What the data for this category looks like
The data for cement intelligent due diligence reports comes primarily from three sources: batch quality inspection ledgers from production enterprises, regional supply monitoring data from building material industry associations, and paper or electronic quality inspection reports from third-party testing institutions. Data update schedules fall into three categories: Production ledgers update daily with single-batch factory shipment information. Quality inspection reports update once corresponding batch production is completed. Industry monitoring data is released monthly. Each due diligence report follows a standardized field structure: batch number, clinker strength grade, 3-day compressive strength, 28-day compressive strength, factory shipment date, supplier name, transportation origin and mileage. Strength units are MPa, mileage units are km, and dates use the YYYY-MM-DD format.
Constraints imposed by these characteristics on vector models and indexes
The multi-source heterogeneous data characteristics of cement due diligence reports require vector models to support semantic encoding of both structured fields and non-standardized text, to avoid terminology bias across different data sources. Batch-based update schedules require indexes to support incremental updates, eliminating the need for full index reconstruction each time to match daily and monthly update frequencies. The clear field and unit system requires vector retrieval to preserve semantic associations between fields, to avoid similarity calculation errors caused by unit confusion or field misalignment. Cross-batch horizontal comparison demands indexes to support batch recall of related documents, to support regional supply and batch quality comparison scenarios used in due diligence work.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
embedding_model | Use a long-text embedding model that supports 768 dimensions or higher, or a specialized model fine-tuned for the building material industry | Cement due diligence reports contain multi-field associated text. Long-text embedding models can preserve semantic associations between fields. Industry fine-tuned models can strengthen vector consistency for building material terminology |
chunk_size | 800–1200 characters | Single-batch cement due diligence report data fields are concentrated. Chunk length adapts to the complete semantics of single-batch core information, avoiding semantic breaks across batches |
index_type | HNSW index | Cement data has batch-associated temporal characteristics. HNSW indexes can efficiently handle nearest neighbor retrieval for high-dimensional vectors, and support incremental updates |
recall_top_k | Top 10–15 results | Due diligence reports require comparison of multiple batches of data. Recall a sufficient number of similar documents to cover horizontal comparison needs |
vector_db_timeout | 60 seconds | When processing multiple batches of due diligence data in bulk, reserve sufficient time for vector encoding and index writing, to avoid timeout interruptions |
parse_structured_field | Enabled | Cement reports contain standardized fields. Enabling structured field parsing can separately encode field semantics, improving retrieval accuracy |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require targeted analysis. Testing on local sample datasets is recommended prior to finalizing configuration settings.
Three common configuration errors
- Scenario: No corresponding index is generated after the
create_training_orderinterface is called, with the prompt “no valid training data”. Cause: Structured fields of cement due diligence reports were not uploaded to the knowledge base via thecollection_add_datainterface in advance. Training orders only support batch vectorization triggered for documents already contained within a collection. - Scenario: Associated cement quality inspection images are not returned in search results, only text snippets are displayed. Cause: The
enable_image_embeddingconfiguration item was not enabled, or the association relationship between images and corresponding structured fields was not bound during document parsing. - Scenario: A 504 timeout error is returned when calling the vector model interface after local deployment, with logs showing “connection timed out”. Cause: The local port of the vector model was not mapped to the public network, or the address entered in the
vector_db_urlconfiguration item did not include the correct port number.
How to confirm configurations are correctly set
- Access the vector model management page of the knowledge base, and confirm that the model displayed for the
embedding_modelconfiguration item matches the currently selected embedding model. - Upload a single cement batch due diligence report. After parsing is triggered, review the parsed results to confirm all structured fields are fully extracted, verifying that the
parse_structured_fieldconfiguration is active. - Initiate a batch vectorization task, and check the task status page to confirm the progress bar advances normally with no error prompts.
- Enter a search term related to cement strength, and check that the number of returned search results falls within the range configured for
recall_top_k.
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.