What the Data for This Category Looks Like
Data sources for industrial park intelligent due diligence reports include park operation management system filing ledgers, local natural resources department planning announcements, industrial and commercial registration information of settled market entities, monthly water and electricity energy consumption reports for the park, rent collection ledgers, and public facility operation logs.
Settled enterprise information is updated in real time as enterprises move in or out. Energy consumption and rent data are updated monthly. Planning-related data is updated annually.
A single due diligence report typically includes modules such as basic park overview, land ownership documents, settled enterprise list, revenue and tax data, supporting facility list, and risk prompts. Fields include covered area, number of settled enterprises, average rent price, total energy consumption, and more. Each field is paired with corresponding physical or business units.
Constraints Imposed on Vector Models and Indexing
Scattered data sources include both structured ledgers and unstructured announcement documents. This requires vector models to support multimodal input adaptation, and indexes to support hybrid retrieval logic.
Varying update frequencies — real-time, monthly, and annual — require incremental synchronization strategies to avoid the high time cost of full index rebuilding.
Fields are paired with physical or business units. Numeric fields must be normalized, otherwise similarity calculations will produce deviations.
Documents contain multi-module long text content. Segmentation strategies must be adjusted to ensure complete semantics for each segment, and avoid semantic fragmentation across modules.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Industrial park due diligence reports contain multi-module long text. This range ensures complete semantics for each segment of text, and avoids semantic fragmentation across modules. |
retrieve_top_k | Top 10–15 results | Due diligence report retrieval must cover multi-dimensional data such as settled enterprises, energy consumption, and rent. This range balances recall coverage and retrieval efficiency. |
vector_db_retrieve_threshold | 0.72–0.85 | Industrial park data contains a large number of numeric fields with units. This threshold filters low-relevance numeric matching results, and retains retrieval results with high semantic relevance. |
incremental_sync_interval | 300 seconds | Park settled enterprise information is updated in real time. Energy consumption and rent data are updated monthly. This interval balances real-time performance and server load. |
embedding_batch_size | 32–64 | The length of single-segment vectorized text is moderate. This batch size balances GPU memory usage and vectorization speed. |
parse_overlap_rate | 10% | Retaining overlapping content after long text segmentation avoids semantic breaks, and adapts to the long paragraph structure of park due diligence reports. |
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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Phenomenon: A PostgreSQL dependency loading failure error occurs when deploying the Milvus vector database. Cause: The default Compose template that does not adapt to the local storage path is used. The built-in PostgreSQL configuration of the template does not match the actual mount directory.
- Phenomenon: A
503 Service Unavailableerror is returned when connecting to a locally deployed embedding model. Cause: The complete model interface address is not filled in the FastGPT model configuration interface, or external access permissions for the model service listening port are not enabled. - Phenomenon: Deviations appear in knowledge base disk occupancy statistics, and it is impossible to distinguish the storage proportions of original files, segmented files, and embedded vectors. Cause: The FastGPT built-in storage statistics module is not enabled, or the path mapping rules for the vector database and file storage are not correctly configured.
How to Verify Successful Configuration
- Upload a sample industrial park due diligence report. Check whether corresponding segmented data is generated in the vectorization task queue, and confirm that the segmented length matches the preset
chunk_sizerange. - Initiate a retrieval test. Enter keywords related to park operations. Check whether the number of returned results falls within the preset range of
retrieve_top_k. - View the vector database monitoring panel. Confirm that the incremental synchronization task is executed regularly according to the configured
incremental_sync_interval, with no accumulation errors. - Check the model service logs. Confirm that there are no memory overflow or timeout errors during embedded vector generation, matching the preset
embedding_batch_sizeparameter.
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.