What the data for this category looks like
Data for papermaking intelligent due diligence reports comes from four main sources: internal production reports of papermaking enterprises, raw and auxiliary material purchase ledgers, sewage discharge monitoring system data, publicly available industry association statistics, and customs import and export declarations.
Refresh cadences vary by data type:
- Production metrics refresh daily
- Procurement and compliance data refreshes weekly
- Industry macro data refreshes monthly
Most documents are structured tables and semi-structured analytical text. Fields include raw and auxiliary material unit consumption, capacity utilization rate, unit energy consumption, sewage discharge concentration, and more. Common units are tons, cubic meters, kilowatt-hours, and ten thousand yuan.
Constraints on vector models and indexing
The mixed structured and semi-structured format of papermaking due diligence reports has specific requirements. Vector models must support both structured field encoding and unstructured text encoding. Generic text models risk losing structured semantic information.
Data with multiple refresh cadences needs differentiated indexing strategies:
- Daily updated production metrics require incremental indexing
- Monthly updated macro data can use full refresh indexing
Fields with multiple units require indexes to bind field metadata. This prevents unit ambiguity during matching.
Long compliance analysis paragraphs need longer segment lengths. This avoids semantic truncation.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Papermaking due diligence reports contain long sections of compliance analysis text, to avoid truncation of core business semantics |
chunk_overlap | 100–150 characters | Retain contextual associations across segments for production metrics, ensuring semantic coherence |
recall_top_k | Top 8–12 results | Cover multi-dimensional metric fields in papermaking due diligence reports, avoiding missed key matching results |
similarity_threshold | 0.72–0.80 | Adapt to matching accuracy for structured fields and professional terminology, reducing the probability of irrelevant recall |
enable_incremental_index | Enabled | Adapt to the synchronization requirements of daily updated production data, reducing resource usage from full indexing |
vector_store_type | Zilliz Cloud | Support high-concurrency structured vector retrieval, adapting to data management with multiple update cadences |
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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: After configuring a vector index in the backend, the originally associated language model configuration items disappear, and large model calls fail. Cause: The configuration entry for the vector model and language model was mistakenly confused, overwriting the original language model binding settings.
- Phenomenon: When deploying version 4.9.6 locally, an
ECONNREFUSEDerror occurs after attempting to configure an external indexing model, with logs showing a rejected connection. Cause: The external port for the indexing model was not opened in local firewall rules, or the access key for the indexing model was not configured correctly. - Phenomenon: After migrating vector storage from PGSQL to Zilliz, retrieval results for some structured fields are empty. Cause: Field metadata binding was not synchronized during migration, causing the index to fail to recognize unit fields in papermaking reports and leading to semantic deviation during matching.
How to confirm proper configuration
- Upload a sample papermaking enterprise due diligence report, check if the length of segmented text blocks matches the preset
chunk_sizeconfiguration. - Initiate a retrieval test for professional metrics, verify that the number of recall results meets the
recall_top_kconfiguration requirements. - View the vector storage connection status panel, confirm that the currently used index type matches the configured
vector_store_type. - Trigger an incremental index synchronization, check if the updated index includes the latest business data.
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.