What the data for this category looks like
Industrial park intelligent due diligence report data mainly comes from public investment promotion announcement documents from park management committees, territorial and spatial planning approvals, industrial and commercial registration information of settled enterprises, monthly operation ledgers, annual industrial development white papers, and other sources. Update frequency varies by content type. Investment policy files update quarterly or at policy release milestones. Settled enterprise information changes in real time as enterprises move in or out. Operation ledgers update monthly. Document structures are mostly mixed formats. These include long text policy paragraphs, structured data tables, and image attachments. Field units include standardized measurement identifiers such as square meters, yuan per square meter per month, number of enterprises, and others.
Constraints on knowledge base retrieval and recall
The multi-type data characteristics of industrial park due diligence reports impose multiple constraints on the retrieval and recall link. First, content mixing structured numerical fields and long text policy documents requires the retrieval system to support both semantic matching and precise numerical retrieval. This prevents missing core data due to only matching semantics. Second, data sources with widely varying update frequencies need flexible switching between incremental and full updates. This ensures timeliness for different types of data. Third, documents containing image attachments require the retrieval system to retrieve associated image and text content. This avoids missing visual support when only text is returned. Fourth, field data with multiple units needs a unified unit mapping rule. This prevents reduced retrieval accuracy caused by inconsistent units.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Industrial park due diligence reports often include multiple attached documents, with large single-file sizes. 500 MB covers most scenarios |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Parsing long documents (such as annual operation report collections) takes a long time. 900 seconds avoids timeout failures |
Chunk size | 800–1200 characters | Industrial park documents contain both long policy texts and structured tables. This range balances context integrity and retrieval accuracy |
Similarity threshold | 0.72–0.78 | Industrial park data fields have a high degree of standardization. This range balances precise matching and result coverage |
Recall count | Top 8 entries | Due diligence reports need to cover multi-dimensional data. 8 entries balances information breadth and relevance |
Rerank result count | Top 3 entries | Final due diligence report output needs to focus on core conclusions. 3 entries avoids information redundancy |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume and business rules. Each scenario requires individual analysis. It is recommended to test on samples specific to the deployment before finalizing settings.
Three common configuration mistakes
- Phenomenon: After uploading HTML interface documents generated by Javadoc, no matching results appear in knowledge base retrieval. Cause: FastGPT’s default parsing rules do not enable HTML content extraction, and only standard office document formats are supported.
- Phenomenon: After configuring the knowledge base, the search process shows no GPU resource usage and returns empty results. Cause: GPU acceleration configuration for knowledge base retrieval is not enabled, or the bound computing resources failed to start properly.
- Phenomenon: Only text content is included in retrieval results, and image attachments such as park planning diagrams in PDF documents are not retrieved. Cause: The image-text synchronous recall switch for document parsing is not enabled, and only text information is extracted.
How to confirm configuration is complete
- Upload a typical industrial park due diligence report document, check if the parsing status shows completed, and confirm that parsing time meets expectations.
- Initiate a retrieval test, enter a query containing fields such as park area, rent, etc., and verify whether the field matching degree of returned results meets business requirements.
- When calling the knowledge base API, carry historical conversation context parameters, and verify whether referring expressions are correctly parsed.
- View the GPU monitoring panel, confirm that corresponding resources are occupied during retrieval operations, and verify that the acceleration configuration takes effect.
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.