What the data for this category looks like
Decoration and renovation research report data primarily comes from public industry statistics released by the China Building Decoration Association, special research reports from securities firms’ building decoration teams, annual and quarterly financial reports of listed home decoration and public decoration enterprises, and decoration engineering bidding data published by local housing and construction authorities. Update cycles include regular quarterly updates, annual industry whitepaper updates, and temporary supplements issued after the release of major bidding policies. Most documents contain modules for overall industry prosperity analysis, revenue breakdown of segmented tracks (home decoration, public decoration, customized decoration), details of key project bids, and policy impact interpretations. Fields include report release date, project bid amount (unit: ten thousand yuan / hundred million yuan), year-over-year change rate, participating enterprise qualification level, policy document number, and other fields. Some bidding documents also include project location and construction period parameters.
Constraints on knowledge base retrieval and recall from these characteristics
The multi-source and heterogeneous nature of decoration and renovation research reports requires the knowledge base to support multi-format document parsing and adaptation, to avoid parsing inconsistencies across different source PDFs and web formats. Differentiated update cycles require support for scheduled incremental synchronization and manual triggering of temporary updates, to ensure the timeliness of policy-related temporary research reports. Fields with clear units such as amount and qualification level require the retrieval system to support unit recognition and precise matching, to prevent retrieval results where values and units are mismatched. The high proportion of long documents requires a segmentation strategy aligned with the chapter structure of research reports, to avoid invalid cross-chapter retrieval.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Decoration and renovation research reports are mostly long documents, and some PDFs contain multi-page charts that take longer to parse. 300 seconds covers the parsing needs of most documents |
maxContext | 800–1200 characters | Core information of decoration and renovation research reports is mostly concentrated in a single paragraph or adjacent chapters. This range retains sufficient context while avoiding redundant information interfering with retrieval |
Number of retrieved entries | Top 6–8 entries | Research report content is mostly structured analysis. Too many retrieved entries will lead to redundant output from the large model. 6-8 entries cover core reference information |
Similarity threshold | 0.72–0.78 | There are many segmented track terms in decoration and renovation research reports. A threshold that is too low will introduce irrelevant industry data, while a threshold that is too high may miss valid content from relevant segmented scenarios |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some large industry whitepaper documents have relatively large sizes. This upper limit covers the upload needs of regular research reports and whitepapers |
Number of re-ranked returned entries | Top 3–4 entries | Core arguments of research reports are concentrated. Retaining 3-4 entries after re-ranking is sufficient to provide reference basis for large model generation |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: Chaotic logic in knowledge base query results after offline deployment, with the large model failing to organize retrieved data. Cause: The local vector database index update mechanism is not properly configured during offline deployment, leading to a mismatch between retrieved data and original documents.
- Phenomenon: Single document retrieval takes more than 15 seconds, and the interface displays a loading timeout prompt. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and long documents are not pre-segmented, resulting in excessive time spent on parsing and retrieval processes. - Phenomenon: The AI generates irrelevant responses even when no matching content exists in the knowledge base. Cause: The configuration item "do not generate responses when no matching results are found" is not enabled, or the similarity threshold is set unreasonably, leading to false retrieval of irrelevant content.
How to confirm successful configuration
- Upload a single decoration and renovation research report document, confirm that no text fields are missing after parsing completes, and core information such as amount and project name is fully retained.
- Initiate a retrieval request for a segmented track, and verify the keyword matching degree and release time relevance of the retrieved results.
- Modify the similarity threshold and run two identical keyword retrievals, observe whether the change trend of the number of retrieved entries aligns with the configured logic.
- Trigger a manual knowledge base update operation, confirm that the vector database index update status is normally displayed in the interface.
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.