What the Data for This Category Includes
Financial marketing content data for coal chemical enterprises primarily comes from internal coal quality inspection ledgers, sales contact records, public industry supply and demand information, and in-house promotional materials accessed by financial institutions partnering with these enterprises. Data update rhythms vary by business scenario: coal quality parameters are updated with batch test results, customer contact records sync in real time, and industry news updates weekly. Individual documents are mostly structured long texts, containing fields such as coal type classification, origin information, calorific value parameters, and downstream chemical application descriptions. Some supporting promotional materials include image-text product introductions.
What Constraints These Characteristics Impose on Deployment and Upgrade
The multi-source, heterogeneous nature of financial marketing content targeting coal chemical enterprises requires adapting to diverse data source formats during deployment, including structured enterprise ledgers, real-time contact records, and web-based industry news. The presence of long document texts requires adjusting document parsing chunk thresholds during upgrades to prevent loss of professional term context, which would undermine accurate matching of financial solutions. Differences in data update frequencies require configuring differentiated synchronization tasks: real-time customer contact records use incremental synchronization, coal quality parameters can be configured for full batch updates, and industry news uses weekly scheduled synchronization tasks to maintain the timeliness of financial marketing content. Additionally, the dense professional term structure of these documents requires updating custom vocabularies during upgrades to improve retrieval accuracy for coal chemical enterprises and optimize the effectiveness of financial solution recommendations.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Coal chemical documents are mostly long texts, requiring sufficient time for complete parsing |
maxChunkSize | 800–1200 characters | Balances context integrity and retrieval recall efficiency, adapting to long text chunking needs |
RECALL_COUNT | Top 8–10 results | Coal chemical professional content has high density, requiring sufficient recall volume to cover valid information |
AIPROXY_API_ENDPOINT | Intranet address and port of locally Docker-deployed M3E service | Configures access endpoint for local indexing models, adapting to local deployment requirements |
AIPROXY_API_TOKEN | Authentication key generated by the local service | Completes identity verification for the local indexing model, ensuring call security |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Documents such as coal chemical inspection reports may have large file sizes, requiring relaxed upload limits |
The parameter values provided on this page are standard recommendations for establishing initial configuration baselines. Actual values are affected by material format, data volume, and business rules. Specific scenarios require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: An incorrect local service address is entered when configuring
AIPROXY_API_ENDPOINT, resulting in failed calls to the indexing model, with the interface displaying the "model connection timed out" error. Cause: The intranet address and port of the local Docker container are not correctly entered into the configuration item, and container port mapping is not enabled. - Phenomenon: Code execution nodes on SaaS version 4.8 and above return a 500 status code during operation, with logs showing "specified execution environment not found". Cause: Corresponding dependency packages for code execution are not bound within the platform, or permission configuration for code execution nodes is not enabled.
- Phenomenon: After uploading long coal chemical documents, retrieval results only return a small number of fragments, with professional fields missing. Cause: The
maxChunkSizeconfiguration is not adjusted, and the default chunk length is too short, leading to loss of complete professional term context after splitting, which reduces retrieval matching accuracy.
How to Confirm Configurations Are Correct
- Upload a coal chemical professional document, check the parsed chunk results, and verify that the chunk length matches the configured value of
maxChunkSize. - Initiate a retrieval request, check that the number of returned recall results matches the configured value of
RECALL_COUNT. - Call the local M3E indexing model, check that platform logs show normal connections, with no timeout or authentication errors.
- After configuring a scheduled synchronization task, check that synchronization logs trigger according to the preset cycle, and data updates meet expectations.
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.