Deployment and Upgrade for Research Report Retrieval

What this type of research report data looks like

Data sources include in-house developed business research report platforms, analysis documents required for submission by regulatory authorities, and public results of industry surveys. Regular research reports are released quarterly. Temporary analysis documents are updated in line with regulatory requirements or business changes. Document structure includes a unified document identifier, publishing entity, release time, business module chapters, and risk reminder module. Fields include research report number, business sector classification, core business indicator values, and business recommendation module. Industry-standard currency units and business scale units are used.

What constraints these characteristics impose on deployment and upgrade

Multiple data source integration requires configuring cross-system access whitelists and permission verification rules to prevent data pull failures. Fixed update schedules require configuring scheduled synchronization tasks adapted to quarterly cycles, and reserving manual trigger update entrances to handle temporary documents. Unified field structures require configuring custom parsing rules to match research report field formats, avoiding missing or misaligned fields after parsing. Long document lengths require adjusting context window and chunk processing parameters to ensure complete content retention. Sensitive business content processing requires configuring compliant content filtering parameters to meet industry regulatory requirements.

Configuration recommendations

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 case-by-case analysis, and testing should be conducted on available samples before finalizing settings.

Three common mistakes

• Symptom: Deployed in a cloud environment, the knowledge base response time exceeds the preset threshold, and logs show long time consumption during the vector recall phase. Cause: The RECALL_BATCH_SIZE parameter was not adjusted for the long text characteristics of research report documents, leading to too many document chunks recalled in a single operation, increasing network transmission and computing overhead.
• Symptom: After the reranking model is deployed, verification via the test interface shows normal functionality, but every retrieval returns false for reranking results. Cause: The correct custom request address was not filled in the FastGPT reranking configuration, or the RERANKER_API_KEY parameter was not configured to complete identity verification.
• Symptom: After upgrading fastgpt-mcp-server, the platform fails to start, and the console returns a port occupied error. Cause: The port mapping parameters in the original configuration file were not retained during the upgrade process, and the newly started service occupied a port already occupied by other components.

How to confirm the configuration is correct

• Upload a typical research report document, check if the parsed fields match the preset custom parsing rules, and confirm that the field mapping configuration takes effect.
• Execute a manual synchronization task, check if the synchronization logs show that all configured data sources have successfully pulled documents, with no permission or connection errors.
• Initiate a research report retrieval request, check if the reranking marker in the returned results is true, and confirm that the reranking model's request address and identity verification configuration are correct.
• View the platform's performance monitoring dashboard, confirm that the time consumption of the vector recall and reranking phases meets business expectations, and adjust relevant parameters to a reasonable range.

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.