What this type of data looks like
Daily operation ledgers of self-owned commercial projects, consolidated group financial statements, and publicly disclosed industry sector operation data serve as the primary sources for commercial real estate financial report data. Three update cycles apply: monthly operation detail updates, quarterly review report updates, and annual full financial report updates. Most documents take the form of multi-dimensional tables, containing fields such as project name, rentable building area, actual leased area, average daily rent per square meter, operation cost items, revenue breakdown, and others. Units include square meters, yuan, yuan/square meter/day, and others. Document length varies widely: small project documents span a few pages, while large complex financial reports can reach dozens of pages.
What constraints do these characteristics impose on knowledge base retrieval and recall?
The multi-dimensional table structure and cross-project detail fields of commercial real estate financial reports demand precise field dimension matching during retrieval to avoid retrieving irrelevant sector data. Documents with different update cycles must be distinguished by timestamps to ensure recall of the latest operation data and exclude archived historical content. The wide span of single document length requires limiting the character length of single-document recall to avoid exceeding the context window. Parallel data from multiple projects needs to support filtering by dimensions such as project name and region; otherwise, recall results will mix information from different projects and reduce the accuracy of financial report analysis. Uniform normalization of field units is also required during retrieval to avoid matching failures caused by unit differences.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_count | Top 3-5 | Commercial real estate financial reports have many and detailed fields. Too many recalls will cause context redundancy, while too few will fail to cover complete analysis dimensions |
similarity_threshold | 0.75-0.85 | The semantic similarity of financial report fields is relatively high. Too low will introduce irrelevant data, while too high may miss relevant details |
chunk_length | 800-1200 characters | The content of a single segment of commercial real estate financial reports should not be too long to avoid destroying field association during splitting, while adapting to conventional context windows |
rerank_count | Top 2-3 | The most relevant project-level financial report data must be retained to reduce redundancy in subsequent processing |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Financial report documents of large commercial complexes have long lengths and take longer to parse, and the default value may be insufficient |
filter_field | Project Name, Report Period | Commercial real estate financial reports need to be filtered by project and time dimensions to ensure the targeting of recalled data |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and testing on self-provided samples is recommended before finalizing.
Three common errors
- Phenomenon: Knowledge base search node authentication fails, and a 403 status code is returned during calls. Cause: The authentication switch for the corresponding node is not enabled in the FastGPT knowledge base configuration, or authentication parameters are not aligned with the application configuration's secret key.
- Phenomenon: No optional values appear when selecting variable references for the knowledge base search node, and configured variables cannot be associated in the input box. Cause: Context variables for filtering are not configured in the application workflow, or the output fields of the variables do not match the filter fields of the knowledge base.
- Phenomenon: Garbled Chinese characters appear after importing CSV-format commercial real estate financial reports. Cause: The encoding format of the CSV file is not set to UTF-8, or the correct encoding parsing option is not selected during upload.
How to confirm configuration is correct
- Upload a test commercial real estate financial report document, view the parsed field list, and confirm that all key fields are correctly identified.
- Initiate a test retrieval, enter a query containing the project name and report period, and verify whether the recall results include documents that meet the filtering conditions.
- Call the API interface to initiate retrieval, compare results returned by online conversation and the API, and confirm that recall count and similarity matching degree are consistent between the two.
- Check the authentication configuration, initiate a call with an invalid secret key, confirm that the corresponding error code is returned, and verify that the authentication logic 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.