What the Data for This Category Looks Like
Data sources for infrastructure construction marketing content include winning bid announcements and bidding documents from public bidding platforms, project brochures and proposals from internal marketing material libraries, and construction logs and acceptance reports from project execution archives. Data updates trigger based on project progress. Updates occur when new winning bid projects are announced or new marketing materials are released. Documents include structured metadata and unstructured text content. Structured fields typically include project ID, project location, investment amount, construction period, and project type. Common units include ten thousand yuan, hundred million yuan, days, and months. Unstructured text sections mostly cover technical proposals and cooperation detail descriptions.
What Constraints These Characteristics Impose on Vector Models and Indexing
Mixed multi-source data requires indexes to handle structured metadata and unstructured text separately. Directly converting structured fields into vectors causes semantic bias, so this separation is necessary. Non-fixed update cycles require indexes to support incremental updates instead of full reconstruction. This adapts to the random update nature of project progress. Wide variation in document lengths requires adaptive segment configuration. This prevents over-splitting short documents or losing context in long documents. Specific fields and units require indexes to support metadata filtering. Filtering by conditions like project type or region narrows search scope and improves retrieval accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Doubao-embedding-v2 or text-embedding-ada-002 | Supports semantic encoding for infrastructure industry terminology and long text input |
chunk_size | 800–1200 characters | Balances semantic completeness and retrieval granularity for long engineering documents |
chunk_overlap | 100–150 characters | Preserves contextual association between segments, prevents semantic breaks in split long documents |
index_type | HNSW | Meets batch retrieval needs for multi-project data and improves recall speed |
metadata_filter_enable | Enabled | Supports filtering search results by metadata such as project type and region |
incremental_update_enable | Enabled | Adapts to non-fixed cycle updates of infrastructure project data, reduces resource consumption from index reconstruction |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific scenarios require individual analysis. Testing on local samples is recommended before finalizing configuration values.
Three Common Misconfigurations
- Phenomenon: Importing an entire database table or specified columns directly as a knowledge base index, with retrieval results that do not match business requirements. Cause: Failed to convert core semantic text of marketing content into vectors, mistakenly used structured fields themselves as index content, and did not configure metadata association rules.
- Phenomenon: Index shows not ready after creation, retrieval returns empty results or
500 Internal Server Error. Cause: Launched retrieval before index construction completed, or encountered vector dimension mismatch or sharding failure during index construction. - Phenomenon: Index construction fails when using the vector model corresponding to
deepseek, with a prompt about model dimension incompatibility. Cause: Did not set thevector_dimensionparameter of the index to match the output dimension of the selected vector model, and did not correctly configure model API access parameters.
How to Verify Proper Configuration
- Review vector model loading logs to confirm the output dimension of the selected model matches the
vector_dimensionparameter configured for the index. - Upload a single infrastructure construction marketing document to trigger index construction. Wait for the interface to show the index is ready, then launch a retrieval test to verify relevant content is returned.
- Configure metadata filtering rules, launch a conditional retrieval, and verify only results matching the metadata are returned.
- Upload new marketing materials to test the incremental update function, confirm the index automatically updates and the new content can be retrieved.
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.