What the Data for This Category Looks Like
Home goods financial report data primarily comes from periodic reports of domestic light manufacturing listed companies, publicly available industry research documents, and supply chain disclosure information. Updates follow fixed cycles of annual, semi-annual, and quarterly reports, covering full-year operational data. Most documents use multi-column PDF formatting. They include core fields such as segmented category revenue, gross margin, inventory turnover days, number of offline stores, and online channel share. Standardized measurement units include 100 million yuan, percentage values, days, and store counts. Data for some segmented categories appears in separate chapters.
Constraints for Knowledge Base Retrieval and Recall
The multi-column PDF layout of home goods financial reports can cause text block order misalignment during extraction, increasing parsing costs before retrieval. Fixed update cycles require the knowledge base to synchronize on a quarterly or annual basis to avoid data lag. The separate chapter structure for segmented categories requires retrieval to accurately match corresponding fields, preventing irrelevant general home goods data from being recalled. The specialized terminology system across multiple fields demands higher matching precision to filter out irrelevant results. Larger page counts per financial report document extend parsing and embedding time, creating requirements for retrieval response speed.
Configuration Settings
| Configuration Option | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Single home goods financial report PDFs typically do not exceed 500 MB. Setting a reasonable upper limit prevents large file upload failures |
PARSE_SEGMENT_LENGTH | 800–1200 characters | Financial reports contain many long paragraphs. This segment length balances context completeness and retrieval precision |
RECALL_TOP_N | Top 8–12 results | Financial reports include multiple segmented category fields. A sufficient recall volume is needed to cover core business data |
SIMILARITY_THRESHOLD | 0.75–0.85 | Financial report terminology is highly specialized. A higher threshold filters out irrelevant retrieval results |
EMBEDDING_MODEL | bge-large-zh-v1.5 | Adapts to Chinese specialized terminology in home goods financial reports, improving embedding precision |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing multi-page financial reports takes longer. This setting prevents premature timeout interruptions |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, 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
- Some PDF files show parsing failure after upload, and no corresponding content appears in knowledge base retrieval. The cause is that most home goods financial reports use multi-column layouts. The parsing engine cannot correctly extract text block order, leading to field misalignment that cannot be matched during retrieval.
- An error "No available text embedding model" occurs when calling the knowledge base. The cause is that
text-embedding-ada-002was added to the third-party interface platform, but the corresponding model was not specified in the FastGPT knowledge base configuration, or the configuration parameter format is incorrect. - The Feishu bot returns a complete single block of text without streaming segmentation. The cause is that the streaming output switch for knowledge base retrieval was not enabled, or the streaming callback logic for bot interaction was not configured correctly.
How to Verify Proper Configuration
- Upload a single home goods financial report PDF with more than 100 pages. Check that the parsing status shows success, and that no text extraction failure errors appear in system logs.
- Test retrieval for "2023 home storage category revenue". Verify that the recalled results include content from the corresponding financial report chapter, and adjust matching precision to meet business expectations.
- View the knowledge base configuration page. Confirm that the
EMBEDDING_MODELparameter matches the deployed model, with no missing or incorrect configurations. - Trigger knowledge base retrieval. Check that the bot interaction returns streaming segmented content, with no single complete output.
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.