Deployment and Upgrade of Investment Research Knowledge Base

What data for this category looks like

Industrial park investment research data comes from multiple sources: park operation ledgers, investment promotion brochures, public financial reports of settled enterprises, local industrial policy documents, and quarterly operation briefings. Data updates follow no fixed cycle. Changes to settled enterprises or policy revisions trigger immediate updates. Investment promotion brochures and planning documents update with project iterations. Document formats include structured tables (such as settled enterprise lists and per-mu tax statistics), unstructured long-text policy files, and location planning materials with combined text and images. Unique fields include "per-mu tax of settled enterprises", "park plot ratio", and "policy redemption cycle". Corresponding units are ten thousand yuan/mu, percentage, and days.

Constraints on deployment and upgrade phases

The multi-source mixed data structure requires deployment phase support for both structured table parsing and unstructured long-text parsing. This avoids field omissions and semantic fragmentation. The lack of a fixed update cycle requires flexible adjustment of incremental index trigger logic during the upgrade phase. This balances data timeliness and server resource consumption. Unique fields and units require dedicated entity extraction rules during deployment. This prevents general parsing models from confusing industrial park-specific terminology. The high proportion of long documents requires adjustments to parsing timeout and segmentation parameters. This avoids task interruptions.

Recommended configuration settings

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Long documents such as park planning documents and annual reports of settled enterprises take longer to parse. This setting avoids task interruptions due to timeout
`UPLOAD_FILE_MAX_SIZE`	`1000 MB`	Single files such as park investment promotion brochures and industrial planning reports have large file sizes. This setting adapts to large file upload requirements
`EMBEDDING_MODEL`	`ali-emb3`	Investment research scenarios have high semantic accuracy requirements for industrial policies and enterprise financial report texts. This model supports vertical domain vector generation
`chunk_size`	`800–1200 characters`	The core paragraph length of park operation reports and policy documents fits this segmentation range. This avoids semantic fragmentation
`recall_top_k`	`Top 8–12 results`	Investment research scenarios need to balance recall breadth and accuracy. This avoids excessive irrelevant documents interfering with retrieval results
`AUTO_SYNC_INTERVAL`	`Every 12 hours`	Park data updates follow no fixed cycle. This frequency balances data timeliness and server resource consumption

The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material formats, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.

Three common configuration errors

Symptom: Front-end access addresses cannot switch from HTTP to HTTPS. Browsers prompt "Connection is not secure" or return 403 status codes. Cause: Reverse proxy SSL certificates and forwarding rules are not configured correctly. FastGPT container port mapping and environment variables are not modified.
Symptom: Index tasks time out and fail. The console returns 504 Gateway Timeout errors. Cause: The PARSE_FILE_TIMEOUT_SECONDS parameter is not adjusted. Long document parsing time exceeds the default threshold.
Symptom: Unit confusion appears in recall results. For example, "per-mu tax" is identified as "per-square-meter tax". Cause: Entity extraction rules for industrial park-specific fields are not configured. General parsing models cannot accurately identify specific terminology and units.

How to confirm configurations are properly applied

Upload a park industrial planning PDF. Check if parsed text segmentation meets expectations. Verify segmented character counts match the configured chunk_size range.
Initiate an incremental sync task. Check if the sync log includes newly added park operation data. Confirm the sync interval complies with the configured AUTO_SYNC_INTERVAL rule.
Initiate an investment research query. Check if the number of returned recall documents matches the configured recall_top_k range. Verify the vector model uses the configured EMBEDDING_MODEL.
Access the configured domain name. Confirm a secure lock icon displays in the browser address bar. Verify HTTPS access works 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.