Data Characteristics of This Category
Data sources for due diligence reports in the paint and ink industry include internal quality inspection archives of manufacturing enterprises, industry weekly reports released by the China Coatings Industry Association, material safety data sheets from raw material suppliers, and application test reports from downstream customers.
Update cadence follows this pattern: raw material metrics are updated weekly, industry standard documents are updated quarterly, and internal enterprise quality inspection reports are generated with each production batch.
Most documents are PDF files with fixed headers or structured tables, containing fields such as raw material name, batch number, test item, measured value, and test date. Units include g/L (VOC content), μm (fineness), and seconds (drying time).
Constraints Imposed on Vector Models and Indexing
Multiple independent data blocks in structured table documents require splitting vector segments by test item to avoid cross-item semantic confusion.
Frequently updated raw material data requires indexes to support incremental synchronization, reducing resource consumption from full index rebuilding.
The presence of multiple unit fields requires uniform formatting before vector modeling, preventing vector representation deviations caused by unit differences.
Long-text application test reports require adapting longer segment thresholds to ensure complete application scenario details are retained.
Processing batch multi-batch data also requires vector models to support reasonable batch call parameters, reducing latency for single calls.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to performance parameters and test data in long paragraphs of paint and ink documents, avoids semantic fragmentation from overly long segments or loss of test item association from overly short segments |
embedding_batch_size | 32–64 | Adapts to batch processing of raw material batch data, balances call latency and resource usage |
index_refresh_interval | 3600 seconds | Matches the weekly update cadence of raw material data, balances real-time performance and system resource consumption |
similarity_threshold | Calibrated via actual testing | Adapts to semantic matching requirements for multi-unit fields, filters irrelevant matches caused by unit differences |
parse_table_mode | Split by row | For structured table documents, ensures each test item is used as an independent vector segment, avoids splicing cross-item content |
incremental_index_enable | Enabled | Supports incremental synchronization of production batch data, no need for full index rebuilding |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Common Mistakes
- Phenomenon: Models such as Zhipu Embedding-3 and CharGLM-4 cannot be selected in the interface, with an error prompt "Model unavailable". Cause: The interface address and call token of the corresponding model have not been added in the vector model service configuration, or the service has not opened call permissions for the corresponding model.
- Phenomenon: Vector model call errors occur after local deployment, returning status code 500 or "connection timed out". Cause: The oneAPI proxy address has not been configured correctly, or the docker-compose network has not bound the corresponding port, causing FastGPT to fail to connect to the vector model service.
- Phenomenon: Imported due diligence report index segments are chaotic, with cross-test item content splicing. Cause: The
parse_table_modesplit-by-row configuration has not been enabled, causing table content to be treated as a single vector segment as a whole, losing test item association logic.
How to Verify Proper Configuration
- Upload a single paint and ink quality inspection report, view the parsed segment list, confirm that table content is split by test item with no cross-item splicing.
- Enter the vector model management page, view the connected model list, confirm that the target model has been added and shows a normal running status.
- Initiate an incremental index synchronization task, view the task log, confirm that there is no prompt triggering full index rebuilding, and the incremental synchronization process completes normally.
- Input a single test parameter to initiate a similarity recall test, confirm that the recall results match the corresponding test item, with no irrelevant field interference.
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.