What the data for this category looks like
Brand agency operation research and investment data includes multi-source structured and unstructured content. Structured data comes from brand sales ledgers and e-commerce platform transaction reports. It includes fields such as customer unit price, repurchase rate, and advertising conversion volume, with units including yuan and times. Unstructured data comes from e-commerce reviews, social media promotional content, and competitor marketing materials. The main formats are long texts, short video scripts, and image annotations. Update frequencies vary significantly: sales data is synchronized daily, public opinion data is updated in real time, and industry research reports and competitor activity materials are updated according to marketing cycles.
What constraints these characteristics impose on deployment and upgrade
Varying update frequencies for multi-source data require mixed configuration of streaming real-time synchronization and batch incremental synchronization during deployment. This avoids excessive cluster resource usage from full synchronization. The coexistence of structured and unstructured data requires configuring associated retrieval capabilities for vector databases and relational databases during deployment. This ensures structured fields can be accurately filtered, and unstructured content can be semantically retrieved. Data volume grows with the number of partnered brands. Upgrades must reserve sufficient storage and computing resources to avoid excessive load on single nodes. Field formats differ across data sources. Field mapping rules must be configured in advance to ensure uniform format during data import, and reduce subsequent cleaning costs.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Brand agency operation material files include long graphic reports and short video scripts, which are usually large in size. This setting adapts to batch upload requirements |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Parsing long documents such as quarterly industry research reports takes a long time. This prevents parsing tasks from being interrupted due to timeout |
maxContext | 800–1200 characters | Most research content is long text. Sufficient context must be retained to support accurate semantic retrieval |
Number of retrieved entries | Top 10 | Brand agency operation research data has multiple dimensions. A sufficient number of associated information must be retrieved to cover analysis needs |
Similarity threshold | 0.75–0.85 | Balance retrieval accuracy and coverage, to avoid irrelevant content being included in search results |
FASTGPT_API_TIMEOUT | 600 seconds | Multi-source data synchronization requires waiting for a long interface response time. This prevents synchronization tasks from failing midway |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on local test samples before finalizing settings.
Three common mistakes
- Issue: After modifying
ROOT_PASSWORDin a local deployment, login with the default account still fails. The cause is failing to synchronously update container environment variables or restart the FastGPT service. - Issue: After Docker deployment, accessing
http://localhost:3000results in a loading spinner followed by a failure. The cause is incorrect port mapping configuration or dependent services such as the vector database failing to start normally. - Issue: After configuring
CONTEXT_PATHas/fastgpt, the application fails to load static resources properly. The cause is failing to synchronously modify the reverse proxy path configuration, resulting in a mismatch between static resource request paths.
How to confirm the configuration is correct
- Upload a test document under 2000 MB, confirm that the parsing task completes normally with no timeout errors.
- Access the configured context path, verify that the application loads normally and static resources have no 404 errors.
- Initiate a research-related query, confirm that the similarity of retrieval results falls within the preset threshold range.
- Restart the service after modifying
ROOT_PASSWORD, verify that the new password can be used to log in to the backend management interface normally.
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.