What the data for this category looks like
Commercial real estate investment research data comes from multiple sources: project record information from government competent departments, monitoring data from business district operation institutions, daily operation ledgers of property owners, public research materials from industry research institutions, and internal investment promotion, lease, and operation archives of enterprises. Most documents use structured tables nested with paragraph descriptions. Some planning documents include scanned files and vector drawings. Core fields in each document cover project location coordinates, rental unit price, tenant business type proportion, passenger flow time distribution, fire acceptance grade, and more. Units include yuan/square meter/day, person-times, square meters, and others. Update cycles vary across data types. Basic project information updates less frequently. Operation data updates more often.
What constraints do these characteristics impose on the "context and token" workflow?
The multi-field and complex unit features of commercial real estate investment research data cause invalid information to mix in retrieved context fragments if field specifications are not unified. This increases unnecessary token consumption. A high proportion of long documents means passing full documents directly quickly exceeds the model's token limit. Documents must be split reasonably. Differing update cycles across multi-source data require filtering expired information during retrieval. Otherwise, outdated content enters the context. Large differences in document lengths from different sources also lead to uneven token allocation for context. Investment research queries usually need to associate information across multiple fields. If retrieved context fragments cannot accurately match query dimensions, invalid token occupation grows further.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContextToken | 8000–12000 | Matches the single-segment token requirements after splitting long commercial real estate documents, avoiding context overflow |
chunkSize | 800–1200 characters | Adapts to the mixed structure of structured tables and paragraphs in commercial real estate documents, avoiding splitting that breaks field associations |
similarityTopK | Top 6–8 entries | Balances the richness of retrieved information and token consumption, adapting to multi-field associated investment research queries |
rerankThreshold | 0.72–0.78 | Filters low-similarity redundant documents, reducing invalid token occupation |
parseFileTimeout | 300 seconds | Adapts to the parsing time of long-cycle operation reports, avoiding parsing interruptions |
maxOutputToken | 2000–3000 | Matches the length requirements of investment research conclusion outputs, avoiding output truncation |
The parameter values provided on this page are conventional recommendations used as starting points for 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 configuration errors
- Phenomenon: A
Reached the max retries per request limiterror appears during invocation. Cause: The retry threshold was not adjusted for the retrieval volume of multi-source commercial real estate data. This causes vector database requests to exceed the maximum retry count due to timeout. - Phenomenon: Token consumption per conversation round exceeds the preset limit. Cause: The number of retrieved entries was not limited, or a reasonable context token upper limit was not set. This introduces overly long operation report fragments.
- Phenomenon: Unupdated rental data appears in output results. Cause: No expired data filtering rules were configured. Old ledger documents beyond the update cycle are retrieved, increasing invalid context token occupation.
How to verify correct configuration
- Upload a typical commercial real estate operation review report. Check the number and length of parsed segments to confirm the
chunkSizeconfiguration matches the document structure. - Launch an investment research query. Check the number of retrieved documents and similarity scores to confirm
similarityTopKandrerankThresholdvalues adapt to query needs. - Simulate multi-round conversations. Count total token consumption per round to confirm
maxContextTokenandmaxOutputTokenvalues stay within the model's supported range. - Review vector database request logs. Confirm the retry count does not hit the error threshold to verify the rationality of related retry configurations.
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.