What the Data Looks Like for This Category
Real estate construction financial report data primarily comes from official disclosed annual and quarterly reports of construction and decoration enterprises, internal project ledgers, and settlement documents. The regular disclosure cycle is quarterly, while internal ledgers can be updated monthly. A single financial report document includes fields such as total project contract value, completed output value, breakdowns of building material, labor, and machinery costs, accounts receivable balance, project duration, etc. Units are mostly ten thousand yuan, square meters, and calendar days; some fields are broken down by individual project.
Constraints Imposed on Vector Models and Indexing
Real estate construction financial reports include multi-item engineering costs and project-level details. Single documents are lengthy with highly segmented fields, requiring vector models to adapt to semantic understanding of professional financial and engineering terminology. Data from multiple sources (ledgers and public disclosures) have inconsistent update cycles, so indexing must support incremental updates and merged indexing for multi-source data. Detailed data split by individual project increases the total number of index entries, so a reasonable chunking threshold must be configured to avoid index redundancy. A large number of numeric fields for amounts and durations exist in financial reports, so vector models must correctly associate numeric values with corresponding business semantics.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
EMBEDDING_MODEL_NAME | text-embedding-ada-002 or m3e-base | Adapts to professional financial and engineering terminology for real estate construction financial reports. text-embedding-ada-002 has stronger compatibility, while m3e-base is optimized for domestic Chinese scenarios |
CHUNK_SIZE | 800–1200 characters | Real estate construction financial reports include segmented details. This chunk length adapts to the semantic integrity of single-item details and avoids splitting across business items |
RECALL_TOP_K | Top 8–12 entries | Real estate construction financial reports have many segmented fields. A sufficient number of entries must be recalled to cover multi-item requirements, while avoiding redundant recall that slows retrieval speed |
INDEX_INCREMENTAL_UPDATE | Enabled | Internal ledgers are updated monthly and public disclosures are updated quarterly. Incremental updates reduce resource consumption from full index rebuilding |
EMBEDDING_BATCH_SIZE | 16–32 | Real estate construction financial report documents are lengthy. Batch processing balances memory usage and processing speed to avoid embedding task timeouts |
SIMILARITY_THRESHOLD | 0.75–0.85 | Filters low-relevance financial report fragments, retaining engineering cost and output value data with high semantic matching to the query |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: After configuring embedding models such as
text-embedding-ada-002, knowledge base retrieval prompts "No recognizable embedding results". Cause: The embedding model was not correctly connected to the platform channel, or the model name in the configuration item was spelled incorrectly and did not match the model ID already connected to the platform. - Phenomenon: When enabling the local
m3emodel, the interface continuously displays the "Indexing" status with no progress. Cause: The local model deployment did not open the correct API port, or batch processing parameters were set too large, resulting in excessive memory usage that prevents completion of embedding generation. - Phenomenon: Financial report retrieval speed is too slow, and the cause is mistakenly attributed to insufficient vector model performance. Cause: The
RECALL_TOP_KorCHUNK_SIZEparameters were not adjusted, resulting in too many recalled entries or overly fine-grained chunking. The actual bottleneck is in the index retrieval link, and the embedding model itself is not the bottleneck.
How to Verify Successful Configuration
- Check the platform embedding model management page to confirm the target model is activated, and the model name in the configuration item exactly matches the name displayed on the management page.
- Upload a single sample real estate construction financial report, perform a manual embedding test, and confirm valid embedding vector entries are generated in the task log without error prompts.
- Perform a small-scale retrieval test, enter a query containing keywords such as engineering cost and project duration, and verify returned results include core detailed items of real estate construction financial reports.
- Adjust the
RECALL_TOP_Kparameter, compare the number and relevance of retrieval results, and confirm the recall logic meets business requirements.
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.