What data for this category looks like
Data sources for comprehensive service research report retrieval include compliant financial news APIs, brokerage research report databases, and public APIs from industry research platforms. Update rhythm follows real-time pushes each workday, with no updates on non-workdays. The structure of a single research report includes modules such as title, publishing institution, publish time, core insights, industry benchmark data, target analysis, and appendices. Fields include 发布机构 (string type), 发布时间 (ISO 8601 format timestamp), 标的代码 (6-digit numeric string), 研报类型 (enumerated value). Revenue, valuation and other data fields use standard units such as ten thousand yuan and multiples respectively.
Constraints imposed by these characteristics on HTTP interfaces and external systems
Data sources are scattered and mostly third-party authorized APIs. External systems must configure independent authentication rules to prevent unauthorized access. Single research reports can be tens of thousands of words long. HTTP interfaces must support paged returns and segmented pulling to avoid timeouts caused by excessive single-transmission data volume. There are many structured fields that require strict validation. Interfaces must provide field-based filtering parameters to ensure retrieval accuracy. Update frequency is high. Interfaces must support incremental pulling modes to balance real-time performance and server load.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Research Report Data Source API Key | custom random string | Complies with authentication specifications for financial data interfaces, avoid using default test tokens |
Maximum Research Reports per API Response | top 20 entries | Adapts to front-end paging display logic, reduces single interface transmission load |
Research Report Content Chunk Size | 800–1200 characters | Adapts to context window limits of mainstream large models, ensures semantic integrity of single segments |
Incremental Sync API Pull Interval | 300 seconds | Matches workday research report update rhythm, balances real-time performance and server resource usage |
API Timeout Threshold | 60 seconds | Covers typical response durations of third-party data source interfaces, avoids long request blocking |
Structured Field Validation Toggle | enabled | Filters invalid research report data, ensures field consistency for data passed to the RAG system |
Three common errors
- Symptom: Calling the HTTP interface returns
401 Unauthorized. External system logs show the token isfastgpt. Cause: Failed to correctly configure the third-party data source API Key, and the built-in FastGPT test token was used by default. - Symptom: After configuring the
gpt-4omodel on the external model scheduling platform, FastGPT page calls fail and return a model not found prompt. Cause: Did not fill in the correct external interface address and model identifier in FastGPT's model configuration, only filled in the generic model name. - Symptom: After restarting Docker containers on an Ubuntu system, executing SQL queries throws a permission error. Cause: Did not synchronize mounted data volume permissions, causing the FastGPT process to fail to read locally cached research report metadata.
How to confirm successful configuration
- Call the test HTTP interface, pass a valid API Key and filtering parameters, check that the returned HTTP status code is
200 OK. - View the external data source synchronization logs, confirm that the number of incrementally pulled research reports matches the expected update count.
- On the FastGPT model configuration page, test calling the bound external model interface, confirm that a normal model response is returned.
- Execute a local SQL query, confirm that the FastGPT process has read and write permissions for the corresponding database tables.
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
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.