What the Data for This Category Looks Like
Data sources include daily shipment ledgers from glass manufacturing enterprises, inventory financing application records from regional building material dealers, and transaction logs from third-party building material supply chain financial platforms. Updates run daily. Each daily report covers all business data from the previous calendar day. The core content is structured tables, with a small number of business remark fields. Fixed fields include glass category, thickness specification, unit price, inventory volume, financing application amount, applying institution, approval status, and update timestamp. Unit price is measured in yuan per square meter. Inventory volume and financing application amount use units of square meters or yuan, with statistical dimensions distinguished by field type.
What Constraints These Characteristics Impose on Vector Models and Indexing
Glass financing daily reports have a high proportion of structured fields, but also include unstructured remarks. Logical boundaries between metadata filtering and semantic vector recall must be clearly defined. The daily full update requirement means indexes must support efficient incremental or scheduled full rebuilds, to adapt to data volume growth as business scales. Glass products with different thickness specifications have clear category distinctions. Vector recall must perform pre-clustering filtering based on specification fields, to avoid incorrect cross-specification matches. Financing approval status is a dynamically updated field. The refresh frequency of vector indexes must match the timeliness requirements of business changes.
How to Configure the Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Doubao-embedding-large | Supports long-text semantic encoding, and can cover feature extraction for mixed fields such as glass specifications and financing amounts |
index_chunk_size | 800–1200 characters | The total field length of a single daily report record ranges from 600 to 1000 characters. This range avoids semantic fragmentation and reduced vector accuracy |
recall_top_k | Top 10–15 results | There are many daily report entries for glass of the same specification. Sufficient recall volume is required for secondary filtering, to avoid missing valid matches |
similarity_threshold | 0.75–0.85 | Glass products with different thickness specifications must be distinguished. This threshold filters low-match cross-specification entries |
index_refresh_interval | Every hour | Financing approval status is updated multiple times per day. This refresh frequency ensures the timeliness of index data |
vector_db_batch_size | 500 items per batch | Daily full data volume ranges from thousands to tens of thousands of items. Batch import avoids excessive database load |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by data form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Issue: In version V4.14.3, clicking to enable the
Doubao-embedding-largeindex model, filling in a custom request address and API key, then clicking test directly results in an error. Cause: The proxy forwarding permission for the corresponding model was not enabled in FastGPT's AIProxy configuration, causing requests to fail to reach the model server normally. - Issue: A large number of float glass entries with different thickness specifications appear in index recall results, and the number of recalled items far exceeds the preset range. Cause: The
similarity_thresholdparameter was not configured, or the threshold was set below 0.7, resulting in insufficient semantic matching accuracy. - Issue: Daily full index rebuild time exceeds the preset duration, and daily data updates cannot be completed. Cause: The
vector_db_batch_sizeparameter was not set, and full data was imported in a single batch, resulting in database connection timeouts.
How to Confirm Proper Configuration
- Navigate to the vector model configuration page in FastGPT, run a connectivity test for the
embedding_model, and confirm the returned HTTP status code is 200. - Upload a single glass financing daily report record, and check that the actual value of
index_chunk_sizein the index construction log matches the configured value. - Initiate a recall query for glass of a specific thickness specification, and verify that the field matching logic of the recall results conforms to preset rules.
- Wait for the configured
index_refresh_intervalduration, then check that the data update timestamp in the index library matches the actual business 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.