What This Category’s Data Looks Like
Airport financing daily report data is sourced from public disclosure documents issued by civil aviation industry regulators, internal financial accounting systems of airport groups, and financing receipt confirmations from cooperating financial institutions. The update cadence is daily T+1, which covers both current and historical daily report data. Each document is organized by airport operating entity, and includes fields such as financing date, financing type, approved quota, actual received amount, corresponding infrastructure project number, name of the lending bank, and more. All monetary amounts use RMB yuan as the unit. Date fields follow the YYYY-MM-DD format.
Constraints for HTTP Interfaces and External Systems
Scattered data sources require interfaces to support multi-source docking. Interfaces must be compatible with API authentication formats from different financial institutions and private interface protocols used by internal financial systems. The daily T+1 update cadence requires scheduled pull tasks to run at fixed 24-hour intervals. Duplicate request deduplication logic must be configured to avoid creating duplicate knowledge base entries. The structured multi-field requirement means interface request parameters must validate financing date formats and monetary value ranges. Interfaces must also support filtering by airport operating entity ID and financing type, to meet personalized data pull needs of different airports.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
api_request_timeout | 600 seconds | Airport financing daily reports have large per-batch data volumes with multiple financing details. This timeout duration covers the full data pull process |
default_embedding_model | text-embedding-3-large | Financing daily reports include long-form content such as infrastructure project descriptions. This model’s long-text vector representation fits the scenario requirements |
vector_db_provider | Milvus | This scenario requires combined vector recall with structured fields such as airport entity ID and financing date. Milvus supports structured filtering combined with vector retrieval |
api_auth_type | bearer_token | Interfaces from cooperating financial institutions commonly use this authentication method, enabling fast cross-system docking |
parse_chunk_size | 800–1200 characters | The text length of individual financing details is moderate. This chunk size preserves context integrity and avoids semantic breaks |
max_batch_import_count | 50 entries | Too many entries in a single import will trigger interface rate limits. Limiting the number of requests per batch improves data synchronization stability |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: When calling the data synchronization interface to generate knowledge base indexes, the vector model automatically changes to
text-embedding-3-large, which does not match the presettext-embedding-ada-002. Cause: Thedefault_embedding_modelparameter is not fixed, and the system uses the latest version model by default for updates. - Symptom: Unable to connect to the private large model service of cooperating financial institutions via the HTTP interface. Cause: Parameters such as
custom_llm_endpointandcustom_llm_api_keyare not entered in the system configuration, and authentication configuration for third-party interfaces is not completed. - Symptom: When using PostgreSQL as a vector database, the recalled financing daily report entries have low matching accuracy with query conditions. Cause: A joint index is not created for structured fields such as airport entity ID and financing date. Relying only on vector similarity recall cannot accurately filter data.
How to Verify Successful Configuration
- Send a single data pull request, confirm that the returned HTTP status code is
200 OK, and that the returned fields include the preset core financing fields. - Check the knowledge base configuration page, confirm that the
default_embedding_modelparameter matches the preset model name. - Run a batch import task, verify that the number of imported entries matches the value set for the
max_batch_import_countparameter. - Send a vector recall test, input a specified airport entity ID, confirm that the recalled results only include financing daily report data for that entity.
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.