What the data for this category looks like
Financial report data for the paint and ink industry originates from public disclosure documents of domestic and overseas stock exchanges, monthly survey data from industry associations, and official annual reports of enterprises. The minimum update cycle is quarterly, with annual reports forming a full update cycle. Each document includes structured fields and unstructured text. Structured fields cover reporting period, revenue scale, unit cost, production capacity, raw material purchase volumes such as titanium dioxide and resin, and similar metrics. Unstructured text includes industry trend analysis, cost change explanations, and other relevant content. Most field units use industrial statistical units like tons and ten thousand yuan.
Constraints on vector models and indexing
The multi-field structured nature of financial report data, especially detailed indicators such as raw material purchase volumes and production capacity, requires vector models to adapt to vectorization of professional chemical industry terminology. This prevents confusion of vector features across different subjects. The quarterly data update rhythm requires indexes to support incremental construction and updates, avoiding time delays caused by full index reconstruction. Document structures with numerous long text paragraphs require segment configuration to balance information integrity and vector retrieval accuracy. Oversized segments increase vector dimension processing pressure. Undersized segments break the contextual connection of professional terminology.
Configuration recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Embedding-3 | Adapts to professional chemical terminology in the paint and ink industry, supports long text vectorization processing |
chunk_size | 800–1200 characters | Financial report text contains long paragraphs of professional descriptions, avoiding truncation of critical cost and production capacity information |
top_k | Top 8–12 results | Financial report-related retrieval must cover multi-dimensional data including raw materials, revenue, production capacity, and other metrics. Too few results will miss critical retrieval outcomes |
similarity_threshold | 0.72–0.78 | Distinguishes similar cost subjects and industry general terminology in financial reports, reducing false matching probability |
index_type | HNSW | Supports incremental index updates, adapting to the quarterly update rhythm of financial reports |
cache_ttl | 3600 seconds | Duplicate requests for the same financial report query are common, caching reduces repeated calls to the vector database |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on relevant samples before finalizing settings.
Three common configuration mistakes
- Symptom: Calling the knowledge base interface returns
400 Bad Requestwith the prompttraining_order_id invalid. Cause: Use cases for "add data to collection" and "create training order" are confused. Batch import of financial report documents requires first creating a training order before performing data import. - Symptom: Image-related fields in retrieval results are empty, making financial report accompanying images unable to be displayed. Cause: Image vectorization configuration is not enabled, and feature vectors of images are not extracted synchronously during index construction.
- Symptom: Response latency for identical financial report queries shows no significant change, with no cache optimization effect. Cause: The
cache_ttlparameter is not configured, and the duplicate request caching mechanism is not enabled.
How to confirm successful configuration
- Call the embedding model test interface, input a segment of professional text from paint and ink financial reports, and verify that the returned vector dimension matches the standard dimension of the selected model.
- Perform a batch import operation for a single financial report document, check if the status in the task management interface shows "Completed", confirming that the incremental index update was successful.
- Initiate two identical financial report queries, compare the response latency of the first and second attempts, confirming that the caching mechanism is active.
- Retrieve financial report documents that contain embedded images, check if the returned results include image-related fields and preview links.
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.