Multi-turn Dialogue and Prompt Engineering for Financial

What the Data for This Category Looks Like

Financial leasing due diligence data comes from lessee credit reports, lease asset ownership certificates, formal lease contract texts, lease asset valuation reports, and annual financial statements. Updates occur immediately after a lease contract is signed. Credit reports are updated quarterly. Lease asset valuations are adjusted quarterly based on market trends. Document structure includes structured fields and unstructured text. Structured fields cover lease asset original value, lease term, monthly rent amount, lessee unified social credit identifier, and other items. Unstructured text consists of PDF-format contracts and reports. Field units include yuan/month, square meters, units, years, and other units. Some fields must be bound to specific effective times.

Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering

Mixed structured and unstructured data requires multi-turn dialogue to retain context for cross-document information association, avoiding disjointed responses. Fields have specific units and effective times. Prompts must mandate that returned results include units and data time ranges, preventing unit confusion or reference to expired data. A high proportion of long documents requires limiting segmentation and recall granularity to avoid information loss or redundancy. Diverse data sources require multi-turn dialogue to guide users to clearly specify query dimensions, such as prioritizing specification of lease assets or lessees.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`12000–15000 characters`	Financial leasing due diligence data includes long lease contracts and multiple reports, requiring sufficient context to support cross-document associative analysis
`RECALL_SCORE_THRESHOLD`	`0.75–0.85`	Filters low-relevance redundant lease asset information while retaining valid associated content from lessee credit reports
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Single lease asset valuation report or annual financial statement typically does not exceed this limit, preventing parsing timeouts
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Long PDF contract parsing requires sufficient time to prevent interruptions that cause data loss
`segment length`	`800–1000 characters`	Matches the paragraph logic of financial leasing due diligence documents, avoiding splitting that breaks the integrity of contract clauses
`recall count`	`top 8–10 entries`	Balances information completeness and response conciseness, covering three core data types: lease assets, lessees, and contracts

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 targeted analysis, and it is recommended to test on your own samples before finalizing.

Three Common Mistakes

A 401 Unauthorized status code is returned when calling the dialogue interface. The application ID is mistakenly passed as the secret key in the interface authentication parameters.
Core fields are empty after uploading and parsing a lease contract PDF. An unreasonable segment length parameter is set, causing long document splitting that breaks context association of contract clauses.
Raw unformatted text is returned when calling the knowledge base interface directly. The preset prompt formatting process is not triggered via the dialogue chain, resulting in output that does not meet due diligence report specification requirements.

How to Verify Proper Configuration

Upload a standard lease contract PDF, and check whether the parsed extracted fields cover three core pieces of information: lease asset ownership, lease term, and rent, with field units matching the source document.
Initiate two progressive queries, such as first querying the credit status of a specific lessee, then querying the valuation of the corresponding lease asset, and checking whether the system associates the two data sets to generate a coherent response.
Call the dialogue interface using the preset authentication secret key, and confirm that the returned status code is 200 OK with no authentication failure errors.
Adjust the recall count parameter, then initiate a query covering multiple data types, and check that the number of returned results matches the configured value.

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.