What the data for this category looks like
Data sources for commercial real estate intelligent due diligence reports include project land tenure archives, surrounding supporting facility ledgers, local transaction filing data, tenant lease documents, property operation logs, and on-site surveying and mapping reports as well as environmental assessment reports. Data update cycles are divided by field attributes. Land tenure and location information updates steadily according to local regulatory cycles. Lease documents and property operation logs update monthly based on business nodes. A single project due diligence report is a one-time submitted document integrated from multiple sources. Each document contains structured fields and unstructured text. Structured fields include building area, rental unit price, property rights term, and other similar items. Unstructured content includes long text such as location analysis and business planning.
Constraints on vector models and indexing
The data characteristics of commercial real estate due diligence impose multiple constraints on the vector models and indexing workflow. Multi-source heterogeneous data formats require adaptive splitting logic for structured fields and non-long text, to avoid over-splitting key content such as lease clauses and location analysis. Some core numeric fields require precise matching, which needs to be combined with numeric indexing instead of relying only on text vectors. This improves recall accuracy for indicators such as rental price and area. Single documents have large volume differences and a high number of chunks. A maximum per-file chunk count limit is needed to avoid resource exhaustion. Update frequencies vary across different fields, so incremental indexing support is required to adapt to high-frequency updates for lease and operation data.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
CHUNK_SIZE | 800–1200 characters | Commercial real estate due diligence reports include long text such as location analysis and lease clauses. 800-1200 characters preserves complete semantic units and avoids splitting key content |
CHUNK_OVERLAP_RATE | 10–15% | Key numeric values such as rental unit price and property rights term may appear across chunks. 10-15% overlap ensures that values are not split across different chunks |
MAX_CHUNKS_PER_FILE | 2000 chunks | Split chunks from a single large due diligence report can reach thousands. A 2000-chunk limit avoids single-file indexing timeouts |
INCREMENTAL_INDEX_ENABLE | Enabled | Commercial real estate lease and operation data is updated monthly. Incremental indexing avoids computational overhead from full reindexing |
NUMERIC_INDEX_ENABLE | Enabled | Due diligence reports contain a large number of numeric fields. Enabling numeric indexing improves recall accuracy for precise matching of rental price, area, and other indicators |
VECTOR_MODEL_NAME | Determined via actual testing | Commercial real estate business formats and location text contain specialized terminology. Select a vector model adapted to the industry or combine with a custom vocabulary |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, 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 Mistakes
- Issue: When uploading commercial real estate due diligence reports larger than 10MB, vectorization fails for some chunks after over 1000 chunks are generated. Retrying multiple times provides temporary recovery. Cause: No
MAX_CHUNKS_PER_FILElimit is set. Insufficient server CPU and memory resources cause the vectorization process to be terminated by the kernel. Retrying only reprocesses failed chunks, temporarily relieving resource occupancy. - Issue: Batch upload of multiple large files causes server crash. After restart, the knowledge base remains unready, and index tasks cannot be automatically triggered. Cause: The
INCREMENTAL_INDEX_AUTO_TRIGGERconfiguration is not enabled. After restart, the system does not automatically scan files pending indexing. Manual triggering of the indexing process is required. - Issue: After upgrading FastGPT to version 4.9-4.10, the original commercial real estate due diligence knowledge base cannot return retrieval results. Cause: The vector model from the old version is incompatible with the new version's indexing format. The embedding dimension of the original vector database does not match the model dimension configured currently, resulting in no valid matches during retrieval.
How to Confirm Correct Configuration
- Upload a typical commercial real estate due diligence report, check the backend splitting log, and confirm that the chunk count is within the preset
MAX_CHUNKS_PER_FILErange, with no abnormal splitting markers. - Initiate a search targeting numeric fields, verify the sorting logic of recall results, and confirm that the numeric indexing is active.
- Modify the lease information of an indexed due diligence report, wait for the system to automatically trigger incremental indexing, and confirm that the indexing progress bar updates normally with no stuck state.
- View the vector model configuration page, confirm that the currently selected model matches the model used in previous vectorization tests, with no dimension mismatch issues.
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.