What the data for this category looks like
Data for publishing intelligent due diligence reports comes primarily from publishing institution topic selection approval archives, copyright ownership documents, past publishing contracts, market research datasets, and compliance review drafts. Updates are triggered by project milestones: updates run when new topics are approved, copyright status changes, or annual compliance audits are conducted. Individual documents are mostly structured long texts, with fixed fields including report ID, publishing entity, ISBN code, copyright term, and author qualification information. The main body contains long sections such as market analysis, compliance checks, and risk warnings. The character count per document varies widely.
What constraints these characteristics impose on vector models and indexes
The structured long-text characteristics of this category require vector models to support long context input. This avoids two issues: splitting text too finely which breaks semantic coherence, or splitting too coarsely which reduces local retrieval accuracy. The on-demand update rhythm requires the index system to support incremental synchronization and version overwriting. This prevents resource waste from full reindexing. The presence of multiple fixed metadata fields requires the index to support precise filtering by metadata dimensions. This allows narrowing recall scope using fields like report ID or ISBN code. The wide variation in per-document character count requires a dynamically adjustable chunking strategy. This accommodates different splitting needs for short abstracts and long analysis sections.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
EMBEDDING_MODEL | text-embedding-3-large | Meets the long-text input requirements of publishing due diligence reports. Supports a maximum input length of 8192 tokens, covering the chunk encoding needs of most individual reports. |
CHUNK_SIZE | 1200–1500 characters | Balances semantic completeness of long texts and retrieval accuracy. Accommodates the splitting needs of long analysis sections in publishing due diligence reports. Prevents encoding overflow from overly long chunks, or lost contextual association from overly short chunks. |
INDEX_INCREMENTAL_SYNC | Enabled | Adapts to the on-demand update project rhythm. Only synchronizes modified report data, reducing resource usage from full indexing. |
RECALL_TOP_K | Top 8–12 results | Accommodates retrieval scenarios with multi-field metadata filtering. Ensures recall results cover relevant report content across different dimensions. |
METADATA_FILTER_ENABLE | Enabled | Supports filtering recall results using fixed fields such as report ID or ISBN code, to accurately locate target reports. |
PARSE_FILE_TIMEOUT | 300 seconds | Adapts to the parsing duration of long-text due diligence reports. Prevents parsing timeout failures caused by overly long documents. |
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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: Knowledge base retrieval response time exceeds business tolerance limits. Logs show significantly increased latency during vector recall. Cause: No reasonable chunk length set to accommodate long-text characteristics. Excessively large per-chunk character count triggers forced truncation during vector encoding or adds extra computation overhead.
- Phenomenon: Index tasks remain stuck in "indexing" status and cannot enter ready state. Restarting the service does not resolve the issue. Cause: Used an embedding model with insufficient input length. Long text chunks are forcibly truncated and cannot complete encoding, causing index tasks to hang.
- Phenomenon: Multiple duplicate index entries appear in the same dataset. Data volume grows abnormally over time. Cause: Did not enable incremental synchronization and version overwriting. Each report update does not overwrite existing index entries, leading to duplicate index data generation.
How to confirm proper configuration
- Check the embedding model configuration item. Confirm the selected model supports the maximum input length of your business documents. Test encoding success by uploading the longest individual due diligence report.
- Run an incremental synchronization task. Verify that the number of updated report index entries matches the actual number of modified documents, to confirm the version overwriting mechanism works.
- Configure metadata filtering rules. Retrieve using a report ID or ISBN code. Confirm only index entries matching the target fields are recalled, to verify the filtering function works correctly.
- Adjust the chunk length parameter. Upload a test document and check chunk splits. Confirm the character count of split chunks falls within the preset range, to verify the chunking strategy works.
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.