What data for this category looks like
Data comes from institutional operating data published by regulators, counterparty-provided target financial statements, internal due diligence interview records, and confirmation replies. Updates follow project timelines. Individual project due diligence reports are updated at milestones during the period from project initiation to archiving. Only supplementary revisions are made after archiving if the target party experiences major operational changes. Document structures include structured fields and unstructured attachments. Structured fields cover subject qualifications, credit limits, collateral valuations, days past due, and similar items. Unstructured attachments include transcribed interview recordings, on-site inspection images, and third-party rating reports. Field units include ten thousand yuan, percentage, days, rating levels, and similar units. Some fields are enumeration types.
What constraints these characteristics impose on vector models and indexing workflows
First, structured fields account for a large share of the dataset, and include enumeration and numerical fields. Vector models must support vectorization of structured data, or separate metadata indexing for structured fields must be configured to support precise recall. Second, data updates are rolled out in batches according to project milestones, not full real-time synchronization. Index systems must support incremental updates and breakpoint resumption to avoid resource consumption from full index rebuilding. Third, documents include long-form interview summaries and unstructured attachments. Vector models must adapt to long-context input. Indexes must support large file chunked storage and multi-level recall. Fourth, fields include clear units and enumeration values. Index configurations must retain field metadata to enable subsequent filtering of recall results by field dimension.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Matches the information density of long-form interview summaries and structured fields in multi-financial due diligence reports, balances single-segment vector accuracy and recall coverage |
Chunk Overlap Length | 100–150 characters | Prevents breakage of associated information across segments, adapts to logically linked expressions across paragraphs in due diligence reports |
Recall count | Top 8–12 results | Matches the multi-dimensional associated key points of due diligence reports, avoids excessive redundant recall content or missed critical information |
Similarity threshold | 0.72–0.85 | Filters low-correlation unstructured attachments and duplicate structured field content, improves recall precision |
Incremental Index Switch | Enabled | Adapts to the batch update nature of due diligence reports by project milestone, reduces resource consumption from full index rebuilding |
embedding_api_url | Calibrated via actual testing | Adapts to interface address requirements of different embedding models, such as access addresses for locally deployed or third-party vector models |
The parameter values provided on this page are standard starting points for configuration setup. Actual values are affected by material format, dataset size, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: After switching the knowledge base vector model, indexing progress stalls with no updates, and the original model cannot be switched back in the interface. Cause: The model is switched without first pausing the currently running indexing task, resulting in a locked indexing process that cannot synchronize task status and configuration parameter updates.
- Symptom: After configuring a third-party embedding model, test calls return a
404 page not founderror. Cause: The interface address of the embedding model is not filled correctly, or the interface path does not match the official endpoint of the corresponding model service provider. - Symptom: Unstructured image attachments in due diligence reports cannot generate valid vectors, and no corresponding results appear during retrieval. Cause: Multimodal vector indexing configuration is not enabled, or a compatible multimodal embedding model is not deployed, resulting in failure to correctly vectorize image attachments.
How to confirm proper configuration
- Navigate to the knowledge base configuration page, view the vector model and indexing configuration items, confirm that parameters such as
Chunk sizeandChunk Overlap Lengthmatch the preset configuration. - Upload a test sample of a multi-financial due diligence report, trigger the indexing task, and check whether there are successful records of vector generation and index writing in the task logs.
- Run a retrieval test, input key expressions from the due diligence report, and verify whether the number and similarity of recall results match the preset verification rules.
- Attempt to perform an incremental index update, confirm that only newly uploaded content is processed, and no full index rebuilding is triggered.
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.