Vector Models and Indexes for Intelligent Due Diligence

What the data for this category looks like

Intelligent due diligence report data for building construction engineering comes primarily from project approval documents, construction drawing design files, construction logs, weekly supervision reports, completion settlement materials, real estate registration information, and surrounding supporting facility survey data. Data update frequency shifts based on project phases. Data volume is low during the initial project approval stage. During the construction phase, progress, material, and cost data are updated weekly or monthly. After project completion and archiving, data stabilizes. Most documents use a mixed structure of structured text and attachments. They include fields such as basic project information, progress milestones, material ledgers, cost details, and quality acceptance records. Supported units include professional engineering units like square meters, yuan, days, tons, and MPa.

What constraints these characteristics impose on vector models and indexes

The mixed structure and staged update properties of building construction due diligence data create multiple constraints for the vector models and indexes workflow. First, the data includes structured metadata, unstructured text, and image attachments. This requires support for multimodal vectorization and metadata filtering. Second, staged high-frequency updates require support for incremental indexing. Full reindexing must be avoided to prevent resource waste. Third, document lengths vary significantly. This demands adaptive semantic chunk splitting instead of fixed-length segmentation, to preserve complete semantics of professional paragraphs. Finally, a large volume of professional engineering terms and fixed enumeration fields requires vector models with domain-specific encoding capabilities.

How to set configurations

Configuration Item	Recommended Approach	Rationale
`embedding_model`	Zhipu Embedding-3 or Tongyi Qianwen Text Embedding V2	Building construction data contains numerous professional engineering terms and structured fields. These models deliver better adaptation for professional vocabulary encoding
`chunk_size`	800–1200 characters	Most building construction documents consist of semantically coherent professional paragraphs. This range preserves complete semantics for single-segment cost or progress records
`chunk_overlap`	100–150 characters	Prevents information breaks across semantic chunks, and supports contextual coherence for long documents
`index_type`	HNSW	The volume of building construction data vectors grows as projects progress. HNSW indexes have minimal reduction in recall efficiency as data volume increases
`metadata_filter_enabled`	Enabled	Building construction due diligence data includes filterable fields such as project address and structure type. Metadata filtering can narrow recall scope and improve accuracy
`image_embedding_enabled`	Enabled	Building construction documents include image attachments such as construction drawings and test reports. Synchronous vectorization enables joint text-image retrieval

The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material formats, data volume, and business rules. Specific scenarios require individual analysis. It is recommended to test on your own samples before finalizing settings.

Three common errors

Symptom: After executing collection add data, the full index does not update synchronously, and query results lack newly added data. Cause: The functional boundaries between collection add data and create training order are confused. The former only adds a single vector, while the latter is used to trigger full or incremental index reconstruction. The correct corresponding operation was not selected based on the update scenario.
Symptom: Retrieval results only return text fragments, and image attachments such as construction drawings and test reports cannot be retrieved. Cause: The image_embedding_enabled configuration is not enabled. No vector embeddings are generated for image content, and recall is based solely on text vectors.
Symptom: When embedding_model is set to Embedding-3, the interface returns a 401 Unauthorized or model not found error. Cause: No alias for the corresponding model is added in the mapping configuration, or the model permission is not granted to the currently used token.

How to confirm the configuration is complete

Upload a sample building construction due diligence document that includes both text and images. Review the parsed vector embedding results to confirm that vectors have been generated for image fields.
Execute collection add data to add a test data entry. Call the retrieval interface to confirm that the vector of the newly added data is included in the index scope.
Configure metadata filtering rules. Attempt to filter recall results by project address or structure type to confirm that the filtering logic works.
Call create training order to trigger a full index reconstruction. Check the index update log to confirm that the task execution status is successful.

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.