of structured and unstructured formats. Structured sections include report numbers, issuing institutions, effective dates, and economic indicator fields with units. Unstructured sections contain main body analysis content, and some internal documents have permission level identifiers.
Constraints Imposed on Deployment and Upgrade by These Characteristics
The multi-source nature and differentiated update cycles of investment research data for large state-owned commercial banks require configuring multiple access adapters during the deployment phase to support a mixed update mode of scheduled pulling and event triggering. Fixed fields and unit requirements for structured data require presetting field verification rules during deployment to prevent import failures caused by format incompatibility. Permission level identifiers in internal documents require updating the vector database’s permission filtering logic during upgrades to ensure retrieval scope complies with internal compliance requirements. Individual internal research reports have long lengths, so document processing parameters must be adjusted to adapt to long texts to avoid loss of key analysis content during the embedding phase.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600–900 seconds | Internal research reports have long lengths, with longer parsing times than general documents. Extending the timeout period prevents parsing interruptions |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Individual investment research reports from large state-owned commercial banks have large file sizes, with some reports containing multi-page charts and data attachments. Raising the upload upper limit is required |
maxContext | 8000–12000 characters | Long research report contexts need to retain more historical information to adapt to multi-turn conversation requirements in investment research scenarios |
recall count | Top 10–15 results | Investment research scenarios require covering multi-dimensional data. A higher number of recalled entries improves information comprehensiveness, while combining reranking to optimize relevance |
similarity threshold | 0.75–0.85 | Balance retrieval accuracy and coverage to avoid missing relevant regulatory policies or industry data, while filtering low-relevance non-investment research content |
PARSE_SEGMENT_LENGTH | 1500–2000 characters | Long research report segmentation must retain complete paragraph logic to avoid splitting that disrupts analysis context, and adapt to the input length limits of embedding models |
The parameter values provided on this page are standard recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Each specific case requires individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Issue: A 500 error is returned when uploading large investment research reports. Cause: The
UPLOAD_FILE_MAX_SIZEconfiguration item was not adjusted. The default upper limit is insufficient for research report files with attachments. - Issue: Frequent timeouts occur when scheduled pulling of macro data or parsing long documents. Cause: The value of
PARSE_FILE_TIMEOUT_SECONDSwas not extended. The default timeout period cannot accommodate batch pulling and parsing of multi-source data. - Issue: A connection refused prompt appears when configuring vllm as an embedding or generation model. Cause: The corresponding service port was not opened during deployment, or the correct service address and port were not filled in the FastGPT configuration.
How to Verify Successful Configuration
- Upload a single large research report that meets the configured upper limit, and check that the parsing progress completes normally with no 500 error prompts.
- Initiate a conversation containing multi-dimensional investment research questions, and verify that the number and similarity of recall results fall within the preset configuration range.
- Test a scheduled pulling task for multi-source data, and confirm that the task executes without timeout interruptions, and that data is synchronized to the knowledge base within the expected time frame.
- Verify the callback address and permission configuration for enterprise-level access, and confirm that external systems can establish normal connections with the FastGPT service with no connection refused errors.
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.