What the Data for This Category Looks Like
Data for this category typically comes from national public resource trading platforms, natural resource trading systems, property right exchange systems, and state-owned assets supervision systems. The update frequency is daily. Daily documents include transfer announcements, trading notices, bid application materials, margin vouchers, transaction confirmation letters, listing result announcements, property right transfer specifications, and more. Most data fields relate to trading target ownership, restriction conditions, margin rules, transaction processes, and similar details. Some documents contain both structured target parameters and unstructured rule descriptions.
What Constraints Do These Characteristics Impose on the Vector Models and Indexes Link
Daily updated data requires indexes to support high-frequency incremental refreshes, avoiding performance losses from full reconstruction. Documents mixing structured parameters and unstructured rules require vector models to adapt to encoding consistency for both short fields and long texts. Differences in document formats across multiple sources require the indexing process to include unified text cleaning logic, ensuring content exported from different systems has a consistent format before vectorization. The length of a single vectorized segment must not exceed the maxToken limit to avoid encoding failures. Content related to ownership is the core query scenario, so indexes must prioritize retaining the vector weight of such segments, preventing non-core content from interfering with retrieval results.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the common paragraph length of documents in this category, balancing vector encoding accuracy and retrieval efficiency |
| Overlap Length | 100–200 characters | Prevents key ownership information from being truncated during chunking, ensuring complete context during retrieval |
| Similarity Threshold | 0.72–0.85, calibrated via actual testing | Distinguishes core ownership content from irrelevant text, adapting to the accuracy requirements of party building learning Q&A |
| Single Search Limit | Top 8 results | Covers common query scenarios for party building learning Q&A, avoiding excessive redundant results that interfere with outcomes |
maxToken | 4096 token | Adapts to the maximum input length of mainstream vector models such as BGE-large, ensuring long document segments can be fully encoded |
defaultToken | 2048 token | Balances the information density of a single vector and retrieval performance, aligning with the average content length of data in this category |
Three Common Misconfigurations
- Phenomenon: After importing documents, the exported knowledge base file only contains the
indexfield and nocontentfield. Cause: The content storage configuration after text chunking was not enabled correctly, resulting in only vector index metadata being retained and original text segments being lost. - Phenomenon: Timeout errors occur when calling vector models in an intranet deployment environment. Cause: The intranet-accessible vector model service address was not configured, or request parameters for the intranet environment were not adapted.
- Phenomenon: When default chunking parameters are used, long transaction announcements are over-chunked, causing ownership-related information to be split across multiple segments. Cause: The default chunking parameters do not adapt to the content structure of long documents in this category, and reasonable chunk length and overlap ratios were not set.
How to Confirm Configurations Are Correctly Set
- Manually import a standard property right transfer specification, and check if both the original text segment and corresponding vector index information are displayed on the knowledge base details page.
- Launch a party building learning Q&A test query that includes ownership-related terms, and verify that the returned results contain core ownership content of the trading target.
- Check the logs of the vector model service to confirm that daily incremental update tasks can be triggered normally and index synchronization is completed.
- Export the test dataset of the knowledge base, and verify that the
contentfield is fully present with no missing entries.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.