What Data for This Category Looks Like
Intelligent due diligence report data for investment platforms comes from multiple sources: target enterprise industrial and commercial registration records, periodic financial reports, industry regulatory announcements, public opinion data, and third-party compliance verification interfaces. Data update cycles differ significantly. Industrial and commercial information and financial reports are updated in batches on a quarterly or annual basis. Public opinion data is updated in real-time incremental batches. Compliance verification data is pulled on demand. A single report document contains structured fields including the target entity’s unified social credit code, revenue scale, shareholding ratio, risk level, and compliance filing number. Revenue is measured in ten thousand yuan. Shareholding ratio is measured in percentage. The length of individual report documents varies widely.
Constraints Imposed on HTTP Interfaces and External Systems
Due diligence data for investment platforms comes from multiple sources with inconsistent update cycles. HTTP interfaces must support two invocation modes: scheduled batch synchronization and real-time incremental data pulling. This prevents data source rate limiting from being triggered by improper call frequencies. There are numerous structured fields and compliance-sensitive data points. Interfaces must include built-in field integrity checks and permission verification logic. This stops invalid or non-compliant data from being written to the knowledge base. The length of individual report documents varies widely. Batch push interfaces must support data sharding for varying lengths. This prevents single requests from timing out due to excessive data volume. Third-party compliance interface calls require additional signature verification configuration. This ensures compliance during data transmission.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
pushData_max_items_per_batch | 150–200 items/batch | Matches the batch data splitting rhythm of due diligence reports. Avoids single requests exceeding default interface limits, and reduces the impact scope of batch failures |
collection_create_api_path | /api/core/dataset/collection/create | Follows FastGPT official standard interface paths. Meets the investment platform’s need to batch-create knowledge base collections for target entities |
model_response_format | json_object | Adapts to the need for structured output of due diligence reports. Ensures fields such as compliance information and financial data can be automatically parsed |
api_request_timeout | 300–600 seconds | Adapts to the large data volume of due diligence reports. Avoids timeout errors triggered by excessively long single request processing times |
ollama_connection_timeout | 120 seconds | Extends timeout duration for locally deployed Ollama models. Accommodates the latency characteristics of model loading and response |
api_concurrent_limit | 5–10 concurrent/second | Matches the rate limiting rules of third-party compliance interfaces. Prevents interface blocking from overly high concurrency |
The parameter values provided on this page are general recommendations to serve 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. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- A call to
/api/core/dataset/collection/createreturns 404 Not Found. The cause is failure to use the FastGPT official specified API gateway domain name, or accidental modification of interface path parameters. - A call to the
pushDatainterface returns only 100 successful write records. The cause is accidental configuration of a single batch data limit of 100 items, failure to match the batch push requirements of due diligence reports, or failure to adjust the parameter to the official specified 200-item limit. - An interface returns a connection error after configuring a local Ollama model, but the model address is accessible via a browser. The cause is failure to fill in the full Ollama API address (including port number) in FastGPT, or failure to enable a compatible response format in the model configuration.
How to Verify Successful Configuration
- Call the
/api/core/dataset/collection/createinterface with test data that matches the investment platform due diligence report format. Check that the returnedcodefield shows a success identifier. - Call the
pushDatainterface to push 200 sets of test data. Confirm that the number of successful returns matches the actual number of pushed items. - Check the model response format configuration. Trigger a single model call. Verify that the interface returned content complies with the
json_objectformat requirement. - Verify the Ollama connection configuration. Initiate a call through the FastGPT built-in model testing tool. Confirm that no connection error logs are generated.
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.