What Data Looks Like for This Category
The data for decoration and renovation intelligent due diligence reports primarily comes from project filing archives, construction contracts, material procurement lists, site survey records, construction progress logs, and completion acceptance documents. The data update schedule aligns with project progression. Basic information is updated during the project initiation phase, progress and material changes are synchronized during the construction phase, and files are finalized for archiving after completion. Most documents use a chapter-based mixed format, including structured tables (such as lists of material brands, models, and unit prices) and unstructured text (such as construction process descriptions and acceptance opinions). Fields include construction area, budget amount, material batch numbers, acceptance dates, and more. Units and formats must comply with industry filing specifications.
Constraints for Vector Models and Indexing
The mixed format and long text nature of decoration and renovation due diligence reports require vector models to adapt to the embedding accuracy of Chinese professional terminology, while indexing structures must support splitting rules for both structured tables and unstructured text. The multi-field, multi-type data characteristics require indexes to support targeted retrieval by field dimensions, to avoid interference from irrelevant information. The requirement for incremental updates means indexes must support resumable uploads and incremental synchronization, to avoid resource waste caused by full re-imports. Differences in document formats from various sources require splitting rules to balance the integrity of table cells and the semantic coherence of text paragraphs.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Decoration due diligence reports contain long paragraphs of construction descriptions and breakdown lists. This range preserves semantic integrity and prevents key information such as material brands and construction processes from being split across segments. |
chunk_overlap | 100–150 characters | Long text after splitting retains contextual cohesion, preventing key information such as material models and acceptance dates from being truncated across two segments. |
similarity_top_k | Top 8–12 results | Retrieval for decoration due diligence reports needs to cover multiple dimensions including qualifications, materials, and quotations. This value balances recall accuracy and inference load. |
embedding_model | bge-large-zh-1.5 | This model adapts to the embedding effect of professional decoration industry terms such as latex paint models and keel specifications for industry scenarios, and supports unified embedding spaces for local and server-side deployments. |
vector_store_index_type | HNSW | Adapts to mixed-format decoration data, balances retrieval speed and recall accuracy, and is suitable for business requirements involving incremental updates. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Matches the parsing time required for large material list tables, preventing individual files from remaining in the indexing state for extended periods. |
The parameter values provided on this page are common starting points for configuration setup. Actual values are influenced 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 Misconfigurations
- Symptom: Vector retrieval results do not match locally split documents, or a
400 Bad Requesterror appears, prompting an embedding dimension mismatch. Cause: The same vector model was not used consistently. Professional term embedding for decoration due diligence reports relies on model training data, and embedding spaces differ across models. - Symptom: A single large material list file remains in the "indexing" state for an extended period, and the interface displays an
INDEXING_STUCKstatus. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted. The default timeout threshold is insufficient for parsing decoration material lists with a large number of breakdown items. - Symptom: Retrieval results return content from two knowledge bases simultaneously, without following preset priority rules. Cause: The
knowledge_base_retrieval_priorityparameter was not configured, or priority rules were not bound to the retrieval pipeline.
How to Verify Proper Configuration
- Upload a single decoration material list file, check the indexing completion time, and adjust
chunk_sizeandPARSE_FILE_TIMEOUT_SECONDSto values that align with business rhythms. - Call the vector embedding interface, compare embedding results from local and server-side deployments, and confirm that embedding dimensions and model versions are consistent.
- Initiate a retrieval request, verify that only knowledge bases with high priority return results, or that results are sorted by weight when multiple knowledge bases are included.
- Check the index type configuration of the vector database, and confirm that it matches the mixed text and table structure of the current data.
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.