What data for this category consists of
White goods financial report data primarily comes from publicly disclosed periodic reports of listed companies on Shanghai, Shenzhen, and Hong Kong stock exchanges, as well as third-party public industry research data. Update cadence: annual reports are released once per year, quarterly reports once per quarter, and temporary announcements are issued promptly after major operating events occur. A single document typically includes consolidated financial statements, operating data for each product line, channel sales status explanations, and other content. Core fields include operating revenue, shipment volume, attributable net profit, and raw material procurement amount, with corresponding units being 100 million yuan, 10,000 units, 100 million yuan, and 100 million yuan respectively.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Publicly disclosed financial report data has fixed, batched update cycles. This requires the retrieval and recall link to support precise filtering by report type and disclosure time to avoid recalling outdated data. Financial reports include structured tables and unstructured text descriptions. When chunking, semantic integrity must be preserved to avoid splitting that breaks cross-row and cross-column associated operating data. Operating data for different product lines is scattered across different sections. Retrieval must match content from the corresponding modules, and cannot broadly recall irrelevant paragraphs. A single financial report document has a large word count. This requires adapting to the limits of long-text chunking and vector encoding to avoid losing key business indicators.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Adapts to the context window limits of mainstream vector models, while preserving the semantic integrity of tables and text in white goods financial reports, and avoiding splitting that breaks cross-row and cross-column operating data associations |
Recall count | Top 6 results | Core operating data for white goods financial reports is scattered across 6 to 8 sections. Too many recalled results will increase token consumption, while too few may miss key product line indicators |
Similarity threshold | 0.72–0.78 | Financial report data contains a large number of professional terms. A relatively high threshold must be maintained to avoid recalling irrelevant industry general content or generic materials |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | A single financial report document has a large word count, so sufficient time is required for structured extraction and chunking operations during parsing |
maxContext | 4000–6000 characters | Combines chunk length and number of recalled results to control the total context token volume, avoiding exceeding the upper limit of the generation model |
Keyword Filtering Rule | Configure exclusive terms such as "shipment volume, product line revenue, attributable net profit" | Filter non-financial report casual chat content, and compensate for the shortcomings of relying solely on empty recall |
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 the settings.
Three common configuration mistakes
- Phenomenon: The token consumption for a single retrieval exceeds expectations. Cause: The
Recall countandmaxContextconfigurations were not adjusted, and too many non-core financial report paragraphs were recalled, resulting in excessive token usage. - Phenomenon: After configuring
过滤空召回, a large amount of casual chat content unrelated to financial reports is still included in the AI response. Cause: No secondary filtering rules for exclusive terms of white goods financial reports were added. Relying solely on empty recall judgment cannot identify generalized casual chat. - Phenomenon: An error "text length exceeds limit" occurs during vector encoding. Cause: Long financial report documents were not split according to the
Chunk sizeconfiguration, and the entire report was submitted directly for encoding, exceeding the context window of the vector model.
How to confirm the configuration is correct
- Upload a test white goods financial report document, check the parsed chunk results, and confirm that the chunk length matches the range specified in the
Chunk sizeconfiguration. - Initiate a retrieval targeting specific financial report indicators, count the number of recalled results, and confirm that it matches the value set in the
Recall countconfiguration. - Initiate a generic casual chat query, confirm that no knowledge base content is recalled, or that the secondary filtering rules are effective.
- Upload a financial report document with more than 10,000 words, check that parsing is completed and no timeout error occurs, and confirm that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration is reasonable.
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.