What data for this category looks like
Data sources for brand agency operation intelligent due diligence reports include monthly operation review documents provided by brands, transaction and traffic data from e-commerce platform store backends, fan interaction reports from social media accounts, and brand compliance qualification filing documents. Data update rhythm is divided by agency service cycles. Full datasets are synchronized every quarter or month. Daily newly added placement records are updated each day. Documents primarily use a structure combining structured tables and unstructured summaries. Table fields include category of beauty and personal care brands, placement channels, impressions, unit price, compliance filing number, and agency service cycle. Corresponding units are times, yuan, string, month/year.
What constraints these characteristics impose on model access and configuration
Mixed-structure documents require adaptation to both structured table parsing and unstructured long text embedding, to avoid segmentation strategies that fail to balance short fields and long summaries. Data with multiple update rhythms requires flexible switching between full and incremental synchronization, to adapt to data source updates across different cycles. Documents containing commercially sensitive fields require configured compliant desensitization rules, to prevent sensitive information leaks. Data from multiple sources requires adaptation to interface formats of different platforms, to avoid format mismatch issues during access. Long text summary documents also require larger model context windows, to ensure logical coherence of due diligence report generation.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
split_chunk_size | 800–1200 characters | Adapts to the mixed structure of brand agency operation due diligence reports, which combine short-field tables and long-text summaries, avoiding overly fragmented segmentation that loses context or overly long segments that cannot be embedded |
RECALL_TOP_N | Top 8–12 entries | Covers core information of multi-channel operation data, balancing recall accuracy and result length |
SENSITIVE_FIELD_MASK | Enabled, desensitize compliance filing number and unit price fields | Due diligence reports contain commercially sensitive data, meeting compliance requirements |
SYNC_CYCLE | Full sync every quarter + incremental sync daily | Matches the periodic update rhythm of agency service cycles and the update frequency of daily placement data |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time of full operation ledgers, avoiding parsing failure for large documents |
MODEL_MAX_TOKENS | 16384 | Meets generation and parsing requirements for long-text due diligence reports |
The parameter values provided on this page are common recommended starting points 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 mistakes
- Phenomenon: In multi-turn due diligence conversations, subsequent replies cannot associate previously mentioned brand category or placement channel keywords, resulting in context loss. Cause: The
maxContextparameter is not configured, or its value is too small to cover the context window for multi-turn conversations. - Phenomenon: Calling an external model returns a 400 error, prompting model parameter mismatch or connection failure. Cause:
MODEL_API_BASEandMODEL_API_KEYare not configured correctly, or access protocol parameters for different models are mixed. - Phenomenon: Generated due diligence reports include the model's internal thought process logs, causing report format confusion. Cause: The model's debug output switch is enabled, and this option is not turned off in the configuration.
How to confirm successful configuration
- Upload a sample document of a brand agency operation due diligence report, check if parsed segments meet expectations, and adjust the
split_chunk_sizevalue until it matches the document's structural characteristics. - Initiate a multi-turn conversation, mention different operation channels and brand categories in sequence, verify if subsequent replies associate previously mentioned keywords, and adjust the
maxContextvalue until context association works correctly. - Call the model to generate a simplified due diligence report, check if sensitive fields have been desensitized, and verify that the
SENSITIVE_FIELD_MASKconfiguration takes effect. - View model call logs, confirm no additional internal thought steps are included in returned results, and verify that the debug output switch is turned off.
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.