What the data for this category looks like
Financial report data for textile manufacturing is primarily sourced from annual, semi-annual, quarterly reports and temporary announcements publicly disclosed by exchanges. Update frequency is once annually, twice semi-annually, four times quarterly. Temporary announcements are updated in real time as business changes take place. Document structure includes two parts: standardized financial statements and segmented operational data. Standardized report fields include ending inventory balance, operating costs, and other items. Segmented operational data includes yarn output, fabric shipment volume, raw material consumption per unit product, and other items. Field units use industry-specific metrology standards such as meters, kilograms, ten thousand yuan, yarn count, and gram weight.
How these characteristics constrain vector models and indexes
The segmented operational fields of textile manufacturing financial reports contain a large number of industry-specific terms. This requires vector models to have semantic alignment capabilities for industrial sub-sectors. General embedding models may fail to accurately recognize professional terms such as yarn count and gram weight. Documents mix structured financial data and unstructured operational descriptions. This requires indexes to support hybrid queries of structured field retrieval and vector recall. Frequently updated financial report content requires indexes to support incremental refresh. This avoids resource consumption caused by full reconstruction. Long-text process and capacity descriptions need to retain contextual relevance. Segmentation rules must adapt to the semantic integrity of industry data.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Industry-fine-tuned textile domain embedding model, or text-embedding-v3 | Adapts to the semantic meaning of exclusive terms in textile manufacturing financial reports, improves vector recall accuracy |
chunk_size | 800–1200 characters | Balances contextual integrity of long sentence process descriptions and structured data in financial reports, avoids excessive truncation |
index_refresh_interval | 1 hour | Adapts to the high-frequency update rhythm of textile manufacturing financial reports (quarterly updates with temporary announcements), ensures index timeliness |
retrieval_top_k | Top 8–12 results | Covers retrieval needs for multi-dimensional segmented fields in financial reports, avoids missing key data due to too few recall results |
vector_db_batch_size | 32–64 entries | Adapts to 8c16G host resource configuration without GPU, avoids overload from single-batch processing |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time of large consolidated financial report files, avoids parsing timeout in environments without GPU |
The parameter values provided on this page are common recommended starting points 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 settings.
Three common errors
- Issue: Knowledge base index creation fails when deploying PG database via Docker. Logs show
vector dimension mismatch. Cause: The embedding dimension of textile financial reports is not matched to the vector database's dimension configuration. The dimension of general embedding models does not match the default dimension of the PG vector plugin. - Issue: Error occurs when configuring embedding model access. The interface displays
503 No available channel for model text-embedding-v3 under current group default. Cause: The index model is not configured separately. The same access channel as the chat model is used, and this channel does not have embedding permissions enabled. - Issue: Model call fails after setting the
CHAT_API_KEYenvironment variable when starting via docker-compose. Cause: Containers are not rebuilt to load new environment variables. Restarting only the container cannot update loaded configuration parameters.
How to confirm successful configuration
- Verify that the index dimension of the vector database matches the output dimension of the currently used
embedding_model. - Upload a single textile manufacturing quarterly financial report file, check whether the index creation status shows success.
- Initiate a retrieval request for textile capacity fields, verify that the recall results include corresponding segmented data.
- Check the environment variables inside the container, confirm that the API key of the index model matches the configuration items.
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.