What the data for this category looks like
Data for IT service intelligent due diligence reports targeting the financial, insurance and wealth management industry comes primarily from service provider qualification filing documents, past project delivery archives, operation and maintenance monitoring logs, compliance self-inspection reports, and customer feedback documents. Data updates are triggered by project delivery milestones or compliance annual audit cycles, with no fixed weekly or monthly schedule. Each individual report includes structured fields and unstructured text. Structured fields include the service provider’s unified social credit code, project execution cycle (unit: days), and compliance rating (unit: levels). Unstructured content covers project implementation details, risk investigation records, and technical solution descriptions. The character count of single documents varies widely.
What constraints do these characteristics impose on vector models and indexing
The data characteristics of IT service intelligent due diligence reports impose multiple constraints on the vector models and indexing workflow. A high proportion of unstructured text and wide variation in character counts require vector models to support long text chunking to avoid semantic fragmentation. Data updates with no fixed cycle require indexes to support incremental update logic, reducing resource usage from full reindexing. Mixed structured fields and unstructured text require support for both vector recall and structured filtering, ensuring retrieval results cover core dimensions such as compliance rating and project cycle. Unique identifier fields such as service provider unified social credit codes require combining exact matching and vector recall to guarantee the uniqueness of retrieval results.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | IT service due diligence reports contain long paragraphs of project implementation details. This range balances semantic completeness and retrieval accuracy |
chunk_overlap | 100–150 characters | Retain overlapping sections after long text chunking to avoid semantic breaks across chunks, adapting to long project description text |
embedding_model | text-embedding-v3 | This model’s semantic representation performance for technical documents and compliance text meets the retrieval needs of IT service scenarios |
index_refresh_interval | Triggered on demand | Data updates have no fixed cycle. Triggering incremental index updates on demand reduces computing resource consumption |
retrieve_top_k | Top 8–12 results | Retrieval for IT service due diligence reports needs to cover multi-dimensional project and compliance information. This range ensures comprehensiveness of recalled results |
filter_field_enable | Enabled | Exact filtering using structured fields such as service provider unified social credit codes is required to avoid mixing reports from unrelated service providers in retrieval results |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Scenario: Returns a 503 status code with the prompt "No available channel for model text-embedding-v3 under current group default". Cause: No available channel node is configured for the text-embedding-v3 model, or the API key of the channel node is not correctly configured.
- Scenario: The number of vector retrieval results does not match the set retrievetopk value, and some key chunks are missing. Cause: chunk_overlap is set too small, causing core semantics to be split into non-overlapping areas after long text chunking, making complete recall impossible during retrieval.
- Scenario: Incremental index update does not take effect. Retrieval results are not updated synchronously after data changes. Cause: indexrefreshinterval is incorrectly set to a fixed cycle, not set to triggered on demand, and incremental index construction is not manually triggered.
How to confirm the configuration is correct
- Upload a sample IT service due diligence report. Check whether the chunking results match the chunksize and chunkoverlap settings, and confirm that there is no excessive fragmentation or chunks exceeding the set length.
- Call the vector retrieval interface, pass retrieval keywords that include project cycle and compliance rating. Check whether both vector recall results and structured filtered results are returned, and confirm that filterfieldenable is correctly enabled.
- Manually trigger an incremental index update. Check whether the index construction log shows the number of incrementally updated entries, and confirm that the on-demand trigger logic for indexrefreshinterval is working.
- Send an embedding test request for the text-embedding-v3 model. Confirm that no 503 channel unavailable error messages are returned.
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.