What the Data for This Category Looks Like
Data for the advertising and marketing industry primarily originates from advertising backend platforms, media monitoring APIs, and placement segment data disclosed in corporate financial reports. Data update cycles cover three tiers: real-time placement metrics, weekly summary reports, and monthly financial report archives. Each data document includes structured fields such as channel type, placement period, budget consumption, conversion volume, and ROI. Units include RMB yuan, cost per thousand impressions, cost per click, and others. Some fields require association with advertiser subject ID and placement plan ID to complete unique identification and avoid data confusion.
Constraints Imposed on HTTP Interfaces and External Systems
The data characteristics of the advertising and marketing category impose three constraints on HTTP interfaces and external systems. First, real-time placement metrics require interface calls with millisecond-level response times to meet the immediate adjustment needs of placement optimization. Second, multi-dimensional associated fields such as channel ID and placement plan ID require interfaces to support multi-parameter combined queries, avoiding redundant overhead from full-volume data pulls. Third, data sources with different update frequencies require adaptive layered synchronization logic: real-time data is pushed via Webhook, weekly and monthly reports are pulled via scheduled batch interfaces, and standardized conversion of field units must be supported to ensure cross-system data consistency.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Advertising and marketing financial report documents typically include multi-channel placement details, with large individual file sizes, requiring adaptation to batch upload requirements |
PARSE_FILE_TIMEOUT_SECONDS | 1200 seconds | Long document parsing requires extended time to avoid interruptions to financial report data extraction due to timeout |
Recall count | Top 8–12 entries | Advertising and marketing financial reports have multiple field dimensions, requiring recall of sufficient associated segments to cover core metrics such as ROI and budget |
Similarity threshold | 0.72–0.78 | Filter low-relevance placement detail data, retaining valid content matching the financial report theme |
Model Redirect Configuration | Map by model alias | Adapt to API call requirements of different media data sources, unifying interface call formats |
VECTOR_MODEL_EMBED_DIM | 1024 | Match the embedding dimension requirements of bge-large-zh-1.5 to ensure the accuracy of vector retrieval |
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, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Incorrect binding of collection ID and file-id during file upload: The
collection_idreturned by the interface does not match the collection to which the uploaded file belongs. The cause is that the correct advertiser-specific collection ID and file identification parameters are not included in the request parameters. - Misunderstanding of model redirection configuration: The actual token consumption when calling
gpt-4o-miniexceeds expectations. The cause is that the actual called model for the redirection mapping is not clearly specified, and the quota calculation logic is mistakenly applied to the general model rules. - Mismatched vector model embedding dimensions: Knowledge base recall results are empty or have extremely low relevance. The cause is that a vector model with an embedding dimension inconsistent with bge-large-zh-1.5 is not used.
How to Verify Correct Configuration
- Send a single advertising and marketing financial report file upload request, and check that the
collection_idreturned in the response body matches the preset collection ID. - Call the quota query interface to verify that the cumulative values of
input_tokenandoutput_tokenmatch the pricing rules of the actual called model. - After importing the specified vector model, test embedding standard text, and check that the returned vector dimension matches the preset value.
- After configuring the model redirection rules, send a test call, and check that the actual called model name in the system logs matches the mapping rules.
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.