What Data for This Category Looks Like
Data sources for commercial real estate investment research include public real estate registration data from local housing and urban-rural development departments, internal operation systems of commercial operators, third-party market research reports, and land transaction platform transfer information. Update cadence follows tiered cycles: basic project information has a long update interval, operational data such as leasing and rent is updated monthly, competitor dynamics are updated quarterly, and sudden investment promotion adjustments or policy changes are updated in real time. Available data formats include structured operation ledgers, semi-structured feasibility study reports, and unstructured location maps and investment promotion brochures. Each structured document can contain thousands of rows. Each unstructured document can be up to hundreds of thousands of characters long. Fields cover total leasable area, average monthly rent per unit area, project opening date, and other relevant metrics. Units include square meters, yuan per square meter per month, dates, and more.
What Constraints These Characteristics Impose on Database and Operations Work
Multiple data types coexist, so support for both relational structured queries and vector retrieval is required. Database selection must balance structured storage and vector indexing capabilities. Tiered data update cycles require configuration of hot and cold data separation strategies, to prevent hot data queries from being disrupted by cold data write operations. Wide variation in single-document size requires configuration of reasonable chunking and paging rules, to avoid overloaded data returns from single queries. A large number of professional fields and units require field validation rules at the database level, to ensure consistency during data entry. Real-time updated investment promotion data requires low-latency write interfaces, to avoid impacting performance of regular investment research queries.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
db_connection_pool_size | 20–30 | Commercial real estate investment research workflows often initiate multiple associated queries simultaneously. This parameter controls the upper limit of the database connection pool, to avoid connection exhaustion affecting concurrency |
POSTGRES_MAX_CONNECTIONS | 150–200 | PostgreSQL is used to store structured rent and investment promotion ledger data. Sufficient connection counts are required to support batch queries when multiple workflows run concurrently |
mongo_vector_index_dim | 1536 | Commercial real estate location and report text mostly use general embedding models. 1536-dimensional vectors balance retrieval accuracy and storage costs |
PARSE_FILE_CHUNK_SIZE | 800–1200 characters | Commercial real estate feasibility study reports have long single-segment content. This parameter controls chunk length, to avoid semantic fragmentation from overly small chunks or reduced retrieval accuracy from overly large chunks |
db_query_timeout | 600 seconds | Batch queries of large investment promotion ledgers require sufficient timeout time to avoid mid-query interruptions, adapting to the large data volume characteristic of commercial real estate |
cold_data_retention_days | 365 days | Basic project information belongs to cold data. Historical data older than one year can be migrated to archive storage, reducing primary database load |
The parameter values provided on this page are common recommendations for starting point configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: When executing database query components in batches, the previous step outputs a list format, but no results are returned after execution. Cause: The
db_batch_process_thresholdparameter is not configured. When the number of list elements exceeds the upper limit of a single database query, the task is silently truncated or blocked. - Phenomenon: An
Access denied for usererror is returned during database connection debugging. Cause: The database access whitelist is not configured correctly, or the FastGPT service account is not granted read and write permissions for the corresponding database. - Phenomenon: Significant delays occur in multi-round investment research queries. Database performance monitoring shows high CPU or IO utilization. Cause: No hot and cold data separation strategy is configured. Mixed storage of cold and hot data causes query resource competition.
How to Confirm Configuration Is Complete
- Initiate a test workflow that includes multiple associated queries, observe the database connection pool usage, and confirm that the number of connections does not reach the configured upper limit.
- Import a commercial real estate feasibility study report, check the semantic integrity of the chunked document fragments, and confirm that the chunk length meets business requirements.
- View the database performance monitoring dashboard, confirm that the response time of hot data queries meets preset standards, and that cold data migration tasks run on schedule.
- Debug the database connection component, use a test account to initiate read and write operations, confirm that permission configurations are correct, and no access errors occur.
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.