What Data for This Category Looks Like
Investment research data for the financial leasing sector comes from internal ledgers of leasing companies, third-party leased asset valuation institution reports, public regulatory disclosure documents, and industry association statistical materials.
Data update rhythms vary: internal ledgers update in real time with new signed lease contracts. Third-party valuation reports refresh quarterly. Regulatory policy documents take effect immediately upon release.
Document types include structured rental cash flow statement sheets, leased asset ledger tables, and unstructured scanned lease contracts, leased asset evaluation reports, and industry policy interpretation documents.
Fields include leased asset purchase cost, lease term, rental payment cycle, and margin coefficient. Units include RMB yuan, natural month, and decimal coefficient.
What Constraints These Characteristics Impose on the Context and Token Workflow
Single-row structured data from rental cash flow sheets can reach hundreds of characters. Single unstructured lease contracts can be thousands of characters long. Direct concatenation quickly depletes model context token quotas.
Differences in data update frequencies require regular refreshing of recalled context content. This prevents expired leased asset valuation data from causing deviations in investment research conclusions. Refresh operations also add additional token consumption.
Mixed multi-type document structures require precise recall of relevant content. Otherwise, irrelevant fields will be included, further increasing redundant token usage.
Cross-document investment research needs require integrating multi-dimensional data within a limited context window. This places higher requirements on recall rules and segmentation strategies.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale | |
|---|---|---|---|
chunkSize | 800–1200 characters | Adapts to the semantic integrity of long leasing contracts and valuation reports, balances segmentation length and token consumption | |
recallTopK | Top 3–5 entries | Covers three core investment research data types: leased assets, contracts, and valuations, avoids excessive recall that causes context token overflow | |
similarityThreshold | 0.72–0.85 | Calibrated via actual testing | Filters low-relevance lease ledgers and industry research reports, reduces redundant content occupying token quotas |
maxContextTokens | 8000–12000 | Matches the context window of mainstream large models, adapts to total token consumption after multi-document concatenation | |
PARSE_CHUNK_OVERLAP | 100–150 characters | Ensures semantic coherence after long contract segmentation, reduces invalid token consumption caused by lost cross-segment information | |
stream | false | Investment research scenarios require complete return of context concatenation results, avoids token statistical deviations caused by streaming returns |
The parameter values provided on this page are all common 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 conduct tests on your own samples before finalizing settings.
Three Common Configuration Errors
- Phenomenon: Returns a 422 status code when called, with an error message containing "Messages token length must". Cause: The total token count of the concatenated context exceeds the model's preset context window limit, and
maxContextTokensis not configured for effective truncation. - Phenomenon: The runtime of online models only displays the first token latency, while offline models display total runtime, leading to inconsistent statistical metrics. Cause: The
streamparameter is not uniformly configured. In streaming return mode, online models only count first token latency, while offline models count full process latency. - Phenomenon: The recalled context mixes a large number of irrelevant leased asset basic ledger fields, leading to final prompt token limit exceedance. Cause: A reasonable
similarityThresholdis not set, and low-similarity documents unrelated to the current investment research topic are recalled.
How to Verify Correct Configuration
- Upload a typical financial leasing lease contract document, check the parsed segmentation results, confirm that the segmentation length matches the
chunkSizesetting. - Initiate a simulated investment research call, check the returned context concatenated content, confirm that the number of recalled documents matches the
recallTopKsetting. - Check the token consumption data in the call logs, confirm that the total token count does not exceed the preset range of
maxContextTokens. - Compare the runtime statistics of online and offline models, confirm that the
streamparameter configuration is uniform, with no statistical metric inconsistencies.
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.