What the data for this category looks like
Financial report data for the e-commerce service category targeting the financial sector mainly comes from internal enterprise ERP systems, API interfaces of partner e-commerce platforms, third-party industry benchmark datasets, and audited official financial report documents. Data update rhythms include fixed-period operational reports and ad-hoc business adjustment announcements. Document formats include structured revenue detail tables, unstructured business analysis paragraphs, PDF and Excel files with embedded charts, and time-series transaction data files. Core fields include transaction serial number, transaction occurrence time, transaction settlement amount, fulfillment duration, and active service customer count. Corresponding units are none, year-month-day hour-minute, yuan, hour, and person-times.
Constraints imposed by these characteristics on vector models and indexing
The multi-source mixed data features of e-commerce service financial reports require vector models and indexing to support mixed encoding and retrieval of structured tables, time-series transaction data, and unstructured text. The fixed-period and ad-hoc update rhythm requires configuring incremental index trigger rules to adapt to rapid synchronization of sudden business announcements. The multi-field detailed data requires establishing field-level index mappings to avoid including irrelevant fields in vector encoding. Additionally, the document length span of e-commerce service financial reports varies widely, from short announcements to complete financial report documents. This requires configuring an adaptive segmentation strategy to prevent long text vectors from losing core information.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | bge-large-zh-v1.5 or domestic open-source vector models | Adapts to Chinese professional terminology in e-commerce financial reports, supports vector encoding of multiple document types, and is compatible with domestic computing environments |
chunk_size | 800–1200 characters | Matches the average length of table paragraphs and business analysis text in e-commerce financial reports, avoiding semantic fragmentation or information loss |
index_type | hierarchical_navigable_small_world_graph | Adapts to the mixed data structure of e-commerce financial reports, balances retrieval speed and recall accuracy, and supports multi-field mixed indexing |
retrieval_limit | Top 6–8 results | Balances context window capacity and core data recall volume, adapting to the retrieval needs of multiple detailed entries in e-commerce financial reports |
vector_db_sync_mode | incremental_on_update | Adapts to the update rhythm of fixed-period updates and ad-hoc announcements for e-commerce financial reports, reducing repeated vector computing resource consumption |
field_mapping_config | Map by business field groups | Matches the structure of multiple types of business fields in e-commerce financial reports, improving the precision of vector retrieval matching |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- The phenomenon is that FastGPT returns a
403 Forbiddenerror when calling a vector model deployed via Ollama, but the model interface can be called normally via curl commands to obtain vector results. The cause is that FastGPT's vector model call configuration did not correctly add the access whitelist for the Ollama service, or the authentication information in the request header does not match the Ollama configuration. - The phenomenon is that the relevance ranking of vector retrieval results does not conform to the business logic of financial report analysis, and cannot accurately hit core business indicators. The cause is that no dedicated index calculation rules were configured for the time-series transaction data of e-commerce financial reports, leading to unreasonable index weight allocation.
- The phenomenon is that after directly performing add, delete, or modify operations on vector database documents via MongoDB, the retrieval results are not updated synchronously. The cause is that the index metadata of the vector database is not bound to the MongoDB document operations. Directly modifying the underlying storage will not trigger incremental updates or reconstruction of the vector index.
How to verify the configuration is properly applied
- Execute the vector model test interface, input a typical text fragment of an e-commerce financial report, and verify that the dimension of the returned vector result matches the configured
embedding_modelparameter. - Upload a test e-commerce financial report document, check the vector database synchronization log, and confirm that the synchronization trigger timing aligns with the
vector_db_sync_modeconfiguration. - Initiate a financial report data retrieval request, verify that the returned result fields match the business fields configured in
field_mapping_config. - Adjust the
retrieval_limitparameter, confirm that the number of retrieval results changes according to the configuration, and verify that the parameter takes effect.
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.