What data for this category looks like
Advertising and marketing research report data primarily comes from financial institution media monitoring systems, brand owner (including financial, insurance, wealth management entities) marketing review documents, public reports from industry marketing associations, and third-party advertising data platforms. Updates follow a regular monthly and quarterly rhythm, with temporary additional documents generated alongside industry events or policy changes. Single documents have wide character count ranges. Their structure includes core marketing conclusions, channel placement data, budget allocation details, and audience profile analysis. Fields include placement amount, cost per thousand impressions, click-through rate, covered audience scale, and more. Each field has corresponding units.
What constraints these characteristics impose on deployment and upgrade
The long-text nature of advertising and marketing research reports requires adjusting text parsing and indexing parameter thresholds during deployment, to avoid truncating core marketing conclusions and data. The non-fixed update rhythm requires supporting incremental synchronization and rapid temporary document import during upgrades, to adapt to access requirements for sudden industry documents. The structure with multiple fields and specific units requires configuring field parsing rules during deployment, to ensure unit information is retained during retrieval and avoid confusion. The wide character count range per single document requires optimizing word segmentation and recall matching logic during upgrades, to adapt to research report content of different lengths.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Advertising and marketing research reports have high per-document character counts, with long regular parsing times. This avoids premature termination of the parsing process |
Chunk size | 800–1200 characters | Adapts to long paragraph marketing data and conclusions in research reports, avoiding truncation of key information |
Recall count | Top 8–12 results | Balances the recall range and result relevance for research report retrieval, adapting to matching requirements for multi-dimensional marketing data |
Similarity threshold | 0.75–0.85 | Filters low-relevance non-marketing documents, retaining research report content that highly matches retrieval keywords |
EMBEDDING_BATCH_SIZE | 16–32 | Adapts to embedding calculation load for batch parsing of research reports, avoiding server resource overload |
Incremental sync interval | Calibrated via actual testing | Adapts to research report data sources with non-fixed update rhythms, can be flexibly adjusted to on-demand synchronization or hourly synchronization |
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 values.
Three common mistakes
- Phenomenon: High disk usage after Docker deployment, temporary files generated during research report parsing are not automatically cleaned. Cause: No temporary file mounting and automatic cleanup rules configured for the Docker container, leading to a large number of parsing cache files occupying storage resources.
- Phenomenon: HTTPS loading failure when accessing the configured service via the frontend, unable to access via custom domain. Cause: No SSL certificate and reverse proxy rules configured during deployment, only HTTP ports are enabled, not adapting to HTTPS access requirements.
- Phenomenon: Low relevance of retrieval results, and unable to match content related to the specified
阿狸-emb3model. Cause: No correct embedding model identifier specified in the configuration, or no weight files for the corresponding model pulled, resulting in use of the default model, not the model adapted for advertising and marketing scenarios.
How to confirm correct configuration
- Upload a typical advertising and marketing research report, check the parsing log, confirm that parsing does not trigger a timeout error, and field metadata is fully retained.
- Initiate a retrieval test, verify that the number of returned results falls within the range specified by the
Recall countconfiguration, and that the similarity meets the set threshold requirements. - Configure an incremental synchronization task, upload a new research report document, confirm that the system only synchronizes new content and does not repeatedly import existing documents.
- Access the configured HTTPS domain name, confirm that the frontend page loads normally, and all API requests are transmitted via encrypted channels.
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.