What this category of data looks like
Financial report analysis data for the e-commerce service category comes primarily from e-commerce platform open operation APIs, backend logs of self-operated e-commerce service SaaS tools, and third-party industry statistical documents. Data update cycles are split into monthly store operation snapshots, quarterly enterprise operation reports, and annual industry comprehensive reports. Documents include structured transaction fields and unstructured operation summaries. Structured fields include GMV, customer unit price, order count, and repeat purchase user count, with units corresponding to ten thousand yuan, yuan, orders, and individual users respectively. Unstructured documents are typically 1000 to 5000 characters per operation review entry, covering details of specific marketing campaigns and supply chain adjustments.
What constraints these characteristics impose on knowledge base retrieval and recall
The dispersed, multi-source data feature requires the retrieval pipeline to support cross-data source index merging, to avoid fragmented recall results. Data with different update cycles needs a combined strategy of incremental and full updates, to prevent delayed recall content or wasted index resources. Fixed units and business meanings of structured fields require associating field metadata during retrieval, to avoid invalid recall caused by unit confusion. Unstructured documents have long lengths, so adaptive segmentation rules are needed to ensure business integrity of recalled segments.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
recall_count | Top 10-15 results | E-commerce financial report data has many closely related fields, requiring coverage of sufficient associated business information |
similarity_threshold | 0.72-0.85 | Balance precision and recall coverage, adapted to the high density of professional terminology in financial report data |
segment_length | 800-1200 characters | Adapt to the paragraph structure of unstructured financial report documents, avoiding business logic fragmentation in segments |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Financial report documents are mostly long texts, requiring sufficient time for parsing and indexing |
rerank_count | Top 5-8 results | Filter redundant recall results, focusing on core business-related segments |
TEXT_EMBEDDING_MODEL | Vectorization model adapted to e-commerce professional terminology | E-commerce financial reports contain a large number of industry-specific terms, requiring targeted models to improve retrieval accuracy |
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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: Retrieval results return content completely unrelated to the referenced knowledge base, and the answer does not hit financial report data in the knowledge base. Cause: No reasonable
similarity_thresholdis set, or the reranking filtering link is not enabled, and the reranking model is not used to filter low-correlation segments, resulting in low-correlation non-financial report segments being recalled and used as generation basis. - Phenomenon: Knowledge base retrieval takes too long, returning a
504 Gateway Timeoutstatus code. Cause: The parsing requirements for long-text financial report documents are not adapted, thePARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, or the incremental indexing strategy is not enabled, and full indexing of all financial report data leads to excessive resource occupation. - Phenomenon: After configuring the LLM to only use knowledge base content, the generated answer still contains external information. Cause: The prompt template does not bind the context variables returned by retrieval, or the configuration item that forces use of retrieval results for generation is not enabled, causing the LLM to call its own pre-trained knowledge to generate content.
How to confirm proper configuration
- Upload a typical e-commerce financial report document, check the parsed segmentation results, and confirm that the segments conform to the paragraph structure of the business document.
- Enter a precise financial report query, check the
recall_countandrerank_countvalues returned by retrieval, and confirm that the numbers match configured expectations. - Test the function that restricts the LLM to only use knowledge base content, and verify that all information in the generated answer comes from the uploaded knowledge base documents.
- Simulate high-concurrency retrieval requests, check the system response time, and confirm that parameters adapt to actual business scenarios.
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.