What This Category’s Data Looks Like
Data sources for computer equipment financing daily reports include internal enterprise procurement management systems, financial accounting systems, official price databases of equipment manufacturers, and industry bidding announcement platforms. Data is updated once daily. Each record corresponds to financing procurement information for a single batch or single computer equipment unit. Document fields include equipment model, specification parameters, purchase quantity, unit price, supplier name, financing credit line, approval status, update date, and more. Units include units, ten thousand yuan, batches, and other types. Each single record has medium text length, and contains both structured numerical information and professional computer equipment terminology text.
Constraints Imposed by These Characteristics on Vector Models and Indexing
First, the data contains multiple field types. There are professional industrial texts such as computer equipment models and supplier names, as well as structured numerical information such as unit price and credit line. This requires vector models to support embedding and retrieval of mixed fields, to avoid losing numerical information when only relying on text embedding. Second, the data is updated daily, with clear incremental update requirements. The indexing system must support incremental synchronization. Full reindexing will cause excessive daily synchronization time, which affects the timeliness of the daily report. Third, each single record contains professional terminology for computer equipment, such as CPU model, memory specifications, etc. The vector model must adapt to semantic understanding in the industrial equipment field, otherwise it cannot accurately match similar equipment financing entries. Fourth, the data contains a large number of duplicate equipment models and supplier information. The index must support deduplication and precise matching, to avoid redundant retrieval results interfering with financing decisions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | bge-large-zh-v1.5 | Adapts to professional terminology for computer equipment, can accurately embed professional texts such as CPU models and memory specifications, with better matching performance than general embedding models |
chunk_size | 800-1200 characters | A single record of computer equipment financing daily reports contains content such as models, parameters, and financing information. This range can fully wrap a single record and avoid truncating key information |
incremental_index_enabled | true | Daily report data is updated once daily. Incremental indexing avoids the time and resource consumption caused by full reindexing, and adapts to the daily synchronization rhythm |
recall_top_k | Top 10 entries | Financing decisions need to reference multi-dimensional candidate entries. 10 recall results can cover the main reference scope, and avoid missing key information |
similarity_threshold | 0.75-0.85 | Matching requirements for equipment models and supplier information are relatively high. This threshold can filter low-matching irrelevant entries and improve retrieval accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | When importing batch equipment financing daily reports, the default timeout duration is insufficient to handle multi-document parsing and embedding tasks. Extending to 600 seconds can avoid import failures |
The parameter values provided on this page are all conventional recommendations, used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing the configuration.
Three Common Misconfigurations
- Phenomenon: Custom index configuration fails to associate with target data in MongoDB, and the query returns empty results. Cause: The associated MongoDB collection name and corresponding fields are not specified in the index configuration, so the index cannot read the correct data source.
- Phenomenon: After importing batch equipment financing daily reports, the generated dataset.csv only contains the
indexmetadata field, with no original content fields. Cause: Theexport_original_contentconfiguration item is not enabled, so only index metadata is exported, and complete equipment information text is not exported. - Phenomenon: When deploying with two 3090 GPUs, the indexing model only uses one GPU, and the second GPU resource is not utilized. Cause: The
embedding_deviceparameter is not configured to specify the GPU ID, so by default only the computing resources of the first GPU are loaded.
How to Confirm Proper Configuration
- Perform a knowledge base export operation, check whether the generated csv file contains the
contentfield and original information such as equipment model and financing quota. - View the associated data source configuration in the index management interface, confirm that the correct MongoDB collection and corresponding fields have been specified.
- Run a test query, enter the equipment model keyword, check whether the matching degree and number of returned results meet the preset recall threshold.
- View the system monitoring panel, confirm that GPU resources are being called normally, and the computing load distribution of embedding tasks meets the configuration requirements.
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.