What data for this category looks like
Data comes primarily from industry financing monitoring platforms, publicly disclosed corporate financing announcements, and local financial supervision public information. Updates run daily, covering small home appliance sector financing updates from the current day and the past 7 days. Most documents are structured tables with these fields: financing entity name, financing round, financing amount (unit: ten thousand yuan / hundred million yuan), investor entity, disclosure date, and segmented track label. Some supplementary documents include details of the financing party’s business layout, presented as paragraphs.
What constraints these characteristics impose on knowledge base retrieval and recall
The high share of structured tables requires the retrieval link to support field-level precise matching. Dedicated filtering rules must be configured for fields such as financing round and segmented track. The daily update cadence demands an incremental indexing strategy to avoid performance loss caused by full index reconstruction. Financing amounts use two units: ten thousand yuan and hundred million yuan. Unit unified conversion must be completed during the document parsing stage to prevent recall deviations caused by unit mismatches during retrieval. Supplementary documents with widely varying paragraph lengths need adaptive variable-length segmentation rules, to avoid loss of key semantic information in long paragraphs.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | Enabled | Structured tables are the core data carrier. Enabling this allows extraction of cell fields for precise retrieval |
PARSE_CHUNK_SIZE | 800–1200 characters | Covers most paragraph lengths of supplementary documents, avoids semantic fragmentation caused by overly long single chunks, and adapts to cell content extraction from structured tables |
RECALL_TOP_N | Top 8–12 results | Effective information for small home appliance financing daily reports is relatively scattered. Multi-round recall can cover updates from different financing entities and avoid missing key information |
SIMILARITY_THRESHOLD | 0.72–0.80 | Distinguishes precise matches for financing entities, rounds, etc., from semantically similar non-target content, and avoids incorrect recall of unrelated industry financing updates |
UPLOAD_INCREMENTAL_ENABLE | Enabled | Adapts to daily updated financing daily report data, reduces time consumption of full indexing, and improves update efficiency |
EMBEDDING_MODEL | text-embedding-ada-002 | Supports semantic encoding of structured fields, is compatible with the encoding formats of most public financing data, and reduces parsing and adaptation costs |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: Parsing fails for some PDF-format small home appliance financing daily report documents. Backend logs return the
PARSE_FAILEDstatus code. Cause: The document contains encrypted embedded fonts or nested table structures, and FastGPT’s structured parsing adaptation rules are not triggered. - Phenomenon: Vector database encoding incompatibility errors appear during knowledge base queries. The interface prompts “No matching vector data available”. Cause: Different embedding models are used during document upload and query stages, leading to inconsistent vector formats that cannot be matched.
- Phenomenon: Timeouts occur during high-concurrency queries, and response speed slows noticeably. Cause: Different embedding models are used for document upload and query, requiring extra format conversion during vector retrieval, which increases processing time.
How to verify correct configuration
- Upload a standard PDF-format small home appliance financing daily report document. Check the parsing task status to confirm no
PARSE_FAILEDerrors appear. - Initiate a query containing “angel round financing” and “personal care small home appliance track”. Verify the field matching degree of the recall results, then adjust
SIMILARITY_THRESHOLDto fit business requirements. - Upload a newly added financing daily report document. Wait for index completion, then run the same query. Confirm the newly added data appears in the recall results.
- View the vector database configuration page. Confirm the embedding model names used during upload and query stages are fully identical.
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.